@article {pmid41671386,
year = {2026},
author = {Li, M and Chen, Y and Liu, A and Wu, Q and Huang, C and Song, D and Hu, F and Lan, J and Huang, C and Hu, J and Wang, G},
title = {SAICAR Drives T Regulatory Cell Differentiation and FOXP3 Maintenance to Promote Immunotherapy Resistance.},
journal = {Cancer research},
volume = {86},
number = {9},
pages = {2218-2236},
doi = {10.1158/0008-5472.CAN-25-4373},
pmid = {41671386},
issn = {1538-7445},
support = {82425041//National Natural Science Foundation of China (NSFC)/ ; 82330084//National Natural Science Foundation of China (NSFC)/ ; 82403349//National Natural Science Foundation of China (NSFC)/ ; 82504215//National Natural Science Foundation of China (NSFC)/ ; 82503378//National Natural Science Foundation of China (NSFC)/ ; 2022YFA1105303//National Key Research and Development Program of China (NKPs)/ ; 2023yfc3402100//National Key Research and Development Program of China (NKPs)/ ; SCZ202409//Major Technology Innovation of Hubei Province/ ; 2021CFA006//Natural Science Foundation of Hubei Province ()/ ; 2024AFB048//Natural Science Foundation of Hubei Province ()/ ; 2024AFB079//Natural Science Foundation of Hubei Province ()/ ; 2023BR036//Huazhong University of Science and Technology (HUST)/ ; },
mesh = {Animals ; *T-Lymphocytes, Regulatory/immunology/metabolism/drug effects ; Mice ; Humans ; *Forkhead Transcription Factors/metabolism/genetics ; Immunotherapy/methods ; Cell Differentiation/immunology/drug effects ; Tumor Microenvironment/immunology ; *Drug Resistance, Neoplasm/immunology ; Mice, Inbred C57BL ; Cell Line, Tumor ; Female ; },
abstract = {UNLABELLED: Regulatory T (Treg) cells within the tumor microenvironment critically undermine the efficacy of PD-1 immune checkpoint blockade. Metabolic reprogramming has emerged as a critical determinant of antitumor immunity, highlighting the need to define the metabolic cues that program Treg differentiation in cancer. In this study, we identified the purine biosynthesis intermediate succinylaminoimidazole carboxamide ribose-5'-phosphate (SAICAR) as a key metabolic driver of Treg induction and resistance to anti-PD-1 immunotherapy. Mechanistically, SAICAR directly bound to the serine/threonine phosphatase PPM1A, inhibiting SMAD3 dephosphorylation and thereby sustaining TGFβ-SMAD3 signaling. Persistent SMAD3 activation enhanced FOXP3 transcription and stabilized the Treg lineage. In both human tumors and mouse models, elevated intratumoral SAICAR levels were associated with increased Treg accumulation, suppression of effector T-cell function, and failure of PD-1 blockade. Genetic or pharmacologic reduction of SAICAR restored antitumor immunity and sensitized tumors to PD-1 therapy. Notably, low-dose 6-mercaptopurine disrupted SAICAR-driven immunosuppression and synergized with anti-PD-1 treatment without inducing systemic immune toxicity. Together, these findings establish SAICAR as an immunometabolic regulator that links purine metabolism to immune evasion and highlight a therapeutically actionable pathway to overcome metabolite-driven resistance to immune checkpoint blockade.

SIGNIFICANCE: SAICAR is necessary and sufficient to drive Treg-mediated immunosuppression in the tumor microenvironment, linking tumor metabolism and immunosuppression and providing mechanistic insights for metabolism-guided combination immunotherapy.},
}

Animals
*T-Lymphocytes, Regulatory/immunology/metabolism/drug effects
Mice
Humans
*Forkhead Transcription Factors/metabolism/genetics
Immunotherapy/methods
Cell Differentiation/immunology/drug effects
Tumor Microenvironment/immunology
*Drug Resistance, Neoplasm/immunology
Mice, Inbred C57BL
Cell Line, Tumor
Female

@article {pmid42072234,
year = {2026},
author = {Ou, HY and Hasegawa, T and Fukayama, O and Miyashita, E},
title = {Neuroscience-Inspired Deep Learning Brain-Machine Interface Decoder.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {13},
number = {4},
pages = {},
doi = {10.3390/bioengineering13040440},
pmid = {42072234},
issn = {2306-5354},
support = {JP23ym0126812//Japan Agency for Medical Research and Development/ ; JP24ym0126812//Japan Agency for Medical Research and Development/ ; },
abstract = {Brain-machine interfaces (BMIs) aim to decode motor intentions from neural activity to enable direct control of external devices. However, most existing decoders rely on monolithic architectures that fail to capture the distinct neural representations of different joint movement directions, limiting their generalizability. In this work, we propose a Single-Direction CNN-LSTM decoder inspired by motor cortex encoding mechanisms, which separately models extension and flexion dynamics through parallel CNN-LSTM branches. Each branch extracts spatial-temporal features from neural spike data and predicts directional joint variables, which are then combined by subtraction to yield the net angular velocity and torque of upper-limb joints. Using invasive recordings from a macaque during a 2D center-out reaching task, we demonstrate that our decoder achieves comparable performance to a conventional CNN-LSTM when trained on all tasks, while significantly outperforming both CNN-LSTM and linear regression baselines in cross-target generalization scenarios. Moreover, the model can capture physiologically meaningful co-contraction patterns, providing richer insights into motor control. These results suggest that incorporating neuroscience-inspired modular decoding into deep neural architectures enhances robustness and adaptability across tasks, offering a promising pathway for BMI applications in prosthetics and rehabilitation.},
}

@article {pmid42074167,
year = {2026},
author = {Melnikova, AA and Egorchev, AA and Rosin, AA and Nurullin, LF and Lipachev, NS and Vedischeva, DS and Derzhavin, DV and Perepechenov, SS and Sukhodolova, EA and Shabernev, GV and Titova, AA and Kiyamova, RG and Kiyasov, AP and Chickrin, DE and Aganov, AV and Samigullin, DV and Popova, IY and Paveliev, M},
title = {Foreign Body Response to Neuroimplantation: Machine Learning-Assisted Quantitative Analysis of Astrogliosis.},
journal = {International journal of molecular sciences},
volume = {27},
number = {8},
pages = {},
doi = {10.3390/ijms27083524},
pmid = {42074167},
issn = {1422-0067},
support = {24-75-00123//Russian Science Foundation/ ; },
mesh = {*Machine Learning ; *Astrocytes/metabolism/pathology ; *Gliosis/pathology/etiology/metabolism ; Animals ; Glial Fibrillary Acidic Protein/metabolism ; *Foreign-Body Reaction/pathology/etiology/metabolism ; Male ; Rats ; },
abstract = {Neuroimplants represent an emerging medical technology, offering new therapeutic approaches for severe neurological and psychiatric disorders. One of the key limitations to long-term neuroimplant performance is the foreign body response elicited by intracortical implantation. Among the contributing cell types, astrocytes play a central role in glial scar formation around the implant, which can compromise device functionality. Immunofluorescence of glial fibrillary acidic protein (GFAP) provides a well-established marker of astrogliosis (neuroinflammation), yet quantitative and reproducible assessment of astrocyte morphology remains challenging due to the complexity and variability of image analysis approaches. Here, we aimed to quantitatively assess implantation-induced astrogliosis and to determine how classifier training strategy influences segmentation outcomes and morphometric measurements. We present a machine learning-assisted pipeline based on the LabKit plugin in Fiji for segmentation and morphometric analysis of GFAP-positive astrocytes in peri-implant scar versus distant cortical regions. Using this approach, we demonstrate an increase in GFAP expression, cell area, and astrocytic process length as well as the redistribution of GFAP signal along astrocytic processes within scar regions. We show that different classifier training strategies produce systematically distinct segmentation outcomes, with rule-compliant annotation improving agreement with manually defined ground truth. These findings highlight the critical role of annotation strategy in shallow learning-based segmentation and provide a practical framework for improving reproducibility of astrocyte morphometry in studies of neuroinflammation and neuroimplant biocompatibility.},
}

*Machine Learning
*Astrocytes/metabolism/pathology
*Gliosis/pathology/etiology/metabolism
Animals
Glial Fibrillary Acidic Protein/metabolism
*Foreign-Body Reaction/pathology/etiology/metabolism
Male
Rats

@article {pmid42074318,
year = {2026},
author = {Wang, Y and Lu, J and Feng, X and Yang, B and He, Q and Luo, P and Yang, X},
title = {ATF3/SLC31A1-Mediated Cuproptosis Contributes to Bortezomib-Induced Peripheral Neurotoxicity and Intervention by (-)-Epigallocatechin Gallate.},
journal = {International journal of molecular sciences},
volume = {27},
number = {8},
pages = {},
doi = {10.3390/ijms27083680},
pmid = {42074318},
issn = {1422-0067},
support = {82274018//National Natural Science Foundation of China/ ; },
mesh = {Animals ; *Bortezomib/adverse effects ; Mice ; *Activating Transcription Factor 3/metabolism/genetics ; *Catechin/analogs & derivatives/pharmacology ; Male ; *Copper Transporter 1/metabolism/genetics ; Copper/metabolism ; *Neurotoxicity Syndromes/metabolism/etiology/drug therapy ; *Peripheral Nervous System Diseases/chemically induced/metabolism/drug therapy ; Mice, Inbred C57BL ; },
abstract = {Bortezomib (BTZ), the first-generation proteasome inhibitor, has been approved for the treatment of relapsed, refractory, and newly diagnosed multiple myeloma. Despite its remarkable antitumor efficacy, BTZ treatment is severely limited by a high incidence of systemic adverse reactions, primarily due to its non-selective cytotoxicity toward rapidly dividing normal cells and its potent neurotoxic effects on peripheral neurons. Bortezomib-induced peripheral neurotoxicity (BIPN) manifests as neuropathic pain and sensory abnormalities, affecting up to 31% to 64% of patients and limiting BTZ's clinical use. Currently, the underlying mechanisms of BIPN are poorly understood. To evaluate the effects of BTZ on the functions of peripheral nerves in mice, we administered an intraperitoneal injection treatment for four weeks. Results indicated that BIPN caused mechanical allodynia, gait abnormalities, and pathological changes in myelin and axons in mice. This study confirms that BTZ upregulates the expression of the activating transcription factor 3 (ATF3), which in turn mediates the increased expression of the copper transporter SLC31A1, causing dysregulation of intracellular copper ion homeostasis and subsequent copper accumulation, and ultimately inducing the development of peripheral neurotoxicity. Elevated intracellular copper concentration exerts a dual effect: it directly promotes the oligomerization of Dihydrolipoamide S-acetyltransferase (DLAT) and concurrently damages the iron-sulfur cluster protein ferredoxin 1 (FDX1), collectively triggering the onset of cuproptosis. Green tea has garnered attention for its rich content of catechins, with (-)-Epigallocatechin Gallate (EGCG) being the most abundant catechin present. This study uncovers the molecular mechanism by which EGCG inhibits BTZ-induced cuproptosis through targeted regulation of copper homeostasis. Analyses demonstrate that EGCG significantly downregulates the expression of the copper transporter SLC31A1, thereby effectively suppressing transmembrane influx of extracellular copper ions. This intervention markedly reduces intracellular copper overload, eliciting a dual regulatory effect: on one hand, the decreased copper concentration directly inhibits the oligomerization of DLAT; on the other hand, it effectively protects the iron-sulfur cluster protein FDX1 from damage. This study aims to systematically elucidate the molecular mechanisms underlying BIPN and to evaluate the therapeutic potential of EGCG in alleviating BIPN, offering a novel therapeutic strategy for the prevention and treatment of BIPN.},
}

Animals
*Bortezomib/adverse effects
Mice
*Activating Transcription Factor 3/metabolism/genetics
*Catechin/analogs & derivatives/pharmacology
Male
*Copper Transporter 1/metabolism/genetics
Copper/metabolism
*Neurotoxicity Syndromes/metabolism/etiology/drug therapy
*Peripheral Nervous System Diseases/chemically induced/metabolism/drug therapy
Mice, Inbred C57BL

@article {pmid42074864,
year = {2026},
author = {Cywka, KB and Skarzynski, PH and Czaplicka, EA and Skarzynski, H},
title = {Outcomes of Bonebridge Implantation in 10 Patients with Rare Genetic Syndromes and Difficult Anatomy.},
journal = {Journal of clinical medicine},
volume = {15},
number = {8},
pages = {},
doi = {10.3390/jcm15083064},
pmid = {42074864},
issn = {2077-0383},
abstract = {Background: Congenital hearing loss occurs in about 2 of every 1000 newborns, of which half probably have a genetic origin. In syndromic patients, hearing impairment often results from craniofacial malformations affecting the outer and middle ear. Anatomical limitations such as microtia or external auditory canal atresia often preclude conventional air-conduction hearing aids, leaving bone-conduction devices as one viable option. However, surgical intervention in such patients is challenging. This study aimed to evaluate the audiological outcomes, safety, and effectiveness of the Bonebridge BCI 602 implant in 10 patients with genetic syndromes. Methods: The case series was made up of 10 patients aged 6-45 years, each diagnosed with a congenital syndrome affecting the external and/or middle ear. All cases involved surgical implantation of the Bonebridge system. Audiological outcomes were evaluated in free-field conditions on the day of sound processor activation and at 3-6 months follow-up via pure-tone and speech audiometry. Results: All surgical procedures were completed without serious adverse events, and the incidence of postoperative complications was low. Audiological outcomes showed clinically significant hearing improvement in all patients following Bonebridge implantation. Post-implantation hearing thresholds ranged from 25 to 40 dB HL, with notable gains in speech perception in both quiet and noisy environments. Conclusions: The Bonebridge implant appears to be a safe and effective option for auditory rehabilitation in patients with hearing loss associated with various genetic syndromes involving craniofacial malformation. However, this complex patient population requires individual assessment, interdisciplinary evaluation, and careful surgical planning.},
}

@article {pmid42076534,
year = {2026},
author = {Alzahrani, SI and Alomari, N and Alkilani, S and Alghamdi, L and Melhem, B},
title = {Design and Performance Evaluation of a Low-Cost High-SNR EOG Sensing System for Arabic Locked-In Syndrome Communication.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {8},
pages = {},
doi = {10.3390/s26082425},
pmid = {42076534},
issn = {1424-8220},
mesh = {Humans ; *Electrooculography/methods/instrumentation ; Signal-To-Noise Ratio ; *Locked-In Syndrome/physiopathology/diagnosis ; Adult ; Male ; Eye Movements/physiology ; Female ; Communication Devices for People with Disabilities ; Signal Processing, Computer-Assisted ; Communication ; Equipment Design ; },
abstract = {Locked-in Syndrome (LIS) is a neurological condition in which individuals remain conscious but experience complete paralysis of voluntary muscles, except for eye movements-highlighting the need for reliable assistive communication technologies. This study presents the design and evaluation of an Arabic electrooculogram (EOG)-based communication system with adaptive classification capabilities for LIS applications. A custom-designed EOG acquisition circuit incorporating filtering and amplification stages was implemented and compared with the OpenBCI Cyton board. The system employed a hybrid classification approach combining amplitude, temporal, and statistical features to distinguish between blinks and voluntary vertical eye movements. Testing with ten healthy subjects yielded a mean classification accuracy of 83.96% ± 4.59% and an information transfer rate of 10.43 letters per minute, corresponding to a 30.38% improvement over conventional approaches. The custom-designed circuit achieved a signal-to-noise ratio of 25.21 dB, outperforming the OpenBCI Cyton board by 8% while reducing system cost by 62%. The integration with a Morse code-based interface enabled Arabic letter composition, while the system incorporated auto-completion and text-to-speech functionalities to further enhance communication efficiency. This cost-effective solution addresses a critical gap in assistive technologies for Arabic-speaking individuals with LIS and shows strong potential for enhancing their communication abilities and overall quality of life.},
}

Humans
*Electrooculography/methods/instrumentation
Signal-To-Noise Ratio
*Locked-In Syndrome/physiopathology/diagnosis
Adult
Male
Eye Movements/physiology
Female
Communication Devices for People with Disabilities
Signal Processing, Computer-Assisted
Communication
Equipment Design

@article {pmid42076632,
year = {2026},
author = {Liu, X and Chen, R and Wu, F and Yu, B and Zhou, G and Hu, S and Zhang, H and Wang, P and Xu, B and Zhuang, L},
title = {Advanced Sensing and Delivery Technologies for Nose-to-Brain Administration: From Nanocarriers to Sensor-Integrated Organ-on-Chips.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {8},
pages = {},
doi = {10.3390/s26082523},
pmid = {42076632},
issn = {1424-8220},
mesh = {Humans ; *Administration, Intranasal/methods ; *Drug Delivery Systems/methods ; Blood-Brain Barrier/metabolism ; *Drug Carriers/chemistry ; *Brain/metabolism/drug effects ; *Lab-On-A-Chip Devices ; Animals ; *Biosensing Techniques ; Nanotechnology ; *Nanoparticles/chemistry ; },
abstract = {Central nervous system (CNS) disorders represent a growing healthcare burden, and various drugs are developed for their treatment. However, the blood-brain barrier (BBB) prevents over 98% of therapeutics from reaching brain tissue. Intranasal delivery provides a promising alternative by exploiting olfactory and trigeminal nerve pathways to circumvent the BBB. This review surveys recent advances in nose-to-brain delivery technologies, from carrier design to evaluation methods. Polymeric and lipid-based nanocarriers show enhanced mucosal penetration and prolonged residence time, and microneedle platforms further enable controlled drug release with minimal discomfort. To evaluate these delivery strategies, sensor-integrated organ-on-chip models provide more physiologically relevant testing than static cultures. Although persistent challenges such as rapid mucociliary clearance and formulation stability remain, combining nanotechnology with microfluidic devices and computational modeling shows potential for developing patient-specific therapeutics.},
}

Humans
*Administration, Intranasal/methods
*Drug Delivery Systems/methods
Blood-Brain Barrier/metabolism
*Drug Carriers/chemistry
*Brain/metabolism/drug effects
*Lab-On-A-Chip Devices
Animals
*Biosensing Techniques
Nanotechnology
*Nanoparticles/chemistry

@article {pmid42077356,
year = {2026},
author = {Yuan, Z and Shi, Z and Wang, Z},
title = {Brain-computer interfaces: an engineering black-box swindle or a lone advance guided by deep learning.},
journal = {Frontiers in neuroscience},
volume = {20},
number = {},
pages = {1783020},
pmid = {42077356},
issn = {1662-4548},
}

@article {pmid42077635,
year = {2025},
author = {Herbert, C and Acuna, VR and Kneipp, RRK and Kapfer, NI},
title = {Exploring individual biases in BCI research and users: Does gender matter?.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1695370},
pmid = {42077635},
issn = {1662-5161},
abstract = {OBJECTIVE: Brain-Computer Interface (BCI) is an interdisciplinary research field characterized by rapid technological advances and collaborative efforts to develop user-friendly, adaptive devices that enable healthy and non-responsive users to communicate and interact with their environment through brain signals elicited by specific instructions or tasks. However, research often shows gender bias, especially in scientific disciplines with strong technological, medical, or social foundations. Gender biases have been found among scientists conducting and publishing research. They may also exist among examiners and study participants.

RESEARCH QUESTION AND METHODS: This study investigates whether gender biases are present in BCI research, particularly in the distribution of women and men across editorial boards and authorship of studies focusing on psychological human factors that influence BCI performance and usability. We systematically analyzed the gender distribution in neuroscientific journals that accept BCI research or have a strong focus on BCI, reviewed their editorial boards, analyzed BCI publications -including those related to psychological human factors-and examined gender biases among study participants. Additionally, we reviewed EEG studies investigating sex- or gender-related differences in EEG signals relevant to BCI research.

RESULTS: We observed significant differences in the representation of women and men among editorial board members and BCI authors, including first-, co-, and last-authorship. Similarly, there were differences in the gender distribution of participants in BCI studies. Moreover, the literature review suggests potential differences in brain signals between women and men within the studied samples. The impact of these differences on performance in BCIs, such as motor-imagery SMR-BCIs, SSVEP-BCIs, and P300-BCIs, as well as training methods and BCI usability, still needs to be explored.

CONCLUSION: Our findings emphasize the importance of increasing awareness of gender-, sex-, and user-related factors in BCI research. In line with recent perspectives that highlight the need to address gender biases and individual differences in the language of the user, their motivation or cultural background, future BCI research should focus on systematically examining gender and sex differences. This will help promote gender equality in BCI research and lead to a better understanding of users' needs, preferences, and individual characteristics.},
}

@article {pmid42079191,
year = {2026},
author = {Hakim, R and Jaggi, A and Heo, G and Matsumoto, H and Uchida, N and Watabe-Uchida, M and Datta, SR and Musall, S and Sabatini, BL},
title = {Spectral envelopes of facial movements predict intention, cortical representations, and neural prosthetic control.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.09.10.675423},
pmid = {42079191},
issn = {2692-8205},
abstract = {1Animals, including humans, use coordinated facial movements to sample the environment, ingest nutrients, and communicate. Rodents, in particular, produce rhythmic facial movements during spontaneous behavior and cognitive tasks. Measuring these movements precisely and linking them to neural activity remains challenging. We introduce face-rhythm, an unsupervised pipeline that combines markerless point tracking, spectral analysis, and non-negative tensor component analysis to decompose facial video into a small set of interpretable components. Applied to videos of mice during a Pavlovian odor-reward task, a brain-machine interface (BMI) task, and free behavior, face-rhythm recovers human-interpretable behaviors such as whisking, sniffing, licking, and more subtle behaviors. The resulting components are consistent across animals, are sufficient to decode task variables or internal belief states, and explain cortical activity using a low-rank representation. We also find that the activity of neurons in face-associated primary motor cortex (M1) is predicted well by a phase-invariant spectral transformation of facial movements above ~ 0.5 Hz, while slower movements retain a phase-variant representation better predicted by the instantaneous position of the face; individual neurons can show either or both forms of tuning. A systematic comparison against deep-learning point-tracking models, contrastive-learning embeddings, and vision-transformer features places face-rhythm competitively across tasks while also achieving the goal of producing a low-dimensional, interpretable description of rodent facial behavior that is closely linked to cortical activity.},
}

@article {pmid42079853,
year = {2026},
author = {Ma, T and Huggins, JE and Kang, J},
title = {Bayesian Signal Matching for Transfer Learning in ERP-Based Brain Computer Interface.},
journal = {Journal of the American Statistical Association},
volume = {121},
number = {553},
pages = {100-112},
pmid = {42079853},
issn = {0162-1459},
abstract = {An Event-Related Potential (ERP)-based Brain-Computer Interface (BCI) Speller System assists people with disabilities to communicate by decoding electroencephalogram (EEG) signals. A P300-ERP embedded in EEG signals arises in response to a rare, but relevant event (target) among a series of irrelevant events (non-target). Different machine learning methods have constructed binary classifiers to detect target events, known as calibration. The existing calibration strategy uses data from participants themselves with lengthy training time. Participants feel bored and distracted, which causes biased P300 estimation and decreased prediction accuracy. To resolve this issue, we propose a Bayesian signal matching (BSM) framework to calibrate EEG signals from a new participant using data from source participants. BSM specifies the joint distribution of stimulus-specific EEG signals among source participants via a Bayesian hierarchical mixture model. We apply the inference strategy. If source and new participants are similar, they share the same set of model parameters; otherwise, they keep their own sets of model parameters; we predict on the testing data using parameters of the baseline cluster directly. Our hierarchical framework can be generalized to other base classifiers with parametric forms. We demonstrate the advantages of BSM using simulations and focus on the real data analysis among participants with neuro-degenerative diseases. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.},
}

@article {pmid42080047,
year = {2026},
author = {Du, X and Wang, H and Xi, M and Qiu, S and Lv, Y},
title = {SSA-DCNet: a cross-session MI-EEG classification network based on deformable convolution and spatial-shift attention.},
journal = {Biomedical engineering letters},
volume = {16},
number = {3},
pages = {769-780},
pmid = {42080047},
issn = {2093-985X},
abstract = {Brain-computer interfaces (BCIs) based on motor imagery (MI) electroencephalogram (EEG) signals have shown tremendous potential in neurorehabilitation due to their non-invasive acquisition and ease of use. However, the cross-session nature of EEG signals-where recordings from the same subject at different sessions may vary due to fluctuations in physiological state and environmental conditions-presents a significant challenge. Efficient extraction and preservation of temporal and spatial features from EEG signals can capture invariant neural activation patterns while suppressing session-dependent noise and variability, thereby greatly enhancing the robustness of cross‑session motor imagery classification. To address the suboptimal performance of existing models in cross-session MI-EEG classification, this paper proposes Spatial-Shift Attention Deformable Convolution Network-SSA-DCNet, a compact convolutional neural network in which temporal filtering is implemented via a two-dimensional deformable convolution of size 1 × 64, so that the sampling grid dynamically adapts to the non-uniform distributions of informative EEG segments while operating on a 1 × 64 kernel along the temporal axis. Thereafter, a spatial-shift attention architecture expands each intermediate feature map from C to 3 C channels, evenly splits them into three subsets, applies distinct spatial-shift operations to each subset, and finally merges them via a split-attention that recalibrates channel weights to emphasize spatial patterns stable across sessions. On the public BCI Competition IV-2a and 2b datasets, SSA-DCNet achieved classification accuracies of 84.72% and 90.45%, respectively. Moreover, t-SNE visualizations provide intuitive evidence, underscoring its superior discriminative power and robust cross-session generalization.},
}

@article {pmid42080048,
year = {2026},
author = {Wang, C and Li, M and Zhang, P and Zhang, Z and Wang, F and Kang, F},
title = {A Multi-perception fusion using shared-control method for brain-mobile robot.},
journal = {Biomedical engineering letters},
volume = {16},
number = {3},
pages = {781-798},
pmid = {42080048},
issn = {2093-985X},
abstract = {For human-robot collaboration, brain-computer interface is promising to express human perception to improve the adaptability of human-robot collaboration in complex environments. In this study, a multi-perception fusion using shared control method (MPF-SC) is proposed to accurately integrate human perception and robot perception. This MPF-SC is applied in brain-controlled mobile robots to accomplish navigation and obstacle avoidance in complex terrain with multiple undetectable obstacles. The MPF-SC establishes a mapping relationship between visual stimulus interface and environment by computer vision, and utilizes a grid costmap to describe the human perception. It integrates EEG and EMG signals with user intent to dynamically adjust the grid costmap, mapping obstacle regions and integrating robot navigation to jointly accomplish driving tasks-with the aim of achieving human-machine shared perception. Sixteen subjects participated in an online obstacle avoidance experiment and compared the performance of the proposed method with two traditional methods. The research results show that the MPF-SC can generate smoother trajectories, achieve a significantly reduced collision rate during navigation, and significantly enhance user comfort. The MPF-SC based on brain-computer interface, fully leverages human anticipation of risks and the robot's perception of obstacle environments, demonstrating that bilateral intelligence is capable of adapting to increasingly complex environments, thereby offering a novel avenue and intuitive avenue for human-machine shared control.},
}

@article {pmid42080051,
year = {2026},
author = {Duan, S and Li, P and Yuan, D and Wang, K and Yu, D and Cheng, L},
title = {Multi-Scale convolutional neural networks integrated with self-attention for motor imagery EEG decoding.},
journal = {Biomedical engineering letters},
volume = {16},
number = {3},
pages = {719-734},
pmid = {42080051},
issn = {2093-985X},
abstract = {Brain-computer interface (BCI), as a cutting-edge technology with great application prospects, has received widespread attention in recent years. Motor imagery (MI) electroencephalography (EEG) classification is a key component of brain-computer interfaces, widely used in applications such as assisting people with disabilities, controlling devices, and interacting with environments. However, since convolutional neural networks (CNNs) extract only local temporal features, they may be unable to capture the long-term dependencies used for EEG decoding, which can have an impact on the decoding performance. In order to address this problem, this paper proposes a novel deep learning network that combines a multi-scale convolutional neural network with an attention mechanism to capture temporal information and global dependencies. First, a multi-scale structure is designed to extract spatial-temporal information at different scales and multimodal information from both the mean and variance perspectives. Second, a squeeze-excite-compress (SEC) module is used to enhance the feature response of each branch and reduce information redundancy. Finally, an encoder with a multi-head attention mechanism extracts more discriminative features and highlights the most valuable information in MI-EEG data. In addition, this paper uses a data augmentation method of signal reorganization to expand the dataset and further enhance the generalization ability of the network. Our method was evaluated by performing experiments on the BCI Competition IV-2a (BCI-IV-2a) and High Gamma Dataset (HGD) with classification accuracies of 85.26% and 95.86%, respectively. The experimental results show that our method achieves state-of-the-art performance and has great potential to be a new baseline for general EEG decoding.},
}

@article {pmid42080269,
year = {2026},
author = {Liu, J and Yang, X and Li, M and Zheng, C and Wang, K and Ren, X and Zhang, B and Li, H and Jiang, D and Li, W and Xu, M},
title = {Application of Graphene Dry Electrode in 512-Lead EEG Cap and Real-Time Monitoring EEG System.},
journal = {ACS applied materials & interfaces},
volume = {},
number = {},
pages = {},
doi = {10.1021/acsami.5c25282},
pmid = {42080269},
issn = {1944-8252},
abstract = {Dry electroencephalography (EEG) electrodes with low noise and minimal potential drift are crucial for daily wearable and high-density noninvasive brain-computer interfaces. In this study, a Na-doped vertical graphene dry electrode with a diameter of 2.8 mm was prepared to construct a 512-lead ultrahigh-density EEG cap and wireless 8- and 32-lead EEG headbands. The Na-doped vertical graphene layer has a three-dimensional architectural structure that absorbs sweat from the scalp and converts it into an Na[+]-mediated solid electrolyte, electrically connecting the device to the scalp. The optimized graphene dry electrodes exhibited low scalp-contact resistance (dry: 3.8-6.5 kΩ, H2O: 4.5 kΩ), self-noise (11.1 μV), DC offset voltage (15.6 mV), and potential drift (189.9 μV). The EEG cap, composed of 512 dry graphene electrodes, recorded different rhythm signals with a high signal-to-noise ratio, demonstrating excellent repeatability and long-term stability over 103 days. In addition, a task-state strategy was designed that combined the intensity ratio of fast and slow waves with frequency-domain event-related potentials, demonstrating the reliability of dry electrode headband systems for rapid attention analysis during daily wear. This wearable metal-doped vertical graphene dry-electrode device, especially the 512-lead ultrahigh-density dry-electrode EEG cap, holds promise for applications in brain function research, neuroimaging, and brain-computer interface control.},
}

@article {pmid42081511,
year = {2026},
author = {Zhong, M and Jiang, Y and Huang, S and Chen, P and Liu, H and Zhang, Y and He, X and Yang, F and Fu, Q and Zheng, Y and Guo, Y and Lin, Q},
title = {A Closed-Loop ta-VNS System Synchronized with BCI-Based Motor Training for Post-Stroke Upper Limb Rehabilitation.},
journal = {Journal of visualized experiments : JoVE},
volume = {},
number = {230},
pages = {},
doi = {10.3791/69272},
pmid = {42081511},
issn = {1940-087X},
mesh = {*Brain-Computer Interfaces ; *Stroke Rehabilitation/methods ; Electroencephalography/methods ; Humans ; *Upper Extremity/physiopathology ; *Vagus Nerve Stimulation/methods/instrumentation ; Stroke/physiopathology ; *Transcutaneous Electric Nerve Stimulation/methods/instrumentation ; },
abstract = {Transcutaneous auricular vagus nerve stimulation (ta-VNS) involves applying electrical stimulation via electrodes to the auricular concha. This activates vagal afferent fibers, initiating an ascending pathway from the periphery to the brainstem, which ultimately stimulates central vagal projections and promotes neural plasticity. Previous studies have demonstrated that combining ta-VNS with motor training offers synergistic benefits for motor recovery after stroke. However, these combined approaches typically employ open-loop stimulation with fixed parameters, lacking real-time closed-loop responsiveness to dynamic neural activity. To address this limitation, we developed a novel closed-loop ta-VNS system synchronized with electroencephalography (EEG)-triggered brain-computer interface (BCI) motor training. This system was designed to enhance corticospinal coupling and promote synaptic plasticity. We established a standardized protocol for applying this closed-loop ta-VNS system synchronized with BCI-based motor training in stroke patients. Using EEG-based functional assessment, we compared the effects of the closed-loop ta-VNS system synchronized with BCI-based motor training to those of sham ta-VNS synchronized with BCI-based motor training. This work provides the methodological and theoretical groundwork for the clinical application of this approach in stroke rehabilitation.},
}

*Brain-Computer Interfaces
*Stroke Rehabilitation/methods
Electroencephalography/methods
Humans
*Upper Extremity/physiopathology
*Vagus Nerve Stimulation/methods/instrumentation
Stroke/physiopathology
*Transcutaneous Electric Nerve Stimulation/methods/instrumentation

@article {pmid42082585,
year = {2026},
author = {Wang, G and Huang, Y and Muckli, L and Faccio, D},
title = {Symbiotic brain-machine drawing via visual brain-computer interfaces.},
journal = {npj biomedical innovations},
volume = {3},
number = {1},
pages = {},
pmid = {42082585},
issn = {3005-1444},
support = {EP/T00097X/1, EP/Y029097/1, EP/ Z533166/1//UK Research and Innovation/ ; },
abstract = {Brain-computer interfaces (BCIs) are evolving from research prototypes into clinical, assistive, and performance enhancement technologies. Despite the rapid rise and promise of implantable technologies, there is a need for better and more capable wearable and non-invasive approaches whilst also minimising hardware requirements. We present a non-invasive BCI for iterative selection-based mind-drawing that infers a subject's internal visual intent through iterative selection of adaptive visual probes presented on a screen encoded at different flicker-frequencies and analyses the steady-state visual evoked potentials (SSVEPs). Gabor-inspired or machine-learned policies dynamically update the spatial placement of the visual probes on the screen to explore the image space and reconstruct simple imagined shapes within approximately two minutes or less using just single-channel EEG data. Additionally, by leveraging stable diffusion models, reconstructed mental images can be transformed into realistic and detailed visual representations. Whilst we expect that similar results might be achievable with e.g. eye-tracking techniques, our work shows that symbiotic human-AI interaction can increase BCI bit-rates by more than a factor 5x, providing a platform for future development of AI-augmented BCI.},
}

@article {pmid42068651,
year = {2026},
author = {Jia, J and Wang, S and Chen, Y and Li, H and Zheng, C and Deng, J and Zhao, F},
title = {Heavy metals, gastrointestinal polymer-related materials, and gut microbiome in an Indo-Pacific bottlenose dolphin (Tursiops aduncus) recovered from a fisheries bycatch-related event in the East China Sea.},
journal = {Ecotoxicology and environmental safety},
volume = {317},
number = {},
pages = {120191},
doi = {10.1016/j.ecoenv.2026.120191},
pmid = {42068651},
issn = {1090-2414},
abstract = {Incidental cetacean bycatch provides irreplaceable opportunities to investigate population dynamics, mortality, and health. This multidisciplinary study examined morphology, age, gut microbiome, heavy metals, and gastrointestinal polymer-related materials in an immature male Indo-Pacific bottlenose dolphin (Tursiops aduncus, 248 cm, 114 kg, 5 years) accidentally captured in the East China Sea. Morphometrics indicated excellent body condition (BCI = 0.506) and superior dorsal fin shape compared to captive individuals, highlighting the role of natural environments in development. The gut microbiome was dominated by Proteobacteria and Firmicutes, showing segment-specific variation. Heavy metals accumulated mainly as Cd in kidneys and Cu and Zn in liver, with overall levels lower than those in other Chinese marine regions. LDIR analysis indicated the presence of polymer-related materials in the gastrointestinal tract, including reported matches to polyamide and chlorinated polyethylene, which may be associated with fisheries activities. These findings provide critical baseline ecotoxicological data for the East China Sea and underscore the importance of standardized passive biomonitoring networks that transform bycatch events into valuable scientific and conservation resources.},
}

@article {pmid42065981,
year = {2026},
author = {Wang, Z and Shen, L and Mi, X and Cheng, L and Yang, Y and Wang, B and Jung, TP and Wan, F},
title = {Unified Online Adaptation Framework for Correlation Analysis-based Spatial Filtering Methods in SSVEP-based BCIs.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2026.3689883},
pmid = {42065981},
issn = {2168-2208},
abstract = {Online adaptation is a promising technique for achieving calibration-free recognition in user-friendly brain-computer interfaces (BCIs) but remains underexplored for steady-state visual evoked potential (SSVEP) recognition. In our previous work on online multi-stimulus canonical correlation analysis (OMSCCA), we introduced a state-of-the-art scheme for the online adaptation of SSVEP spatial filters. Despite its effectiveness, this approach can not be directly extended to other advanced spatial filtering methods, thereby seriously limiting the broader development of calibration-free algorithms. To address this limitation, we propose a unified online adaptation frame work for correlation analysis (CA)-based spatial filtering methods, encompassing both spatial filter computation and utilization. Specifically, we extend the least-squares (LS) unified framework originally designed for full calibration with large amounts of training data to the online adaptation scenario without any pre-calibration, thereby enabling continuous updates of spatial filters. Moreover, to sufficiently utilize spatial filters, we introduce a cross-stimulus transfer method for online adaptation of the common impulse response and generation of user-specific templates for all stimuli using limited online unlabeled data. Finally, leveraging the proposed unified framework, we adapt three advanced spatial filtering methods from their calibration based counter parts to online adaptation paradigms and validate their performance through simulation studies. Our results demonstrate the framework's effectiveness in promoting the development ofzero-calibration SSVEP-based BCIs. Compared to the OMSCCA, the proposed online adaptation methods canimprove the recognition performance by more than 12%. This work provides a generalizable approach for transforming existing calibration-based methods into adaptive, user-friendly solutions for practical BCI applications.},
}

@article {pmid42066562,
year = {2026},
author = {Li, K and Zhang, C and Li, R and Yuan, X and Jia, A and Zhang, K and Deng, W},
title = {Disrupted global and local brain functional network dynamics in adolescents with obsessive-compulsive disorder.},
journal = {Comprehensive psychiatry},
volume = {148},
number = {},
pages = {152702},
doi = {10.1016/j.comppsych.2026.152702},
pmid = {42066562},
issn = {1532-8384},
abstract = {BACKGROUND: Obsessive-compulsive disorder (OCD) frequently emerges during adolescence, a critical period for the development of static and dynamic properties of large-scale brain networks. Although previous studies have reported altered static connectivity in adolescents with OCD, the temporal organization of functional networks during this stage remains largely unexplored.

METHODS: We analyzed resting-state fMRI data from 40 adolescents with OCD and 40 age- and sex-matched healthy controls. Group independent component analysis (ICA) was used to identify intrinsic connectivity networks (ICNs). A sliding-window approach and k-means clustering were applied to derive dynamic brain states, while graph-theoretical metrics (strength, local efficiency, clustering coefficient) were computed to assess nodal variability over time. Group comparisons were performed using general linear models controlling for age and sex, and symptom correlations were tested using partial correlation analyses.

RESULTS: Compared to controls, OCD patients spent significantly less time in a globally integrated brain state characterized by strong intra- and inter-network connectivity. At the local level, reduced temporal variability was observed in the striatum, thalamus, and dorsolateral prefrontal cortex, key nodes of the cortico-striato-thalamo-cortical (CSTC) circuit. Notably, reduced striatal variability correlated with greater OCD symptom severity and decreased time in the integrated brain state.

CONCLUSIONS: These findings reveal disrupted dynamic network integration and reduced functional flexibility in adolescents with OCD, both globally and locally. This multilayered impairment may reflect early pathophysiological mechanisms and offers potential targets for age-sensitive neuromodulation strategies.

CLINICAL TRIAL REGISTRATION: ChiCTR2400092275, Chinese Clinical Trial Registry (www.chictr.org.cn).},
}

@article {pmid42058018,
year = {2026},
author = {Kokorina, A and Syrov, N and Yakovlev, L and Lebedev, M},
title = {Case Report: post-stroke rehabilitation with a visuomotor transformation-based brain-computer interface.},
journal = {Frontiers in human neuroscience},
volume = {20},
number = {},
pages = {1774409},
pmid = {42058018},
issn = {1662-5161},
abstract = {Brain-computer interfaces (BCIs) are increasingly explored as tools for post-stroke neurorehabilitation. Motor imagery (MI)-based paradigms are widely used but may be difficult for some patients to perform reliably, motivating the exploration of alternative control strategies. This study presents a retrospective exploratory case series (n = 5) evaluating the feasibility and safety of a P300-based BCI paradigm designed to engage visuomotor transformation processes during upper limb rehabilitation. Two patients underwent rehabilitation using the P300-based paradigm, while three patients used an MI-based BCI within the same rehabilitation framework. In both conditions, BCI control was integrated with a robotic orthosis and an immersive virtual reality (VR) environment. BCI performance, neurophysiological responses (event-related potentials and event-related desynchronization), and clinical measures (Fugl-Meyer Assessment of the Upper Extremity, NIHSS) were assessed before and after a 10-session rehabilitation course. All participants were able to achieve BCI control above chance level. Across cases, changes in clinical scores and consistent neurophysiological patterns associated with task engagement were observed. No adverse events or clinically significant safety concerns were identified. These findings suggest that a P300-based BCI paradigm incorporating visuomotor transformation can be feasibly implemented within a VR-assisted robotic rehabilitation framework. Given the exploratory design, small sample size, and heterogeneity of the cohort, the results should be interpreted as hypothesis-generating. Further controlled studies are required to determine the clinical relevance and potential applications of this approach.},
}

@article {pmid42058718,
year = {2026},
author = {G, A and K, D},
title = {Multi-source domain generalization with few-shot fine-tuning (MSDG-FT) for cross-dataset EEG mental workload classification.},
journal = {MethodsX},
volume = {16},
number = {},
pages = {103913},
pmid = {42058718},
issn = {2215-0161},
abstract = {EEG-based mental workload (MWL) classifiers consistently achieve high within-dataset accuracy but collapse when applied across datasets recorded under different paradigms or hardware. This cross-domain generalisation gap limits real-world deployment of passive brain-computer interfaces. We evaluate transfer strategies across three publicly available EEG-MWL datasets - CogBCI (29 subjects, 3 sessions), Neuro2021 (15 subjects), and STEW - revealing a mean within-domain accuracy of 78.8% versus cross-domain accuracy of only 44.0%, a gap of 34.8 percentage points. We propose Multi-Source Domain Generalisation with Few-Shot Fine-Tuning (MSDG-FT), which reduces this gap to 6.6 percentage points using as few as 50 labelled calibration samples. Cross-session drift on CogBCI is further characterised across all six session-pair directions, showing near-chance baseline accuracy (36.0%) that recovers to 51.6% with minimal calibration.•A 3 × 3 cross-domain transfer matrix quantifies generalisation failure across three heterogeneous EEG-MWL datasets and establishes a reproducible benchmark for future methods.•Multi-source pre-training combined with few-shot target-domain fine-tuning (MSDG-FT) closes the 34.8% transfer gap to 6.6% using only 50 labelled samples from the target domain.•Random calibration (20 samples) matches sophisticated confidence-weighted selection (p = 0.28), demonstrating simple baselines suffice. Cross-session benefits vary by dataset: CogBCI +15.6%, Neuro2021 +3.5%, indicating task-dependent effectiveness.},
}

@article {pmid42059314,
year = {2026},
author = {Maya, I and Noiret, B and Merlot, B and Denost, Q},
title = {Robotic posterior pelvic exenteration with perineal reconstruction with a fasciocutaneous flap - A video vignette.},
journal = {Colorectal disease : the official journal of the Association of Coloproctology of Great Britain and Ireland},
volume = {28},
number = {5},
pages = {e70470},
doi = {10.1111/codi.70470},
pmid = {42059314},
issn = {1463-1318},
}

@article {pmid42060426,
year = {2026},
author = {Lou, X and Li, X and Meng, H and Li, Z},
title = {Subject-Independent Deep Learning Framework for Motor Imagery Electroencephalogram Decoding in Neurorehabilitation.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2026.3689121},
pmid = {42060426},
issn = {2168-2208},
abstract = {Motor imagery (MI) has emerged as a pivotal paradigm in non-invasive brain-computer interfaces (BCIs) for neurorehabilitation, enabling motor function restoration through mental rehearsal of movements. However, traditional MI electroencephalogram (EEG) classification models face significant challenges due to high inter-subject variability and the expensive requirement of annotated EEG data for each new subject. To tackle these limitations, we introduce a deep learning framework, the Dual-branch Subject-aligned Generalization Network (DSGNet). DSGNet simultaneously extracts temporal and spectral EEG features through dual complementary convolutional branches and incorporates a novel class alignment loss to enforce domain-invariant representation across subjects, enabling generalization to unseen individuals without requiring subject-specific labeled data. We evaluate DSGNet on four public MI-EEG datasets-OpenBMI, BCI Competition IV 2a, SHU Version 5, and BCI Competition IV 2b-under a rigorous leave-one-subject-out cross-validation protocol. Experimental results show that DSGNet achieves the highest accuracy on the three-class and four-class datasets, with improvements of 0.22% and 2.15% over the strongest baselines, respectively, while maintaining comparable performance on the binary-class dataset. These findings highlight the effectiveness of class-structure alignment in developing reliable subject-independent BCI systems for neurorehabilitation.},
}

@article {pmid42060724,
year = {2026},
author = {Wang, Y and Cheng, L and Li, D and Lu, Y and Hopkins, WD and Sherwood, CC and Xu, T and Liu, C and Paxinos, G and Jiang, T and Chu, C and Fan, L},
title = {Homologous specialization of arcuate fasciculus ventrolateral frontal connectivity in marmosets and humans.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {123},
number = {18},
pages = {e2600429123},
doi = {10.1073/pnas.2600429123},
pmid = {42060724},
issn = {1091-6490},
support = {2021ZD0200203//Brain Science and Brain-like Intelligence Technology - National Science and Technology Major Project/ ; 82472061//Natural Science Foundation of China/ ; 82202253//Natural Science Foundation of China/ ; 62250058//Natural Science Foundation of China/ ; 2022M722915//China Postdoctoral Science Foundation ()/ ; 2024M761725//China Postdoctoral Science Foundation ()/ ; AD22035125//Guangxi Science and Technology Base and Talent Special Project/ ; },
mesh = {Animals ; Humans ; *Callithrix/physiology/anatomy & histology ; *Frontal Lobe/physiology/anatomy & histology ; Male ; Neural Pathways/physiology ; Female ; Language ; Pan troglodytes ; Brain Mapping ; Speech/physiology ; Macaca ; Nerve Net/physiology ; },
abstract = {The arcuate fasciculus (af) is a crucial dorsal pathway underpinning human language, yet its weak frontal connectivity in macaques-the standard primate model-creates an evolutionary puzzle. Here, we investigate the common marmoset, a distantly related platyrrhine with high vocal complexity, to test for convergent neural adaptations. By integrating retrograde and anterograde tracing with ultra-high-resolution diffusion MRI, we identified a robust af homolog in marmosets that is anatomically distinct from the superior longitudinal fasciculus. Comparative mapping across marmosets, macaques, chimpanzees, and humans reveals a notable similarity in connectivity patterns: The marmoset af terminates extensively in the ventrolateral frontal cortex, exhibiting a connectivity profile significantly more similar to humans than to that of the phylogenetically closer macaque. Functionally, this pathway targets cortical regions activated during vocal exchanges, partially overlapping with the human speech network. These findings suggest that the frontal connectivity of the dorsal audio-motor pathway is not strictly determined by phylogenetic proximity but represents an evolutionarily labile scaffold that undergoes lineage-specific elaboration under pressure associated with complex vocal communication.},
}

Animals
Humans
*Callithrix/physiology/anatomy & histology
*Frontal Lobe/physiology/anatomy & histology
Male
Neural Pathways/physiology
Female
Language
Pan troglodytes
Brain Mapping
Speech/physiology
Macaca
Nerve Net/physiology

@article {pmid42061401,
year = {2026},
author = {Xu, A and Zhang, J and Wu, B and Xu, M and Wang, T and Shao, C and Bing, S and Huang, Y and Yao, Y and Wang, J and Tang, Y and Cao, J and Yang, B and Shao, X and He, Q and Ying, M},
title = {Acyltransferase ZDHHC22 promotes N-Myc transcriptional activation to drive neuroblastoma progression and chemoresistance.},
journal = {Molecular cell},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.molcel.2026.04.002},
pmid = {42061401},
issn = {1097-4164},
abstract = {MYCN-amplified neuroblastoma is one of the most lethal pediatric malignancies, where aberrant N-Myc-driven transcription promotes tumor progression. As direct targeting of N-Myc has proven challenging, current approaches prioritize understanding the mechanisms that regulate its activity, which remain poorly understood. Here, we demonstrate a crucial role of S-acylation in regulating N-Myc transcriptional activity and identify the acyltransferase zinc finger DHHC-type containing 22 (ZDHHC22) as a key regulator of this process. Mechanistically, ZDHHC22 catalyzes the S-acylation of N-Myc, which enhances its transcriptional activity by facilitating the recruitment of coactivators such as TIP60 and GCN5. Furthermore, N-Myc transcriptionally upregulates ZDHHC22, establishing a feedback loop that contributes to chemoresistance in high-risk neuroblastoma. Targeting ZDHHC22 suppresses neuroblastoma cell growth in vitro and in vivo, particularly in refractory patient-derived models. Collectively, our findings uncover a biological function of ZDHHC22 in regulating N-Myc transcriptional activation and indicate that ZDHHC22 is a promising therapeutic target for N-Myc-driven high-risk neuroblastoma, especially in MYCN-amplified patients.},
}

@article {pmid42061591,
year = {2026},
author = {Zhang, Y and Li, X and Jin, Z and Zhang, J and Li, L},
title = {Distinct Frontal Lobe Subregions Mediate the Emergence and Reporting of Visual Consciousness.},
journal = {NeuroImage},
volume = {},
number = {},
pages = {121964},
doi = {10.1016/j.neuroimage.2026.121964},
pmid = {42061591},
issn = {1095-9572},
abstract = {Persistent debate surrounds whether the frontal lobe supports the emergence or reporting of consciousness, raising the hypothesis that distinct frontal subregions may support these processes. We addressed this by combining electroencephalography (EEG) with eye-tracking in Report and No-Report paradigms. Eye-movement features distinguished conscious and unconscious trials in the no-report task. Event-related potential analyses showed that the Visual Awareness Negativity (VAN) was independent of reporting, whereas P3b occurred only with explicit reports. Importantly, the frontal Dorsal Attention Network (DAN) supported the emergence of consciousness, independent of post-perceptual reporting, as shown by multivoxel pattern analysis showing that a classifier's ability to decode visual consciousness generalized bidirectionally between report and no-report tasks. In contrast, frontal components of the Default Mode Network (DMN) and Frontoparietal Control Network (FPN) encoded visual consciousness only when explicit reports were required, indicating roles in reporting. These findings demonstrate a functional dissociation within the frontal lobe and refine the anatomical framework for the neural basis of visual consciousness.},
}

@article {pmid42062414,
year = {2026},
author = {Turay, T},
title = {A novel 3D region-based speller paradigm for BCI systems.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-026-49989-9},
pmid = {42062414},
issn = {2045-2322},
abstract = {This study proposes and evaluates a novel three-dimensional region-based (3D-RB) speller paradigm designed to enhance classification performance. EEG data were recorded from 15 participants using 32 channels. Classification accuracy was examined across both single electrodes and predefined electrode groups. Subject-dependent analyses revealed that electrodes located in the parietal and occipital regions (e.g., Pz, P7, P8, O1, O2, Oz) achieved the highest single-channel accuracies (approximately 80-85%), whereas central electrodes (e.g., Cz, C3, C4) yielded lower accuracies (around 70-73%). Electrode grouping provided a distinct advantage; for most participants, Group 4 (Parietal + Occipital) and Group 5 (Parietal + Occipital + Central) achieved the highest performance, reaching nearly 99% accuracy. Notably, despite including fewer electrodes, Group 4 performed nearly as well as Group 5, underscoring the practical benefit of optimized electrode selection. Subject-independent (LOSO) analyses showed similar trends. Among single electrodes, P7, P8, O1, and O2 achieved the highest accuracies (approximately 78-79%), while central electrodes (e.g., Cz, Cp1, Cp2, C3, C4) remained lower (70-73%). Electrode groups again outperformed single channels, with Group 4 and Group 5 reaching approximately 89-91% accuracy. The comparable performance of Group 4, despite fewer electrodes, highlights its practical advantage for real-world applications. Grand Average ERP analyses indicated that differences between target and non-target stimuli primarily emerged within early and mid-latency time windows, with these effects being more pronounced over parietal and occipital regions. Taken together, these findings demonstrate that incorporating three-dimensional visual effects within a region-based paradigm significantly enhances classification performance by leveraging parietal-occipital activity. The proposed 3D-RB paradigm therefore offers an efficient and user-friendly approach for future BCI speller designs.},
}

@article {pmid42062474,
year = {2026},
author = {Jin, JY and Song, YX and Lu, JB and Li, GQ and Wang, JQ and Feng, XJ and Luo, PH and Yang, B and Xu, ZF and Yan, H and He, QJ and Yang, XC},
title = {Deficient chaperone-mediated autophagy drives multiorgan fibrogenesis via SMAD2/4 stabilization to sustain TGFβ-SMAD signaling.},
journal = {Acta pharmacologica Sinica},
volume = {},
number = {},
pages = {},
pmid = {42062474},
issn = {1745-7254},
abstract = {Fibrotic diseases, driven by excessive extracellular matrix deposition, account for substantial global morbidity and mortality, yet effective therapies remain elusive. Emerging evidence highlights impaired protein homeostasis as a key contributor to fibrosis, prompting exploration of autophagy-mediated degradation pathways. Here, we investigate the role of chaperone-mediated autophagy (CMA), a selective lysosomal degradation mechanism, in fibrosis progression. We demonstrate that CMA activity is suppressed in fibrotic tissues from experimental mice and human patients, correlating with pathological SMAD2/4 accumulation. Mechanistically, CMA deficiency impedes SMAD2/4 degradation, amplifying TGF-β signaling and collagen overproduction. AAV-mediated LAMP2A overexpression to restore CMA activity alleviated bleomycin-induced pulmonary fibrosis and carbon tetrachloride-induced hepatic fibrosis in mice. Furthermore, we identify sunitinib, an FDA-approved tyrosine kinase inhibitor, as a novel CMA activator that enhances LAMP2A transcription via targeting the transcription factor JUND, reduces SMAD2/4 levels, and mitigates fibrosis in vivo. Our findings establish CMA dysfunction as a common pathological hallmark of fibrotic diseases and unveil therapeutic strategies targeting CMA to restore protein homeostasis. This study provides critical insights into fibrosis pathogenesis and positions pharmacological CMA activation as a promising treatment avenue. CMA is impaired across fibrotic tissues, driving disease progression. Sunitinib activates CMA by targeting JUND to promote SMAD2/4 degradation, suppressing TGFβ-SMADs-fibrosis signaling. CMA, chaperone-mediated autophagy; IPF, idiopathic pulmonary fibrosis; PF, pulmonary fibrosis; HF, hepatic fibrosis.},
}

@article {pmid42051419,
year = {2026},
author = {Sun, M and Zhang, H and He, X and Wei, X and Cui, B and Huang, H and Li, H and Lin, Y and Zhang, S and Li, ZA and Shi, P and Xu, L},
title = {Ultrathin and ultrastrong hydrogel bioelectronic membranes.},
journal = {National science review},
volume = {13},
number = {8},
pages = {nwag105},
pmid = {42051419},
issn = {2053-714X},
abstract = {Hydrogels are promising materials for constructing next-generation bioelectronics because of their excellent biocompatibility and mechanical compliance. Yet, creating robust and multifunctional hydrogel devices that conform to the surface of 3D organs remains challenging. Here, we report a biomimetic strategy for engineering ultrathin and ultrastrong hydrogel membranes as an advanced platform for organ-conformal bioelectronics. In these hydrogels, self-organized nanofiber networks confer strain-stiffening characteristics with a phenomenal combination of high mechanical strength (∼13.65 MPa), fracture toughness (∼21 573 J/m[2]), and low initial stiffness (∼600 kPa), which accommodates the construction of ultrathin membranes (∼10 μm thickness) reconciling mechanical robustness and 3D conformability. Theoretical simulations reveal unique strengthening mechanisms originating from the topological reconfiguration of fibrillar joints, indicating a widely applicable principle for designing soft composites involving 3D fibrillar networks. We show that various electronic components, including conducting polymers and wafer-fabricated microelectronic sensors, can be integrated on the ultrathin hydrogel membranes, providing means for multimodal physiological sensing and stimulation. These hydrogel membranes open paths to robust, functional and biocompatible interfaces with 3D soft organs and tissues, which are useful for epidermal electronics, implantable brain-machine interfaces, peripheral nerve stimulation, and many other bioelectronic applications.},
}

@article {pmid42052286,
year = {2026},
author = {Rossi, A},
title = {Commentary: Editorial: The convergence of AI, LLMs, and industry 4.0: enhancing BCI, HMI, and neuroscience research.},
journal = {Frontiers in computational neuroscience},
volume = {20},
number = {},
pages = {1810869},
pmid = {42052286},
issn = {1662-5188},
}

@article {pmid42052812,
year = {2026},
author = {Li, R and Liu, Z and Yan, S and Zhang, L and Zhang, R and Chen, M and Hu, Y and Shi, L},
title = {Recurrent Processing Dynamics in Occluded Object Recognition Revealed by Electroencephalography and Deep Neural Networks.},
journal = {International journal of neural systems},
volume = {},
number = {},
pages = {2650035},
doi = {10.1142/S0129065726500358},
pmid = {42052812},
issn = {1793-6462},
abstract = {The human visual system excels at recognizing occluded objects, yet the temporal dynamics of recurrent processing in this task remain unclear. Using high-temporal-resolution Electroencephalography (EEG), backward masking, and deep neural networks (DNNs), we employed a two-stage paradigm to investigate recurrent processing in occluded object recognition. In Experiment 1, we manipulated occlusion levels and applied multivariate pattern analysis (MVPA) and temporal generalization analysis (TGA) to investigate the neural differences in object recognition across varying degrees of occlusion. In Experiment 2, backward masking was used to dissociate feedforward and recurrent contributions, assessed via representational similarity analysis (RSA). Results revealed a distinct shift in processing mechanisms: While low occlusion primarily relied on a rapid feedforward sweep, higher occlusion necessitated the recruitment of additional processing. Further characterization of this processing based on TGA and RSA under mask conditions revealed a two-stage recurrent process: An early stage (200-300[Formula: see text]ms) associated with low-level features, and a late stage (300-500[Formula: see text]ms) involved mid- and high-level representations, reflecting cross-hierarchical recurrent interactions. The early mask condition disrupted this coordination, highlighting the essential role of recurrent processing. These findings clarify the temporal dynamics of recurrent processing in occluded object recognition and emphasize the critical role of recurrence in achieving robust biological vision.},
}

@article {pmid42054384,
year = {2026},
author = {Huang, Y and He, Z and Ding, C},
title = {A GNN-based approach for accurate trade balance forecasting and interpretable analysis.},
journal = {PloS one},
volume = {21},
number = {4},
pages = {e0346324},
doi = {10.1371/journal.pone.0346324},
pmid = {42054384},
issn = {1932-6203},
mesh = {*Commerce ; *Neural Networks, Computer ; Forecasting/methods ; Machine Learning ; Humans ; },
abstract = {In this study, we developed a machine learning pipeline to predict trade balances across 229 countries, utilizing a Graph Neural Network (GNN), and compared it with several deep learning and regression-based models. The data preprocessing involved handling missing values, normalizing features, and conducting exploratory data analysis to uncover key patterns. Feature selection was performed using a Random Forest Regressor to identify the most influential predictors of trade balances. We then evaluated multiple models, including a complex Deep Neural Network (DNN), Transformer with multi-head attention, Random Forest, and a hybrid ensemble model, using various regression metrics. Among these, the GNN proved to be the most effective model, achieving an MSE of 0.06, RMSE of 0.26, MAE of 0.18, and an R[2] of 0.91. These results demonstrate that GNN outperforms other models in terms of accuracy, robustness, and consistency in predicting trade balances. We compared models across several key evaluation metrics and conducted a detailed comparison of residual plots to assess prediction quality and error distribution. Residual plots and ROC curves were used to validate the reliability and performance of the GNN and other models, ensuring robust and accurate predictions across the board. This study highlights the potential of machine learning techniques to improve trade balance forecasting, providing policymakers and economists with a more adaptable and precise tool for navigating complex global trade dynamics. The findings contribute to more informed economic strategies and enhanced forecasting methodologies.},
}

*Commerce
*Neural Networks, Computer
Forecasting/methods
Machine Learning
Humans

@article {pmid42054886,
year = {2026},
author = {Xu, Z and Zhang, Y and Li, B and Zhou, G and Lu, X and Lukasiewicz, T},
title = {Advancing federated semi-supervised medical image segmentation: A duo of interactive denoising pseudo-labels and convolutional contrastive learning.},
journal = {Medical image analysis},
volume = {112},
number = {},
pages = {104091},
doi = {10.1016/j.media.2026.104091},
pmid = {42054886},
issn = {1361-8423},
abstract = {Many existing studies on federated learning (FL) for segmentation primarily assume that all client data are labeled. However, in reality, due to the high cost of hospital construction and the scarcity of expert annotators, many medical sites can only provide unlabeled data. Therefore, in our work, we focus on a more practical and challenging problem, namely federated semi-supervised segmentation (FSSS), where only a subset of clients possesses labeled data while the remaining clients contribute unlabeled data. To tackle this problem, we propose an effective and generalizable FSSS framework. Specifically, labeled clients are first aggregated to construct a label-based aggregation model, which serves to guide the pseudo-label generation for unlabeled clients. Since the generated initial pseudo-labels often suffer from feature offset, we develop a pixel-level denoising method based on uncertainty feature map estimation, which enhances the quality of pseudo-labels by leveraging local data. Second, we design a model-convolutional contrastive learning to endow unlabeled clients with enhanced feature discrimination capabilities, thereby correcting their inaccurate representations. Finally, an effective dynamic model aggregation method is devised to adjust the aggregation weight of each client by considering the contribution quantified via a one-hot scheme. We comprehensively evaluate our method from multiple perspectives on three non-independent and identically distributed (Non-IID) segmentation tasks, and the experimental results confirm the effectiveness of our method. The codes of this work has been released at the following link: https://github.com/ZhenghuaXu/FedDPCon.},
}

@article {pmid42055147,
year = {2026},
author = {Nishi, A and Yanagisawa, T and Fukuma, R and Yamamoto, S and Tani, N and Kishima, H},
title = {Decreased gamma band power and increased betagamma phaseamplitude coupling are characteristic of brain activity in patients with chronic spinal cord injury.},
journal = {Brain research bulletin},
volume = {},
number = {},
pages = {111899},
doi = {10.1016/j.brainresbull.2026.111899},
pmid = {42055147},
issn = {1873-2747},
abstract = {Neurophysiological biomarkers are needed to characterize the condition of patients with spinal cord injury (SCI), for which effective symptomatic biomarkers are lacking. We recorded the resting-state magnetoencephalography data of 22 patients with SCI and 29 healthy controls. Power spectral density and phase-amplitude coupling (PAC) were assessed for six frequency bands using source-reconstructed cortical currents. Compared with controls, SCI patients exhibited significantly reduced gamma band power and increased beta-gamma PAC in the frontal cortex, including the primary motor area (q < 0.05, FDR corrected). No significant differences were observed in alpha or beta power. These results suggest that decreased gamma power and increased beta-gamma coupling reflect altered cortical dynamics after SCI and may serve as potential neurophysiological signatures for chronic cortical adaptation.},
}

@article {pmid42055968,
year = {2026},
author = {Yu, X and He, X and Huang, B and Li, G and Yu, X},
title = {Brain-Controlled Wheeled Mobile Robots: A Shared Control Framework Integrating Event-Triggered Mechanism and Deep Reinforcement Learning.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2026.3689068},
pmid = {42055968},
issn = {1558-0210},
abstract = {This study addresses the problem of quantifying user control authority in brain-computer shared control by integrating Event-Triggered Control (ETC) with Deep Reinforcement Learning (DRL). Firstly, an ETC-based brain-computer shared-control framework is developed for a wheeled mobile robot (WMR). In this framework, the Steady-State Visual Evoked Potential brain-computer interface (SSVEP-BCI) directly controls the WMR during non-triggered intervals, while control is transferred to a model predictive controller (MPC) once an event is triggered. Secondly, to overcome the limited adaptability of the Fixed Threshold (FT) triggering mechanism, a DRL-based adaptive triggering strategy is introduced to replace manually designed threshold rules. A grouped training strategy is further adopted to account for inter-subject differences in SSVEP-BCI decoding reliability during DRL training. Finally, experimental results demonstrate that integrating ETC into the SSVEP-BCI shared-control system improves the path-tracking performance of brain-controlled WMRs while enabling explicit quantification of user control authority. Specifically, compared with the FT-based strategy, the proposed DRL-based method achieves comparable lateral tracking performance, reduces heading error by 32.34%, and lowers intrusion rate by 57.85%. In addition, compared with the Time-Triggered Shared Control baseline, the cumulative execution time is reduced by 82.38%. These results indicate that the proposed framework achieves a favorable trade-off among tracking performance, computational cost, and preservation of user control authority.},
}

@article {pmid41996427,
year = {2026},
author = {Campbell, E and Eddy, E and Scheme, E},
title = {Transformer-Based Context-Informed Incremental Learning With sDTW Alignment Unlocks Fast and Precise Regression-Based Myoelectric Control.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {34},
number = {},
pages = {2118-2129},
doi = {10.1109/TNSRE.2026.3685074},
pmid = {41996427},
issn = {1558-0210},
mesh = {Humans ; *Electromyography/methods ; Male ; Adult ; Female ; Algorithms ; Young Adult ; Neural Networks, Computer ; Regression Analysis ; User-Computer Interface ; *Machine Learning ; Calibration ; Brain-Computer Interfaces ; },
abstract = {Regression-based myoelectric interfaces hold the promise of enabling intuitive proportional and simultaneous control but remain limited by calibration sensitivity, unpredictable dynamics, and inconsistent user behaviours. Temporal neural architectures have the potential to substantially improve these controllers by capturing the temporal structure of user behaviours, provided they are trained using dynamics that are sufficiently representative of closed-loop use. Context-informed incremental learning (CIIL) offers a mechanism for acquiring such data online; however, its reliance on environment-derived pseudo-labels makes it vulnerable to temporal deviations between assumed and true user intent. This study introduces T-sDTW-CIIL, a transformer-based incremental learning framework that integrates temporal modelling, closed-loop learning, and soft dynamic time warping (sDTW) to enable tolerant label alignment. Twelve participants completed an adaptive regression-based cursor-control task using four pipelines: static and CIIL variants of both MLP and transformer models. T-sDTW-CIIL achieved significantly higher success rates, throughputs, efficiencies, and simultaneity gains when evaluated in a high precision ISO-Fitts' environment. T-sDTW-CIIL achieved throughputs of $2.0\times $ , $2.4\times $ , and $3.7\times $ those of an MLP trained using conventional screen-guided training when acquiring large, medium, and small targets, respectively. Perhaps more importantly, it maintained success rates of 98.4% for small targets, whereas the static MLP degraded to only 23.4% success. T-sDTW-CIIL-based adaptation also reduced overall contraction intensities by ~10%. These results demonstrate the powerful combination of temporal learning with context-informed co-adaptation. T-sDTW-CIIL overcomes key limitations of existing regression-based myoelectric controllers, enabling robust, low-intensity human-computer interaction.},
}

Humans
*Electromyography/methods
Male
Adult
Female
Algorithms
Young Adult
Neural Networks, Computer
Regression Analysis
User-Computer Interface
*Machine Learning
Calibration
Brain-Computer Interfaces

@article {pmid42046874,
year = {2026},
author = {Kong, L and Yang, Y and Zhou, W and Hu, S},
title = {Sporadic Alzheimer's disease with bipolar-like features: a case report and a brief review of the current research status.},
journal = {Journal of Zhejiang University. Science. B},
volume = {27},
number = {4},
pages = {416-425},
doi = {10.1631/jzus.B2500307},
pmid = {42046874},
issn = {1862-1783},
mesh = {*Alzheimer Disease/pathology/diagnosis ; Humans ; Amyloid beta-Peptides/metabolism ; Female ; tau Proteins/metabolism ; Aged ; Male ; Oxidative Stress ; Brain/pathology ; },
abstract = {Alzheimer's disease (AD) is among the main causes of cognitive impairment, memory loss, and dementia, particularly in old adults. It has been listed as one of the most expensive, lethal, and burdening diseases of the 21st century and develops with the process of aging worldwide (Scheltens et al., 2021). Currently, it is widely acknowledged that the typical pathogenesis of AD involves the deposition of amyloid-β (Aβ) and Tau proteins in the cerebral parenchyma and vasculature, intraneuronal neurofibrillary tangles, and the gradual degeneration of synapses (Scheltens et al., 2016; Rostagno, 2022). According to several hypotheses, abnormalities and dysfunctions in vascular structure, mitochondrial metabolism, oxidative stress, glucose utilization, and neuroinflammation are considered fundamental for AD pathology (Scheltens et al., 2016).},
}

*Alzheimer Disease/pathology/diagnosis
Humans
Amyloid beta-Peptides/metabolism
Female
tau Proteins/metabolism
Aged
Male
Oxidative Stress
Brain/pathology

@article {pmid42048458,
year = {2026},
author = {Zhu, R and Hu, Z and Lou, Z and Xie, F and Zhao, S and Jiao, X and Wang, J and Fukuda, K and Chen, X and Hu, W and Cheng, HM and Li, X and Someya, T and Xu, X},
title = {An exceptionally conductive hydrogel for all-organic, ultraflexible, and chronic neural interfaces.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {123},
number = {18},
pages = {e2532840123},
doi = {10.1073/pnas.2532840123},
pmid = {42048458},
issn = {1091-6490},
support = {2023YFE0101400//Ministry of Science and Technology of the People's Republic of China (MOST)/ ; JPMJCR21P2//JST, CREST/ ; 52273249//National Natural Science Foundation of China/ ; 2021ZT09L197//GDSTC | Guangdong Provincial Introduction of Innovative Research and Development Team (Guangdong Innovative Research Team Program)/ ; KQTD20210811090112002//| Natural Science Foundation of Shenzhen Municipality (Shenzhen Natural Science Foundation)/ ; 2308085MA19//Anhui Provincial Natural Science Foundation/ ; },
mesh = {*Hydrogels/chemistry ; Electric Conductivity ; *Brain-Computer Interfaces ; Animals ; *Electrocorticography/methods/instrumentation ; Electrodes, Implanted ; },
abstract = {Chronic neural interfaces are essential for advancing brain-computer interfaces, neuroprosthetics, and neuromodulation technologies. However, a long-standing trade-off between performance and longevity persists due to the scarcity of materials that simultaneously achieve superior electrical performance, mechanical compliance, and biocompatibility. Here, we overcome this limitation with an all-organic, ultraflexible electrocorticography (ECoG) design that features a thickness of only 9 µm, achieving low electrode-tissue impedance and durability in vivo. Central to this design is a conductive hydrogel featuring an interfacial percolation (CHIP) microstructure, with tunable hydration levels and softness, achieving a highest in-plane electrical conductivity of 2,512 S cm[-1]. We further developed an in-plane swelling control with a dry, soft-protective etching strategy that preserves the structural integrity during hydrogel processing. The resulting all-organic ECoG array conforms to the cortical surface, minimizing foreign body response and providing exceptional signal quality, with the longest record up to 550 d.},
}

*Hydrogels/chemistry
Electric Conductivity
*Brain-Computer Interfaces
Animals
*Electrocorticography/methods/instrumentation
Electrodes, Implanted

@article {pmid42049850,
year = {2026},
author = {Xu, G and Yu, C and Shao, G and Pan, G and Wang, Y and Hao, Y},
title = {Low-power differencing feature extracts spiking-band activities for high-performance intracortical brain-computer interfaces.},
journal = {Communications biology},
volume = {},
number = {},
pages = {},
doi = {10.1038/s42003-026-10144-9},
pmid = {42049850},
issn = {2399-3642},
abstract = {Intracortical brain-computer interfaces (iBCIs) demand computationally efficient feature extraction methods to process high-bandwidth neural signals in resource-constrained implantable systems. We present the mean absolute of n-th difference (MAND), a feature extraction technique that utilizes optimized differencing operations to isolate spiking-band activity with minimal computational overhead. Through theoretical and empirical validation across multiple datasets encompassing human handwriting, primate reaching/grasping, and rodent cognitive tasks, MAND demonstrated superior performance compared to state-of-the-art features, significantly reducing velocity reconstruction error and improving classification accuracy. An extended MAND variant, incorporating a weighted sum of dual-differencing, achieved additional performance gains through enhanced spectral alignment with neural spiking activity. Hardware implementation on FPGA/MCU platforms confirmed MAND's exceptional efficiency - processing 10-second neural recordings in just 6 ms while consuming only 3 mW of power - representing orders-of-magnitude improvements in both speed and energy efficiency compared to conventional methods. These results establish MAND as a breakthrough solution that enables superior decoding performance with exceptional computational efficiency, paving the way for next-generation, fully implantable iBCI systems.},
}

@article {pmid42049861,
year = {2026},
author = {Tao, X and Pu, Y and Kong, XZ},
title = {Rebalancing psychology in China.},
journal = {Communications psychology},
volume = {4},
number = {1},
pages = {},
pmid = {42049861},
issn = {2731-9121},
}

@article {pmid42050355,
year = {2026},
author = {Zhao, X and Lin, Z and Zhang, H and Zhang, W and Liu, Z and Chen, C and Ji, H and Hu, S and Xu, X},
title = {Longitudinal associations of cardiovascular-kidney-metabolic syndrome with midlife or late-life mental disorders and dementia, and the mediating role of metabolomic signature.},
journal = {Communications medicine},
volume = {},
number = {},
pages = {},
doi = {10.1038/s43856-026-01608-4},
pmid = {42050355},
issn = {2730-664X},
abstract = {BACKGROUND: Cardiovascular-Kidney-Metabolic (CKM) syndrome assesses the interconnections among metabolic, kidney, and cardiovascular diseases, rendering significant prognostic value for age-related chronic diseases and mortality. We aimed to investigate the effects of CKM syndrome on transitions between healthy status, mental disorders, and dementia and evaluate the potential mediating role of a CKM-related metabolomic signature in these associations.

METHODS: This prospective longitudinal study used UK Biobank data from 375,203 midlife and older adults at baseline and 188,018 with metabolomic information. CKM was staged from 0 to 4. Mental disorders and dementia were identified via ICD-10. Multi-state models analyzed the impact of CKM on transitions from healthy status to mental disorders and dementia. Competing risk (death) models assessed the associations of CKM with specific mental disorders and dementia. Mediation role of CKM-related metabolomic signature was evaluated.

RESULTS: We show that per-stage CKM increase elevates hazards of transitioning from healthy to mental disorders (HR = 1.24[1.22-1.26]) and subsequently to dementia (HR = 1.38[1.21-1.58]), or directly to dementia (HR = 1.27[1.21-1.33]). Worsening CKM stages are associated with bipolar, depressive, and anxiety disorders; whilst only advanced stages (3/4) associated with all dementia types. The CKM metabolomic signature mediates 34.9% and 8.1% of associations of CKM with pre-dementia mental disorders and dementia, respectively.

CONCLUSIONS: CKM syndrome is associated with pre-dementia mental disorders and dementia, emphasizing the need for regular monitoring and early intervention to manage CKM progression and reduce geriatric neuropsychiatric disturbances.},
}

@article {pmid42037083,
year = {2026},
author = {Eilts, H and Ivucic, G and Koenen, N and Wright, MN and Schultz, T and Putze, F},
title = {Explainable AI Insights Into EEG Classification and Its Alignment to Neural Correlates.},
journal = {Human brain mapping},
volume = {47},
number = {6},
pages = {e70528},
doi = {10.1002/hbm.70528},
pmid = {42037083},
issn = {1097-0193},
support = {459360854//Deutsche Forschungsgemeinschaft/ ; 447089431//Deutsche Forschungsgemeinschaft/ ; },
mesh = {Humans ; *Electroencephalography/methods/classification ; *Deep Learning ; *Neural Networks, Computer ; Adult ; *Attention/physiology ; *Imagination/physiology ; *Cerebral Cortex/physiology ; Male ; Young Adult ; Female ; },
abstract = {While deep learning has drastically improved the performance of electroencephalography (EEG) analysis, it remains unclear what these models, such as EEGNet, learn from the data and how their learned features relate to neuroscientific concepts. In this work, we introduce a comprehensive interpretability framework for deep learning models of neural data based on Concept Relevance Propagation (CRP), an extension of layer-wise relevance propagation that enables the analysis of abstract concepts encoded by individual neurons and filters. We apply CRP to individual filters of convolutional neural networks (EEGNet) trained using leave-one-out cross-validation. To identify common classification strategies across models, we guide the selection of representative data for individual filters using relevance maximization, reduce dimensionality via UMAP, and identify clusters of filters encoding similar concepts through density-based clustering. To gain insight into the neural correlates of these tasks, we analyze the learned features across multiple data domains without requiring model retraining. We integrate a virtual inspection layer to project explanations into the frequency domain, enabling the simultaneous analysis of spatial, temporal, and spectral aspects using topographic maps, functional grouping, and independent component analysis (ICA). Using three EEG classification tasks-auditory attention, internal/external attention, and motor imagery-we demonstrate that our approach reveals interpretable, task-relevant neural patterns that generalize across participants. Overall, this framework provides a step toward understanding the models itself and gaining insights into the tasks in terms of neuroscience.},
}

Humans
*Electroencephalography/methods/classification
*Deep Learning
*Neural Networks, Computer
Adult
*Attention/physiology
*Imagination/physiology
*Cerebral Cortex/physiology
Male
Young Adult
Female

@article {pmid42037346,
year = {2026},
author = {Xue, Y and Li, Z and Wang, F and Zhao, L and Li, T and Gong, A and Nan, W and Fu, Y},
title = {[Ethical risks and regulatory considerations in neurofeedback technology].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {43},
number = {2},
pages = {414-420},
doi = {10.7507/1001-5515.202507052},
pmid = {42037346},
issn = {1001-5515},
mesh = {Humans ; *Neurofeedback/ethics ; Informed Consent/ethics ; Brain/physiology ; Personal Autonomy ; },
abstract = {Neurofeedback transforms real-time brain activity features into multimodal feedback to guide self-regulation of brain function, showing potential applications in neuropsychiatric treatment and cognitive enhancement. However, its use entails ethical risks including cognitive autonomy, personal identity integrity, safety and efficacy, privacy protection, and the safeguarding of vulnerable populations, with informed consent challenges being particularly pronounced in implicit neurofeedback. Based on these risks, this paper proposes establishing an ethical evaluation framework for neurofeedback, promoting ethics-embedded design, and strengthening international cooperation and public education, emphasizing responsible innovation to align technological development with ethical safeguards.},
}

Humans
*Neurofeedback/ethics
Informed Consent/ethics
Brain/physiology
Personal Autonomy

@article {pmid42037366,
year = {2026},
author = {Perdigão, B and Chang, B and Anand, GAE and Tao, L and Kim, K and Eriksen, AZ and Schönhoff, MA and Ferreira, G and Liu, X and Genelioglu, S and Lyngholm-Kjærby, J and Zahoor, N and Lind, JU and Fanta, ABDS and Hansen, TW and Cai, C and Han, A},
title = {Solid Ethanol as a Renewable, Low-Toxicity, Electron-Beam Direct Write, and Biomedical Material.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {},
number = {},
pages = {e75341},
doi = {10.1002/advs.75341},
pmid = {42037366},
issn = {2198-3844},
support = {R345-2020-1440//Lundbeck Foundation/ ; NNF24OC0092323//Novo Nordisk Foundation/ ; NNF20OC0064289//Novo Nordisk Foundation/ ; NNF24OC0095407//Novo Nordisk Foundation/ ; 1133-00016B//Danish Medical Research Council/ ; },
abstract = {3D ice lithography (3DIL) is an emerging direct-write technique that fabricates intricate 3D structures using frozen precursors. Here, we report the use of ethanol as a renewable and low-toxicity precursor for 3DIL, intended for the first time for the fabrication of intricate porous microstructures for in vitro and in vivo biomedical applications. The first nanoindentation analysis of 3DIL materials reveals mechanical properties (Young's modulus 2-4 GPa) comparable to biocompatible polymers. TEM shows that the material is an amorphous carbon that undergoes controlled graphitization under annealing at very high temperatures (1300°C). Due to its chemical composition, mechanical properties, and stability in water, cross-linked ethanol scaffolds support in vitro endothelial cell adhesion and proliferation with high confluency. Patterned neurostimulation electrodes implanted in mouse brains elicit no significant increase in astrocytic or microglial activation, indicating excellent in vivo biocompatibility. Additionally, we present for the first time the use of optically transparent substrates and the first patterning of neurostimulation electrodes using 3DIL. This study positions 3DIL using ethanol as a versatile, direct-write technique using renewable precursors to produce novel microdevices in biomedical engineering.},
}

@article {pmid42037370,
year = {2026},
author = {Xing, Y and Yang, Y and Hong, Z and Tian, C and Chu, H and Prokop, SC and Cai, H and Gu, M and Tchieu, J and Mackie, K and Guo, F},
title = {Organoid Brain-Machine-Interface Devices for Central Nervous System Repair.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {},
number = {},
pages = {e75444},
doi = {10.1002/advs.75444},
pmid = {42037370},
issn = {2198-3844},
support = {U54AG090792//National Institute of Health Awards/ ; R01GM160423//National Institute of Health Awards/ ; U01DA056242//National Institute of Health Awards/ ; EFRI2422149//National Science Foundation Award/ ; },
abstract = {Central nervous system (CNS) repair and regeneration suffer from tremendous clinical challenges due to current limitations in replacing lost neural tissues and restoring long-term neural circuits. Neural organoids, 3D lab-cultured neural tissues derived from stem cells, can recapitulate key cellular, structural, and physiological features of the human CNS, showing promising potential for neural regeneration. Here, we envision organoid brain-machine-interface (Organoid-BMI) devices as a new kind of neuroelectrical interface for CNS repair. The Organoid-BMI devices employ neural organoids and bioelectrodes as biohybrid bidirectional communication pathways to connect the human CNS and the external world. Acting as a biologically compatible intermediate, this approach may facilitate structural incorporation and functional alignment with host neural circuits for addressing persistent challenges of CNS repair including graft-host mismatch and long-term circuit stability. Through implementing adaptive and closed-loop strategies, this approach can modulate interaction and functional communication with the host for promoting CNS circuit remodeling and functional recovery. Together, this innovative technology may open new avenues for personalized regenerative medicine.},
}

@article {pmid42037657,
year = {2026},
author = {Aabedi, A and Mashiach, D and Fraix, MP and Agrawal, DK},
title = {Most Effective Interventions for Improving Upper Extremity Function in Patients with Hemiparesis.},
journal = {Cardiology and cardiovascular medicine},
volume = {9},
number = {6},
pages = {504-511},
pmid = {42037657},
issn = {2572-9292},
abstract = {Hemiparesis, commonly resulting from stroke, leads to significant impairments in upper extremity function, limiting daily activities and reducing quality of life. Effective rehabilitation strategies are essential to enhance motor recovery and restore functional independence. This review evaluates the most effective interventions for improving upper extremity function in patients with hemiparesis. A comprehensive literature review was conducted, analyzing systematic reviews, randomized controlled trials, and clinical guidelines. The efficacy of various interventions, including task-specific training, constraint-induced movement therapy (CIMT), neuromuscular electrical stimulation (NMES), mirror therapy, virtual reality, bilateral arm training, pharmacological approaches, and robotic-assisted rehabilitation, was assessed based on their impact on motor function and daily activities. The review highlights the role of neuroplasticity in motor recovery, emphasizing interventions that promote cortical reorganization. Task-specific training, CIMT, and NMES demonstrate strong evidence in enhancing motor function. Emerging technologies, such as brain-computer interfaces and robotics, show promise in optimizing rehabilitation outcomes. Factors influencing recovery, including stroke severity, time since onset, and patient motivation, are discussed. Studies consistently support the effectiveness of CIMT and task-specific training in improving upper extremity function. NMES and mirror therapy are beneficial adjunct therapies, particularly for patients with moderate impairment. Virtual reality and robotics enhance engagement and motor learning, while pharmacological and stem cell therapies are emerging areas with potential but require further research. A multimodal rehabilitation approach combining task-oriented therapies, neuromodulation, and emerging technologies yields the best outcomes for upper extremity recovery in hemiparesis patients. Future research should focus on optimizing individualized treatment plans and integrating novel therapeutic modalities to maximize functional gains.},
}

@article {pmid42039372,
year = {2026},
author = {Zhao, P and Liang, T and Jia, H and Dayan, A and Dinarès-Ferran, J and Solé-Casals, J},
title = {MCFANet: a multi-class fusion attention network for motor imagery EEG classification.},
journal = {Frontiers in human neuroscience},
volume = {20},
number = {},
pages = {1811759},
pmid = {42039372},
issn = {1662-5161},
abstract = {INTRODUCTION: This paper proposes a Multi-Class Fusion Attention Network (MCFANet) that combines the multi-class spatial filtering outputs of FBCSP with the spatiotemporal feature extraction capability of convolutional neural networks for multi-class motor imagery EEG classification. In multi-class motor imagery decoding, traditional spatial filtering methods extract effective discriminative spatial features but decompose the task into independent binary subproblems, and typically retain only energy statistics while discarding temporal dynamics. Deep learning methods can learn spatiotemporal features but must learn spatial patterns from the beginning, making it difficult to fully capture established neurophysiological priors under limited training samples.

METHODS: MCFANet concatenates the spatial filtering outputs from all classes and sub-bands along the channel dimension to construct a virtual channel representation containing the discriminative responses of all classes. The full time series is preserved and fed into a convolutional module for spatiotemporal feature extraction, and a channel attention module adaptively reweights the feature maps to focus on the most discriminative representations. Four-class classification experiments were conducted on two public datasets.

RESULTS: On Dataset 2a, MCFANet achieved an accuracy of 67.94% ±13.70, outperforming FBEEGNet (63.98%) and EEGNet (58.79%). On the High Gamma Dataset, MCFANet achieved 87.10% ±10.09, improving over FBEEGNet by approximately 2.5 percentage points. Paired t-tests and effect size analysis confirm that the improvements over the main baseline methods are statistically significant.

DISCUSSION: The results suggest that reorganizing multi-class spatial discriminative responses into a unified representation that preserves temporal dynamics provides an effective path for bridging traditional spatial filtering and deep learning.},
}

@article {pmid42039954,
year = {2026},
author = {Yıldırım, Y and Ertaş, E},
title = {Surgical outcomes and the role of probe exit site in nasal endoscopy-guided interventions for congenital nasolacrimal duct obstruction: a cross-sectional study.},
journal = {International journal of ophthalmology},
volume = {19},
number = {5},
pages = {901-908},
pmid = {42039954},
issn = {2222-3959},
abstract = {AIM: To evaluate the clinical presentation, nasal endoscopic findings, and surgical outcomes of probing surgery (PS) or bicanalicular silicone tube intubation (BCI) performed under nasal endoscopic guidance (NEG) in pediatric patients with congenital nasolacrimal duct obstruction (CNLDO), regardless of previous surgical history.

METHODS: This retrospective cross-sectional study included CNLDO patients with data on demographics, fluorescein dye disappearance test (FDDT) results, dacryoscintigraphy findings, prior surgeries, and outcomes of NEG-PS or NEG-BCI. NEG-BCI using Crawford stents was performed intraoperatively in complex cases. Intraoperative and postoperative complications were recorded. Surgical success was evaluated clinically and with FDDT at postoperative months 1 and 6. Stents were retained for a minimum of 12wk, with follow-up for at least 6mo after removal.

RESULTS: Of the 67 pediatric patients (67 eyes, mean age 37.4±17.5mo), 44 (65.7%) were female. Preoperative FDDT was graded 3+ in 85.1% of cases, and dacryoscintigraphy confirmed obstruction in 92.5%. Nine patients (13.4%) had a history of PS. At 6mo, surgical success was achieved in 96.6% (28/29) of the NEG-PS group and 71.1% (27/38) of the NEG-BCI group (P=0.009). All cases with probe exit through the inferior meatus (IM) were successful, whereas exits through the inferior concha (IC) or submucosal IM (SM) were significantly associated with failure (P<0.001).

CONCLUSION: NEG allows intraoperative classification of CNLDO and selection of surgical method based on real-time anatomical findings. Probe exit through the IM predicts a high likelihood of success, whereas IC or SM exits are risk factors for failure. Incorporating NEG into routine practice may improve surgical precision and reduce the need for repeated procedures.},
}

@article {pmid42040188,
year = {2026},
author = {Wang, N and Chai, X and He, Y and Song, J and Cao, T and He, Q and Zhu, S and Jia, Y and Si, J and Yang, Y and Zhao, J},
title = {Graph-Theoretical Signature from Neural and Vascular Signals Reveals Spinal Cord Stimulation Frequency-Specific Brain Network in Disorders of Consciousness Patients.},
journal = {Cyborg and bionic systems (Washington, D.C.)},
volume = {7},
number = {},
pages = {0539},
pmid = {42040188},
issn = {2692-7632},
abstract = {Introdution: Spinal cord stimulation (SCS) has emerged as a promising neuromodulatory intervention for patients with disorders of consciousness (DoC). However, the identification of optimal stimulation frequencies remains a subject of ongoing debate. Although previous electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) studies have suggested the therapeutic efficacy of 5- and 70-Hz, respectively, the integrative neurovascular mechanisms and frequency-specific network dynamics underlying these effects remain to be elucidated. Objective and Impact Statement: This study aims to characterize frequency-dependent network reconfiguration in DoC using simultaneous EEG-fNIRS recordings and graph theoretical analysis. By delineating distinct neurophysiological and hemodynamic signatures, our findings establish a mechanistic framework for the optimization of SCS parameters, thereby advancing personalized neuromodulation strategies for the promotion of consciousness recovery. Methods: This prospective trial used simultaneous EEG-fNIRS and graph theory in 16 patients with DoC undergoing multifrequency SCS at 5, 20, 70, and 100 Hz to decode frequency-specific network dynamics. Our integrated EEG-fNIRS analysis revealed 3 principal advances. First, multimodal cortical mapping via a unified anatomical atlas quantified frequency-dependent network reconfiguration, generating graph-theoretical metrics (global and nodal efficiency, characteristic path length, and clustering coefficients) from source-localized EEG (delta-gamma bands) and fNIRS (oxyhemoglobin and deoxygenated) data. Second, we identified frequency-dependent neurophysiological profiles. Results: Five-hertz stimulation produced acute enhancement of theta-band global network efficiency coupled with elevated gamma-band nodal efficiency in the right cingulate motor area, indicating immediate frontolimbic engagement. Conversely, 70-Hz stimulation selectively evoked delayed hemodynamic responses in the visual cortices and increased occipital hemoglobin oxygenation without concomitant EEG alterations, suggesting preferential retinotopic pathway recruitment. Conclusion: Multimodal EEG-fNIRS analysis elucidates frequency-specific SCS mechanisms, where 5-Hz stimulation optimizes local information integration through theta and gamma modulation, while 70-Hz enhances long-range connectivity, exposing frequency-specific neural plasticity mechanisms.},
}

@article {pmid42041774,
year = {2026},
author = {Wang, Z and Ma, Y and Du, Y and She, Q},
title = {A Band-Aware Riemannian Network with Domain Adaptation for Motor Imagery EEG Signal Decoding.},
journal = {Brain sciences},
volume = {16},
number = {4},
pages = {},
doi = {10.3390/brainsci16040363},
pmid = {42041774},
issn = {2076-3425},
support = {LZ26F010007//Zhejiang Provincial Natural Science Foundation/ ; 62371172//National Natural Science Foundation of China/ ; 2025ZY01045//Central Government-Guided Local Science and Technology Development Fund/ ; 2025E10015//Zhejiang Provincial Key Laboratory of Brain Computer Collaborative Intelligence Technology and Applications/ ; },
abstract = {BACKGROUND: The decoding of motor imagery electroencephalography (MI-EEG) is constrained by core issues including low signal-to-noise ratio (SNR) and cross-session as well as cross-subject domain shift, which seriously impedes the practical deployment of brain-computer interfaces (BCIs).

METHODS: To address these challenges, this paper proposes a novel end-to-end MI-EEG decoding method named BARN-DA. Two innovative modules, Band-Aware Channel Attention (BACA) and Multi-Scale Kernel Perception (MSKP), are designed: one enhances discriminative channel features by modeling channel information fused with frequency band feature representation, and the other captures complex data correlations via multi-scale parallel convolutions to improve the discriminability of the network's feature extraction. Subsequently, the features are mapped onto the Riemannian manifold. For the source and target domain features residing on this manifold, a Riemannian Maximum Mean Discrepancy (R-MMD) loss is designed based on the log-Euclidean metric. This approach enables the effective embedding of Symmetric Positive Definite (SPD) matrices into the Reproducing Kernel Hilbert Space (RKHS), thereby reducing cross-domain discrepancies.

RESULTS: Experimental results on four public datasets demonstrate that the BARN-DA method achieves average cross-session classification accuracies of 84.65% ± 8.97% (BCIC IV 2a), 89.19% ± 7.69% (BCIC IV 2b), and 61.76% ± 12.68% (SHU), as well as average cross-subject classification accuracies of 65.49% ± 11.64% (BCIC IV 2a), 78.78% ± 8.44% (BCIC IV 2b), and 78.14% ± 14.41% (BCIC III 4a). Compared with state-of-the-art methods, BARN-DA obtains higher classification accuracy and stronger cross-session and cross-subject generalization ability.

CONCLUSIONS: These results confirm that BARN-DA effectively alleviates low SNR and domain shift problems in MI-EEG decoding, providing an efficient technical solution for practical BCI systems.},
}

@article {pmid42041797,
year = {2026},
author = {Zhang, H and Tao, C},
title = {Deep Learning Decoding of Steady-State Visual Evoked Potential (SSVEP) for Real-Time Mobile Brain-Computer Interfaces: A Narrative Review from Laboratory Settings to Lightweight Engineering Applications.},
journal = {Brain sciences},
volume = {16},
number = {4},
pages = {},
doi = {10.3390/brainsci16040387},
pmid = {42041797},
issn = {2076-3425},
support = {QY25232//National Natural Science Foundation of China/ ; },
abstract = {Background/Objectives: SSVEP-BCI has broad application potential in mobile human-computer interaction due to its high information transfer rate and stable signal characteristics. The introduction of deep learning technology has significantly advanced SSVEP decoding performance, offering novel approaches for processing short-duration signals and tackling complex classification tasks. The establishment of the Tsinghua Benchmark dataset provides a standardized benchmark for evaluating algorithm performance, accelerating the development of deep learning-based SSVEP decoding. However, a summary of SSVEP deep learning decoding technologies for real-time mobile applications is lacking. Methods: We conducted a comprehensive literature review of SSVEP deep learning decoding studies published since 2023, using the Tsinghua Benchmark dataset. This review focuses on technical developments targeting real-time performance, low computational complexity, and high robustness. Results: We summarize the key technologies developed for real-time mobile SSVEP decoding. Our analysis thoroughly examines how these techniques address core challenges in the engineering implementation of mobile brain-computer interfaces, including real-time processing requirements, resource constraints, and environmental robustness. Conclusions: This review provides a comprehensive overview of SSVEP deep learning decoding technologies for mobile applications, establishing a technical foundation to advance mobile brain-computer interfaces from laboratory settings to practical deployment.},
}

@article {pmid42041805,
year = {2026},
author = {Christodoulides, P and Peschos, D and Zakopoulou, V},
title = {The Use of EEG in the Study of Emotional States and Visual Word Recognition with or Without Musical Stimulus in University Students with Dyslexia.},
journal = {Brain sciences},
volume = {16},
number = {4},
pages = {},
doi = {10.3390/brainsci16040396},
pmid = {42041805},
issn = {2076-3425},
abstract = {This study investigated neural oscillatory dynamics underlying visual word recognition in university students with dyslexia using a portable brain-computer interface (BCI) EEG system. The sample included university students with dyslexia (N = 12) and matched controls (N = 14) who completed auditory discrimination and visual word recognition tasks, with and without musical accompaniment. Through these experimental conditions, the researchers assessed (a) the cortical activation across frequency bands, (b) the modulatory effect of background music, and (c) the relationship between emotional states and brain activity. Results revealed significant group differences in oscillatory patterns, with reduced β- and γ-band activity in the left occipito-temporal cortex among participants with dyslexia, confirming disrupted temporal coordination in posterior reading networks. Compensatory right-hemisphere activation was observed, particularly under musical conditions, accompanied by increased α-band power and reduced δ activity, indicating enhanced attentional engagement and reduced cognitive fatigue. Emotional assessment using the DASS-21 revealed higher stress and anxiety scores in the dyslexic group, suggesting that affective factors may modulate oscillatory dynamics. The presence of background music appeared to attenuate these effects, supporting improved emotional regulation and cognitive focus. These findings demonstrate that dyslexia reflects a distributed disruption in neural synchrony and cross-frequency coupling, influenced by both cognitive and affective mechanisms. The integration of portable EEG technology with rhythmic auditory stimulation offers new insights into the neurophysiological and emotional aspects of dyslexia, highlighting the potential of rhythm- and music-based approaches for both diagnostic and therapeutic applications.},
}

@article {pmid42041832,
year = {2026},
author = {Padilla, GL and Farfán, FD},
title = {Stimulus Size Modulates Periodic and Aperiodic EEG Components in SSVEP-Based BCIs.},
journal = {Brain sciences},
volume = {16},
number = {4},
pages = {},
doi = {10.3390/brainsci16040424},
pmid = {42041832},
issn = {2076-3425},
abstract = {Background/Objectives: Steady-State Visual Evoked Potential-based Brain-Computer Interfaces face a critical trade-off between system accuracy and user visual fatigue. To address this challenge, the objective of this study was to determine how the spatial manipulation of stimulus size modulates the full spectral dynamics of the Electroencephalogram, encompassing both the periodic oscillatory response and the aperiodic (1/f) background noise. Methods: Twenty-two healthy subjects completed a sustained visual attention task using a competitive stimulus paradigm (20 Hz and 30 Hz) presented in three spatial dimensions (Small, Medium, and Big). Parieto-occipital brain signals were decomposed using the spectral parameterization algorithm (SpecParam) to extract frequency-specific visually evoked response power and the aperiodic slope, while visual fixation was continuously monitored via eyetracking. Results: Increasing stimulus size induced a statistically significant gain in the power of the attended signal (Target) without increasing the response of the peripheral distractor. Simultaneously, larger stimuli produced a significant increase in the aperiodic slope during 20 Hz attention and visual rest, suggesting increased cortical inhibition and a reduction in broadband neural activity. This aperiodic modulation was not observed at 30 Hz. Conclusions: The improvement in Signal-to-Noise Ratio with increasing stimulus size arises from a dual neurophysiological mechanism: enhancement of the periodic evoked response together with a reduction in background neural noise.},
}

@article {pmid42042585,
year = {2026},
author = {Urfy, M and Mir, MT},
title = {A Decade of Artificial Intelligence in Stroke Care (2015-2025): Trends, Clinical Translation, and the Precision Medicine Frontier-A Narrative Review.},
journal = {Journal of personalized medicine},
volume = {16},
number = {4},
pages = {},
doi = {10.3390/jpm16040218},
pmid = {42042585},
issn = {2075-4426},
abstract = {Background/Objectives: Stroke generates 157 million disability-adjusted life-years (DALYs) annually, making it the leading neurological cause of global disease burden. Artificial intelligence (AI) and machine learning (ML) have emerged as transformative technologies across the stroke care continuum. This narrative review maps the trajectory of AI in stroke medicine over the decade from 2015 to 2025. Methods: We conducted a narrative review with a structured, pre-specified search strategy across eight pre-specified thematic clusters using PubMed/MEDLINE (January 2015-December 2025), identifying 8549 records and including 1335 studies after screening. Inclusion criteria encompassed primary research articles, systematic reviews, meta-analyses, and RCTs reporting quantitative performance metrics or clinical outcome data for AI/ML in stroke. Results: Stroke imaging AI is the most commercially mature domain, with over 30 FDA-cleared tools. Automated ASPECTS scoring reduced radiologist reading time by 74.8% (AUC 84.97%; 95% CI: 83.1-86.8%). The only triage AI RCT demonstrated an 11.2 min reduction in door-to-groin time without significant improvement in 90-day functional independence (OR 1.3, 95% CI 0.42-4.0). Brain-computer interface rehabilitation showed significant upper limb recovery in a 17-center RCT (FMA-UE mean difference +3.35 points, 95% CI 1.05-5.65; p = 0.0045). AF detection AI is FDA-cleared and RCT-validated. LLMs and federated learning are pre-regulatory but growing exponentially. Conclusions: AI in stroke has achieved diagnostic maturity but therapeutic immaturity. Bridging algorithmic performance to patient outcomes, addressing equity gaps, and building the economic evidence base for scalable deployment are the defining challenges of the next decade.},
}

@article {pmid42044749,
year = {2026},
author = {Pan, X and Zhang, R and Xia, X and Cui, H and Liu, L and Chen, X},
title = {Feasibility of a Hybrid SSVEP-Motor Imagery BCI with Robotic Feedback for Upper Limb Motor Rehabilitation in Stroke Patients.},
journal = {Journal of neuroscience methods},
volume = {},
number = {},
pages = {110780},
doi = {10.1016/j.jneumeth.2026.110780},
pmid = {42044749},
issn = {1872-678X},
abstract = {BACKGROUND: Stroke remains a leading cause of long-term disability, necessitating innovative neurorehabilitation strategies to address persistent motor deficits. Traditional therapies often exhibit limited efficacy due to therapeutic plateau, highlighting the critical need for alternative rehabilitation paradigms.

NEW METHOD: This study assesses the feasibility of a novel hybrid brain-computer interface (BCI) that integrates motor imagery (MI) and steady-state visual evoked potentials (SSVEP), with robotic glove-assisted feedback used to optimize overall system performance. Thirty-two stroke patients were divided into a control group (conventional therapy) and an experimental group (conventional therapy plus BCI intervention with 10- or 20-day cycles).

RESULTS: Outcomes assessed via Fugl-Meyer Assessment (FMA) scores, electroencephalography (EEG) classification accuracy, laterality coefficients (LC), and weighted brain connectivity analysis indicated promising trends. The experimental group showed considerable improvements in FMA scores compared with the control group. The proposed BCI system successfully achieved satisfactory EEG classification accuracy (maximum value of 98.08%) and robust system operation. Furthermore, increases in EEG accuracy, normalization of laterality coefficients (LC), and reinforcement of task-specific weighted brain connectivity were observed, particularly after prolonged training.

The proposed hybrid BCI system demonstrates a potential to overcome the limitations of conventional therapies and traditional single-modality BCIs, offering a more engaging and adaptive rehabilitation approach.

CONCLUSIONS: These findings demonstrate the feasibility of the proposed hybrid BCI system and the observed improvements in motor function, neurophysiological markers, and brain connectivity underscore its promise as a novel paradigm to enhance neuroplasticity and recovery outcomes.},
}

@article {pmid42045070,
year = {2026},
author = {Canfield, RA and Ouchi, T and Fang, H and Macagno, B and Smith, LI and Scholl, LR and Orsborn, AL},
title = {The spatiotemporal structure of neural activity in motor cortex during reaching.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {},
number = {},
pages = {},
doi = {10.1523/JNEUROSCI.1965-25.2026},
pmid = {42045070},
issn = {1529-2401},
abstract = {Intracortical brain-computer interfaces (BCI) leverage knowledge about neural representations to translate movement-related neural activity into actions. BCI implants have targeted broad cortical regions known to have relevant motor representations, but emerging technologies allow flexible targeting to specific neural populations. The structure of motor representations in neural populations across frontal motor cortices, which span centimeters, has not been well characterized. We investigate how motor representations and population dynamics (temporal coordination) vary across a large expanse of frontal motor cortices. We used high-density, laminar, microelectrode arrays to record many neurons, sampling neural populations across frontal motor cortex while two male monkeys performed a reaching task. Our experiments allowed us to map neuronal activity across three spatial dimensions and relate them to movement. Target decoding analysis revealed that target direction information (one key aspect of task information) was heterogeneously distributed across the cortical surface and in depth. Similarly, we found that the temporal dynamics of different neural populations was highly variable, but that the amount of task information predicted which neural populations had similar dynamics. The neural populations with the most similar dynamics were composed of neurons with high task information regardless of spatial location. Our results highlight the spatiotemporal complexity of motor representations across frontal motor cortex at the level of neurons and neural populations, where well-learned movements consistently recruit a spatially distributed subset of neurons. Further insights into the spatiotemporal structure of neural activity patterns across frontal motor cortex will be critical to guide future implants for improved BCI performance.Significance Statement Motor brain-computer interfaces (BCI) translate neural activity into movement, but how to target implants within motor cortices to maximize performance remains unclear. We used high-density recordings of neural activity spanning a large cortical area and related them to movement to map the spatial distribution of task information and the evolution of neural population activity over time. Our measurements revealed that neurons with the most task information were highly distributed across cortex yet also evolved coherently in time, suggesting that spatially distributed neurons coordinate to control movements. Our results provide new links between neuron- and population-level maps of motor representations, and highlight the complex spatiotemporal structure of activity that may need to be considered when designing next-generation BCIs.},
}

@article {pmid42045188,
year = {2026},
author = {Wang, H and Zhang, Y and Chai, Q and He, Q and Hu, J and Bai, Y and Liu, G and Li, Z and Chai, J and He, X and Zhao, M and Xue, G and Liu, K and Fu, Y and Tang, H and Xu, Y and Yu, B},
title = {Artificial plateau neurons with in-situ spike-malleability for rhythmic quadrupedal locomotion.},
journal = {Nature communications},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41467-026-72428-2},
pmid = {42045188},
issn = {2041-1723},
support = {DT23F0401//National Natural Science Foundation of China (National Science Foundation of China)/ ; LDT23F04011F04//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; },
abstract = {Whole-body intelligent locomotion systems face persistent challenges of redundant actuation and poor energy efficiency, limiting real-world deployment. Bio-inspired central pattern generators offer a promising framework for rhythmic control, yet hardware implementations struggle to match the efficiency and adaptability of biological systems. Here, we introduce an in-situ spike-malleable artificial plateau neuron integrating a bistable plateau gate with a transient threshold-switch. The neuron generates amplitude-programmable rhythmic spike bursts, achieving energy-efficient, antagonistic activation of extensors and flexors via a scalable circuit comprising two paired units (plateau gate and threshold-switch). The design leverages distributed encoding for coordinated muscle control, operating at ultra-low energy dissipation (141.37 pJ/spike). An expanded four-unit circuit enhances dynamic spike malleability, enabling parallel processing for multi-joint coordination. On a quadruped robot (Unitree Go2), these distributed circuits directly drive joint-level proportional derivative controllers using the Gaussian-filtered rhythmic spikes, enabling energy-efficient trotting without centralized computation. Critically, the system achieves stable on-ground locomotion and demonstrates adaptive gait transitions in real-world environments. Our approach merges ultra-compact hardware with bio-inspired architecture, advancing neuromorphic systems for energy-efficient autonomous robotics.},
}

@article {pmid42034279,
year = {2026},
author = {Long, Y and Yao, P and Zou, G and Guo, Y and Shao, Y and Li, Y and Liu, J and Zhou, S and Xu, J and Sun, H and Zou, Q and Gao, JH},
title = {EEG microstates during wake and NREM sleep in insomnia disorder.},
journal = {Progress in neuro-psychopharmacology & biological psychiatry},
volume = {},
number = {},
pages = {111718},
doi = {10.1016/j.pnpbp.2026.111718},
pmid = {42034279},
issn = {1878-4216},
abstract = {Insomnia disorder (ID) is prevalent and debilitating, yet its neurophysiological basis remains unclear. Abnormalities in temporal parameters of electroencephalography (EEG) microstates have been linked to diverse neuropsychiatric disorders, but their dynamics across wakefulness and non-rapid eye movement (NREM) sleep in chronic insomnia remain unexplored. In this study, EEG microstate dynamics across wakefulness and NREM sleep were examined in adults with ID (n = 33) and healthy controls (HC; n = 29). Simultaneous EEG and functional magnetic resonance imaging (fMRI) were acquired during nocturnal sleep. Microstate parameters were tested for group differences, diagnostic classification, and associations with insomnia symptom course. Six stable microstates across stages were identified in ID. Linear mixed-effects models revealed significant interactions for Group × Map and main effects of Group on duration and occurrence of the microstates. Compared to HC, ID exhibited significantly shorter mean duration of microstates 1-3, and higher overall occurrence rates of microstates 4-6. The best-performing classifier achieved 90.0% accuracy in distinguishing ID from HC. The most influential predictor was the mean duration of Microstate 2, which was negatively associated with insomnia course. Together, these findings suggest stage- and map-dependent alterations in sleep microstate dynamics in ID, especially during N2 sleep.},
}

@article {pmid42034616,
year = {2026},
author = {Zhai, X and Guo, J and Shen, Q and Chen, LN and Wang, G and Shen, DD and Zhang, C and Xu, X and Mao, C and Zhang, Y and Liu, Z},
title = {Core conformation of arrestin coupling to parathyroid hormone type 1 receptor.},
journal = {Nature communications},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41467-026-72448-y},
pmid = {42034616},
issn = {2041-1723},
abstract = {The recruitment of β-arrestin (βarr) by G-protein-coupled receptor (GPCR) holds imperative importance in physiological processes, while the mechanisms underlying arrestin engagement with receptors remain obscure. The parathyroid hormone type 1 receptor (PTH1R), as a prototypical class B1 receptor, incorporates arrestin for signaling and regulates G-protein signaling by distinct mechanisms. Here, we report three cryo-electron microscopy structures of β-arrestin1 (βarr1) engaged with the activated wild-type and chimeric PTH1R in core conformation, revealing a distinctive binding mode of βarr1 coupling to PTH1R compared to other GPCRs. In addition to the pronounced kinking of transmembrane (TM) 6, βarr1 establishes extensive interactions with the core cavity of PTH1R by promoting the outward movement of TM5 and intracellular loop (ICL) 2, stabilizing the core conformation of the complex. Further, our work shows that the core coupling mode of βarr with PTH1R mediates receptor internalization and trafficking. Collectively, our work offers a paradigm for the arrestin coupling to class B1 GPCR and regulating the signaling transduction.},
}

@article {pmid42035423,
year = {2026},
author = {Ji, YL and Yin, B and Zhu, G and Wang, X and Zhang, Y and Zhao, Q},
title = {Discontinuous 3D Printing of Amorphous Photonic Crystal Hydrogels for Multifunctional Applications.},
journal = {Small (Weinheim an der Bergstrasse, Germany)},
volume = {},
number = {},
pages = {e73576},
doi = {10.1002/smll.73576},
pmid = {42035423},
issn = {1613-6829},
support = {62575143//National Natural Science Foundation of China/ ; 62174085//National Natural Science Foundation of China/ ; 62288102//National Natural Science Foundation of China/ ; BK20240034//Natural Science Foundation of Jiangsu Province/ ; },
abstract = {Amorphous photonic crystals (APCs) offer angle-independent structural color for reliable sensing, yet their precise 3D fabrication remains challenging due to the tendency of particles to self-assemble into ordered structures. We develop a discontinuous digital light processing 3D printing strategy combining discrete ink reflow and rapid curing to construct disordered APCs hydrogels. The printed hydrogels integrate single-sized polymer nanospheres and MXene nanosheets to achieve structural color, mechanical robustness, and interactive optical/electrical responsiveness. The structural color responds to moisture-induced swelling yet remains unchanged under mechanical deformation because of strain-accommodating microcracks. These features ensure reliable visual feedback without strain interference. Meanwhile, mechanical deformation modulates the conductive network and thus provides a complementary electrical response. In diabetic wound models, the hydrogel enables precise electrical stimulation and provides visual alerts of micro-swelling to prevent secondary damage from unnoticed volumetric changes. This strategy provides a generalizable pathway for precise intervention and real-time monitoring in wound management.},
}

@article {pmid42035944,
year = {2026},
author = {Poslu Karademir, F and Özçelik, SS and Efe, AÇ and Ulaş, MG and Diri, İ and Kaynak, P and Taşkapılı, M},
title = {Outcomes of probing with or without bicanalicular intubation in children aged three years and older: a decade of experience at a tertiary eye hospital.},
journal = {Journal of AAPOS : the official publication of the American Association for Pediatric Ophthalmology and Strabismus},
volume = {},
number = {},
pages = {104841},
doi = {10.1016/j.jaapos.2026.104841},
pmid = {42035944},
issn = {1528-3933},
abstract = {PURPOSE: To evaluate the clinical efficacy of probing with or without bicanalicular intubation (BCI) for congenital nasolacrimal duct obstruction (CNLDO) in children at least 3 years of age and to identify factors influencing surgical success.

METHODS: The medical records of children treated between 2014 and 2024 at Health Sciences University Beyoğlu Eye Training and Research Hospital were reviewed retrospectively. All patients underwent probing with or without bicanalicular silicone intubation (BCI) using the square knot technique. Surgical success was defined as resolution of symptoms and a normal fluorescein dye disappearance test.

RESULTS: A total of 95 children (116 eyes) were included. Mean patient age was 4.57 ± 1.98 years (range, 3-14). Mean follow-up was 15.5 ± 15.4 months. BCI was performed initially in 102 eyes. Mean tube retention was 66.8 ± 43.0 days. Overall success was 87%, increasing to 95% after reprobing and BCI in failed cases. Age, sex, obstruction type, canalicular stenosis, Rosenmüller's valve hypertrophy, and inferior turbinate infracture were not significantly associated with success (P > 0.05). Tube retention for 45-90 days was significantly associated with higher success compared with retention <45 days (P = 0.013; OR = 12.75; 95% CI, 1.72-94.48).

CONCLUSIONS: In our study cohort of children undergoing surgery for CNLDO at 3 years of age and older, probing and BCI achieved high success, especially if the tube was successfully retained for at least 45 days. Reintubation in failed cases can improve outcomes.},
}

@article {pmid42036262,
year = {2026},
author = {Wang, H and Liu, S and Bai, L},
title = {A dimmer switch for reward: the vagus sets the gain.},
journal = {Trends in neurosciences},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.tins.2026.04.002},
pmid = {42036262},
issn = {1878-108X},
abstract = {The vagus nerve, among its various functions, carries gut nutrient signals to brain reward circuits. In a recent study, Onimus and colleagues have shown it is more than a simple relay: vagal integrity is required for maintaining dopamine release and the circuit structure of the mesolimbic system to mount reward responses. These findings reveal the vagus as a tonic gatekeeper of motivation and reward.},
}

@article {pmid42036584,
year = {2026},
author = {Tsai, HJ and Tsai, SJ},
title = {Neurofeedback in Major Depression.},
journal = {Advances in experimental medicine and biology},
volume = {1502},
number = {},
pages = {461-475},
pmid = {42036584},
issn = {0065-2598},
mesh = {Humans ; *Neurofeedback/methods ; *Major Depressive Disorder/therapy/physiopathology/psychology ; *Brain/physiopathology ; Electroencephalography ; Brain-Computer Interfaces ; Magnetic Resonance Imaging ; Neuronal Plasticity/physiology ; Emotions/physiology ; },
abstract = {Emerging evidence highlights the significant interplay between mental health and brain health, underscoring the potential of non-pharmacological interventions for major depressive disorder. Brain-computer interfaces offer a promising avenue in psychiatry, advancing self-regulation techniques to elucidate relationships among human behavior, emotional processes, and brain functionality. By visualizing brain function and enabling active modulation of cortical activity through real-time feedback, neurofeedback utilizes signals derived from electroencephalography and/or real-time functional magnetic resonance imaging, providing patients with interactive indicators for self-brain training. This closed-loop system targets specific brain activity or regions known to be associated with depression for upregulation or downregulation, thereby enhancing emotion regulation and executive functioning via mechanisms underlying neuroplasticity. Clinical evidence demonstrates promising outcomes, including strengthened neural connectivity, symptom improvement, and increased remission rates in depression. By coupling the advantages of psychotherapy and neuromodulation, neurofeedback aligns with the field's shift toward personalized, technology-driven psychiatry. This chapter also addresses the practical challenges, including protocol standardization, precision targeting, long-term assessment, and scalable delivery, that are essential for translating promising pilot data into routine clinical practice and for empowering patients to actively engage in their brain health toward mental health improvement.},
}

Humans
*Neurofeedback/methods
*Major Depressive Disorder/therapy/physiopathology/psychology
*Brain/physiopathology
Electroencephalography
Brain-Computer Interfaces
Magnetic Resonance Imaging
Neuronal Plasticity/physiology
Emotions/physiology

@article {pmid42026803,
year = {2026},
author = {Zhang, F and Hu, K and Sun, C and Chen, R and Ni, G and Liu, X and Wei, L and Su, R},
title = {Gene-level gut microbiome signatures as predictive biomarkers for response to immune checkpoint inhibitors across multiple cancer types.},
journal = {Gut microbes},
volume = {18},
number = {1},
pages = {2662690},
doi = {10.1080/19490976.2026.2662690},
pmid = {42026803},
issn = {1949-0984},
mesh = {Humans ; *Immune Checkpoint Inhibitors/therapeutic use ; *Gastrointestinal Microbiome/genetics/drug effects ; *Neoplasms/drug therapy/microbiology ; Deep Learning ; Biomarkers, Tumor/genetics ; *Bacteria/classification/genetics/isolation & purification ; Female ; Male ; Metagenomics ; },
abstract = {Targeting programmed cell death protein 1 (PD-1) and cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) with immune checkpoint inhibitors (ICIs) has improved survival across multiple cancer types, but the variability in patient response highlights the need for better predictive biomarkers. Existing studies rely on taxonomic abundance derived from reference genome databases, limiting the discovery and functional interpretation of uncharacterized microbes. Here, we integrated metagenomic data from multiple ICI-treated cohorts spanning diverse cancer types and geographic regions and developed a deep learning model, named BioP-VAE, that incorporates biological prior knowledge via protein sequence embeddings and uses gene-level microbial abundance features as input. Gene-level microbial abundance outperformed taxonomy abundance in predicting both ICI response and 12-month progression-free survival (PFS). In patients receiving combination immune checkpoint blockade (CICB), BioP-VAE achieved a mean AUC of 0.89 in intracohort and 0.88 in cross-cohort evaluation. Notably, in the monotherapy-treated intracohorts, BioP-VAE achieved a mean AUC of 0.97. Feature attribution analysis revealed key microbial genes. Additionally, we identified distinct predictive microbial signatures via age-stratified analysis, suggesting that host age may modulate microbiome‒immune interactions. Importantly, this is the first large-scale study to evaluate gene-level microbial abundance features for ICI response prediction across multiple cancer types by deep learning. Our findings demonstrate that incorporating biological prior knowledge into deep learning models can improve the discovery of microbial biomarkers that can be generalized across cancer types and treatment settings, offering a novel strategy for patient stratification in immunotherapy.},
}

Humans
*Immune Checkpoint Inhibitors/therapeutic use
*Gastrointestinal Microbiome/genetics/drug effects
*Neoplasms/drug therapy/microbiology
Deep Learning
Biomarkers, Tumor/genetics
*Bacteria/classification/genetics/isolation & purification
Female
Male
Metagenomics

@article {pmid42028018,
year = {2026},
author = {Zhang, J and Zhang, Y and Zhang, X and Xu, B and Zhao, H and Sun, T and Wang, J and Lu, S and Shen, X},
title = {Erratum: A high-performance general computer cursor control scheme based on a hybrid BCI combining motor imagery and eye-tracking.},
journal = {iScience},
volume = {29},
number = {5},
pages = {115705},
doi = {10.1016/j.isci.2026.115705},
pmid = {42028018},
issn = {2589-0042},
abstract = {[This corrects the article DOI: 10.1016/j.isci.2024.110164.].},
}

@article {pmid42028123,
year = {2026},
author = {Mediana, E and Hamid, ARAH and Rahman, F and Mochtar, CA and Umbas, R and Abol-Enein, H},
title = {Quality of Life After Radical Cystectomy: Meta-analysis of Neobladder and Ileal Conduit Outcomes Across Multiple Assessment Tools.},
journal = {European urology open science},
volume = {87},
number = {},
pages = {115-124},
pmid = {42028123},
issn = {2666-1683},
abstract = {BACKGROUND AND OBJECTIVE: Radical cystectomy (RC) requires urinary diversion, commonly orthotopic neobladder (ONB) or ileal conduit (IC). While ONB preserves natural voiding, IC is technically simpler. This study aimed to compare long-term (>12 mo) quality of life (QoL) outcomes between ONB and IC to aid preoperative shared decision-making.

METHODS: Following PRISMA guidelines, we searched PubMed, Cochrane Library, and Google Scholar up to September 15, 2025. We included studies comparing ONB and IC in adults with follow-up >12 mo. Heterogeneity was explored using meta-regression. The Newcastle-Ottawa Scale assessed bias, and Review Manager v5.4 was used for analysis.

KEY FINDINGS AND LIMITATIONS: Nineteen studies involving 2379 patients were analyzed. For all assessment tools used (EORTC QLQ-C30, FACT-BL, SF-36, and Bladder Cancer Index [BCI]), higher scores indicate better QoL or function. Pooled analysis showed that ONB was associated with higher global health status (EORTC QLQ-C30: mean difference [MD] = -9.42, p = 0.009; negative value indicates higher score in ONB) and functional well-being (FACT-BL -2.60, p = 0.010). Conversely, the IC group demonstrated higher scores in urinary outcomes (BCI Urinary: MD = 22.81, p = 0.02; positive value indicates higher score in IC). Heterogeneity among studies was moderate to high. Meta-regression indicated geographic location and tumor characteristics influenced heterogeneity. Limitations include observational design and potential selection bias.

ONB reconstruction is associated with higher overall QoL scores, while IC is associated with higher urinary scores. These findings represent clinical trade-offs rather than superiority. Surgical selection should be individualized, balancing patient preference for body image against the risk of functional management challenge.},
}

@article {pmid42031565,
year = {2026},
author = {Haller, D and Beermann, F and Sîmpetru, RC and Hofbeck, L and Enoka, RM and Del Vecchio, A},
title = {Voluntary Dissociation of Motor Unit Activity in the Vastii Muscles.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {},
number = {},
pages = {},
doi = {10.1523/JNEUROSCI.1982-25.2026},
pmid = {42031565},
issn = {1529-2401},
abstract = {The CNS controls movement with consistent activation patterns across muscles and motor units (MU), suggesting the presence of a relatively fixed and high-dimensional number of neural constraints on voluntary actions. In the human quadriceps, the vastus medialis (VM) and vastus lateralis (VL) contribute to the knee extensor torque and are considered a synergistic pair largely activated by shared neural inputs. However, some evidence suggests that these muscles, or even subregions within them, can be controlled independently. We investigated whether humans can dissociate neural input to VM and VL during isometric contractions. Ten participants (6 males, 4 females) received real-time feedback from multiple intramuscular electromyography (EMG) electrodes inserted into different regions of the VM and VL while attempting to activate each muscle or region selectively. Nine out of ten participants were able to separate VM and VL activity based on the intramuscular EMG feedback. However, MU decomposition from the intramuscular EMGs revealed that selective recruitment of a unique set of MUs was possible only within the proximal region of VM. In contrast, we found highly correlated activity between MUs in VL and distal VM. Correlation analyses confirmed that the proximal VM exhibited distinct activation profiles compared with both distal VM and VL, supporting the existence of compartmentalized control within VM. These findings demonstrate that it is possible to dissociate the activation of MUs within this synergistic muscle group during low-force isometric contractions.Significance Statement Humans are typically thought to lack voluntary control over individual quadriceps muscles due to a shared neural input and a common distal tendon. With real-time EMG feedback from multiple muscle implants we found that participants were able to activate distinct MU populations within vastus medialis, partially dissociating its activity from the vastus lateralis. These results reveal a relatively flexible, region-specific neural control within a pair of synergistic muscles that offers new perspectives for motor learning and targeted rehabilitation.},
}

@article {pmid42031765,
year = {2026},
author = {Kim, C and Yeh, JY},
title = {Molecular evolution and antigenic stability of ibaraki virus: evidence for regional circulation from a South Korean whole-genome analysis.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-026-47936-2},
pmid = {42031765},
issn = {2045-2322},
support = {RS-2025-02304897//Korea Institute of Planning and Evaluation for Technology in Food, Agriculture and Forestry/ ; 2020-0325//Incheon National University/ ; },
}

@article {pmid42032248,
year = {2026},
author = {Tang, Y and Wang, Y and Zhang, W and Liu, X and Li, Y and Hu, W and Ding, L and Feng, F and Chen, X and Feng, J and Xu, S and Chen, S and Wang, J},
title = {Magnetic NeuroRing: a portable adaptive brain-computer interface for real-time transcranial magnetic stimulation in post-stroke motor rehabilitation.},
journal = {npj biomedical innovations},
volume = {3},
number = {1},
pages = {},
pmid = {42032248},
issn = {3005-1444},
support = {82202798, 5230130320, 22205254//National Natural Science Foundation of China/ ; 82202798, 5230130320, 22205254//National Natural Science Foundation of China/ ; 82202798, 5230130320, 22205254//National Natural Science Foundation of China/ ; 24YL1900202//the Project of Shanghai Science and Technology Commission/ ; 22YF1404200//Shanghai Sailing Program/ ; },
abstract = {Stroke often causes persistent upper limb and hand motor dysfunction due to disrupted neural reorganization. To address this, we developed the Magnetic NeuroRing: a portable brain-computer interface integrating real-time electroencephalogram (EEG) with closed-loop continuous theta burst stimulation (cTBS) for adaptive transcranial magnetic stimulation (TMS). A multi-channel EEG array over motor cortical regions (FC3, FC4, CP3, CP4, FT7, FT8, TP7, TP8) detects event-related desynchronization (ERD), indicating motor intent. When ERD/ERS falls below a threshold (ERD/ERS < 0 over five consecutive activations), the system delivers inhibitory cTBS to hyperactive regions, aiming to rebalance stroke-impaired interhemispheric dynamics. The lightweight, patient-specific headgear uses magnetic levitation for precise targeting and EEG-TMS synchronization. In healthy subjects, adaptive cTBS significantly modulated resting-state and task-related neural metrics, aligning with prior large-device findings and demonstrating feasibility for inducing neuroplastic changes. By bridging real-time diagnostics with targeted neuromodulation, the Magnetic NeuroRing enables dynamic, data-driven rehabilitation across clinical and home settings.},
}

@article {pmid42032320,
year = {2025},
author = {Li, J and Chen, G and Li, G and Xiao, L and Jia, R and Zhang, K},
title = {Flexible brain electronic sensors advance wearable brain-computer interface.},
journal = {npj biomedical innovations},
volume = {2},
number = {1},
pages = {},
pmid = {42032320},
issn = {3005-1444},
support = {2024NSFJQ0048//Sichuan Provincial Science and Technology Support Program/ ; 82022033//National Natural Science Foundation of China/ ; },
abstract = {The emerging field of wearable brain-computer interface (BCI) strives to achieve both high spatial and temporal resolution. The performance of flexible brain electronic sensor (FBES) has been validated across a variety of experimental settings, demonstrating their potential for real-world applications. As a result, FBES are increasingly shaping the landscape of health monitoring and disease treatment by enabling non-invasive, precise neural data acquisition. This review summarizes recent studies recent progress in wearable brain computer interface technology and FBES development, while provides insights into future clinical application of FBES within BCI systems. Additionally, we propose strategic directions to bridge the gap between laboratory research and practical healthcare implementations.},
}

@article {pmid42032610,
year = {2026},
author = {Bourgeois, A and Maiani, M and Minhas, A and Shaw, P and Nikitovic, D and Irvine, B and Robu, I and Brand, N and Jadavji, Z and Wilding, G and Ambrogiano, M and Schrag, E and Hilderley, A and Márquez, DC and Carlson, HL and Romanow, N and Kirton, A and Kinney-Lang, E},
title = {Creating an engaging brain computer interface, electrical stimulation therapy for children with hemiparesis: a pilot study.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {},
number = {},
pages = {},
doi = {10.1186/s12984-026-01990-z},
pmid = {42032610},
issn = {1743-0003},
}

@article {pmid42021568,
year = {2026},
author = {Li, Z and Ge, R and Zhao, Z and Xiao, H and Du, C and Lai, Y and Wang, L},
title = {From Bio-Interface Materials to Neural Integration: The Next-Generation Brain-Machine Interfaces Powered by Hydrogels.},
journal = {Advanced materials (Deerfield Beach, Fla.)},
volume = {},
number = {},
pages = {e23422},
doi = {10.1002/adma.202523422},
pmid = {42021568},
issn = {1521-4095},
support = {22322803//National Natural Science Foundation of China/ ; 22375047//National Natural Science Foundation of China/ ; 22361162607//International Cooperation and Exchanges NSFC/ ; 20240305028YY//Key Research Development Program of Jilin Province/ ; 2022YFB3804905//National Key Research and Development Program of China/ ; 2022YFB3804900//National Key Research and Development Program of China/ ; //Graduate Innovation Fund of Jilin University/ ; FZ2025038//State Key Laboratory of New Textile Materials and Advanced Pro- cessing/ ; },
abstract = {Brain-machine interfaces (BMIs), which serve as revolutionary tools for neural recording, modulation, and rehabilitation, are highly dependent on the biocompatibility and mechanical suitability of their electrode materials. Although traditional metal electrodes possess excellent conductivity, their inherent rigidity causes a substantial mechanical mismatch with soft neural tissue, leading to chronic inflammatory responses and poor long-term stability. The emergence of hydrogel electrodes has provided a breakthrough solution to this fundamental limitation. Hydrogels, characterized by their softness, high ionic conductivity, and tissue-like compliance, offer a viable solution to mitigate these issues. This review systematically explores the material properties of hydrogel-integrated BMIs, providing an in-depth investigation of key hydrogel characteristics, including toughness, adhesion, conductivity, and biocompatibility. Additionally, hydrogel-based BMIs are categorized into non-invasive and invasive systems, each defined by its characteristic operational principles and signal-acquisition mechanisms. The study further reviews critical issues, including surgical implantation strategies, multimodal data fusion, integration of artificial intelligence, as well as system integration and clinical translation. From a therapeutic perspective, this work highlights the application of BMIs in treating neurological disorders such as Alzheimer's disease, Parkinson's disease, epilepsy, stroke, neuropathic pain, and depression. Furthermore, this review critically examines the persistent challenges faced by hydrogel-based BMIs and proposes innovative strategies for future development. Ultimately, it outlines a developmental roadmap for next-generation hydrogel-based biotherapeutic technologies aimed at achieving high-fidelity, stable and clinically translatable BMI systems.},
}

@article {pmid42022239,
year = {2026},
author = {Roualdes, V and Moussaoui, S and Normand, JM and Kuhn, E and Nizard, J and Van Langhenhove, A},
title = {EEG-based brain-computer interface with immersive virtual reality for phantom limb pain: a single-center pilot neurofeedback trial.},
journal = {Frontiers in human neuroscience},
volume = {20},
number = {},
pages = {1697837},
pmid = {42022239},
issn = {1662-5161},
abstract = {BACKGROUND: Phantom limb pain (PLP) is a challenging neuropathic pain condition following limb amputation or brachial plexus injury. Non-pharmacological interventions such as motor imagery training, phantom motor execution and mirror therapy have shown potential to alleviate PLP by engaging sensorimotor circuits, but their effects are debated. We developed GHOST, a portable EEG-based brain-computer interface (BCI) coupled with immersive virtual reality (VR), allowing patients to control a virtual limb via motor imagery in real time, as a neurofeedback-based rehabilitation tool.

METHODS: We conducted a single-center exploratory pilot trial to assess the feasibility and preliminary efficacy of this device. Seven patients with chronic upper-limb PLP (amputees or brachial plexus avulsion, pain ≥3/10) underwent 10 training sessions over 2 weeks. Daily pain diaries (distinguishing continuous pain vs. paroxysmal pain episodes) were recorded for 1 month before and 1 month after the intervention, with follow-up to 6 months. Motor imagery ability, anxiety-depression (HADS), and quality of life (SF-36) were also evaluated.

RESULTS: Six patients completed ≥8 sessions. All participants achieved BCI control of the virtual hand, with high success rates (>70%) even as task difficulty increased, demonstrating system feasibility. No adverse events occurred. Compared to baseline, patients experienced a significant short-term reduction in paroxysmal pain (frequency and intensity of pain "flare-ups"), with up to >80% median decrease in weekly cumulated pain episode intensity (p < 0.001). Three of five patients also reported around 30% improvement in average daily pain during the first post-training month. HADS anxiety/depression scores showed a non-significant improving trend. By 3-6 months post-training, pain levels had largely returned to pre-intervention values.

CONCLUSION: This pilot study supports the safety and feasibility of EEG-BCI with VR for PLP and suggests that it can yield short-term analgesic effects, particularly on paroxysmal pain. These findings support the hypothesis that sensorimotor re-engagement could effectively target maladaptive neural processes underlying PLP, while warranting confirmation in controlled trials. Future work will optimize the training protocol and investigate neuroplastic changes associated with this BCI-VR intervention.},
}

@article {pmid42023246,
year = {2026},
author = {Kumaresan, V and Pahari, S and Hung, CY and Hermann, BP and Schlesinger, LS and Seshu, J},
title = {Role of dual specificity phosphatase 1 in influencing inflammatory pathways in macrophages modulated by Borrelia burgdorferi lipoproteins.},
journal = {Frontiers in immunology},
volume = {17},
number = {},
pages = {1766756},
pmid = {42023246},
issn = {1664-3224},
mesh = {Animals ; *Borrelia burgdorferi/immunology ; *Macrophages/immunology/metabolism ; *Lipoproteins/immunology ; Mice ; Signal Transduction/immunology ; *Lyme Disease/immunology/microbiology ; *Dual Specificity Phosphatase 1/metabolism/genetics/immunology ; *Inflammation/immunology ; Humans ; Proteomics ; Host-Pathogen Interactions/immunology ; Mice, Inbred C57BL ; },
abstract = {Borrelia burgdorferi (Bb), the spirochetal agent of Lyme disease, has a large array of lipoproteins that play a significant role in mediating host-pathogen interactions within ticks and vertebrates. While prior work has established that borrelial lipoproteins (BbLP) modulate immune signaling pathways, the broader transcriptional and proteomic programs induced by these molecules in macrophages are unclear. Here, we used integrated multi-omics approaches to characterize host signaling pathways activated specifically by purified borrelial lipoproteins in murine bone marrow derived macrophages (BMDMs). Single-cell RNA-Seq (scRNA-Seq) performed on BMDMs treated with various concentrations of borrelial lipoproteins revealed macrophage subsets within the BMDMs. Differential expression analysis showed that genes encoding various receptors, type I IFN-stimulated genes, signaling chemokines are upregulated while mitochondrial and ribosomal genes are downregulated in BMDMs in response to lipoproteins. Unbiased proteomics analysis of lysates of BMDMs treated with lipoproteins corroborated several of these findings. Notably, dual specificity phosphatase 1 (Dusp1) gene was upregulated during the early stages of BMDM exposure to BbLP. Pharmacological inhibition with benzylidene-3-cyclohexylamino-1-indanone hydrochloride (BCI), an inhibitor of both DUSP1 and 6 prior to exposure to BbLP, demonstrated that DUSP1 negatively regulates NLRP3-mediated pro-inflammatory signaling and positively regulates the expression of interferon-stimulated genes and those encoding Ccl5, Il1b, and Cd274. Using human monocytic reporter cell lines, we showed MyD88- and IKK-dependent pathways contribute to mitochondrial alterations upon stimulation with lipoproteins. Extracellular flux analysis using the Seahorse assay revealed decreased oxygen consumption rate (OCR) and increased extracellular acidification rate (ECAR), indicating time-dependent metabolic reprogramming and a shift toward a glycolytic, pro-inflammatory metabolic state in BMDMs following BbLP stimulation. Collectively, these findings define signaling networks, regulatory nodes and metabolic alterations induced by borrelial lipoproteins in macrophages and highlight DUSP1 as a key modulator of lipoprotein-driven innate immune responses. This work provides a mechanistic framework for understanding how borrelial lipoproteins shape macrophage signaling, independent of the broader complexity of infection with intact pathogen.},
}

Animals
*Borrelia burgdorferi/immunology
*Macrophages/immunology/metabolism
*Lipoproteins/immunology
Mice
Signal Transduction/immunology
*Lyme Disease/immunology/microbiology
*Dual Specificity Phosphatase 1/metabolism/genetics/immunology
*Inflammation/immunology
Humans
Proteomics
Host-Pathogen Interactions/immunology
Mice, Inbred C57BL

@article {pmid42024948,
year = {2026},
author = {Ma, W and Zhang, H and Li, Y and Wei, M},
title = {NeuroDecoder: A new framework for image decoding and reconstruction of EEG signals.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2026.3686624},
pmid = {42024948},
issn = {2168-2208},
abstract = {Brain-Computer Interface (BCI) technology holds great promise for enhancing human health and quality of life, with visual stimulus reconstruction from EEG signals being a key application. However, the complexity and noise of EEG data challenge existing reconstruction methods. To address these issues, we propose NeuroDecoder, an end-to-end multimodal guidance generation framework that produces high-quality images from EEG signals. The key innovation is the collaborative mitigation of EEG noise and cross-modal representation discrepancies through a noise-robust encoder, mask-based triple-contrastive alignment, and a fixed generative model. Specifically, NeuroDecoder consists of three integrated learning stages: 1) EEG Decoding, 2) Modality Alignment, and 3) Image Reconstruction. In the decoding stage, a novel visual decoding model extracts visually relevant features with superior classification accuracy. In the alignment stage, a mask-based triple contrastive learning strategy achieves efficient cross-modal alignment of EEG, text, image, and edge map embeddings into a unified space. In the generation stage, a new reconstruction pipeline feeds the aligned EEG embeddings into a pre-trained stable diffusion model, enabling high-quality visual stimulus reconstruction with enhanced semantic and structural fidelity, without fine-tuning the generative model. On three EEG datasets, NeuroDecoder achieved subject-dependent classification accuracies of 99.76%, 94.41%, and 56.67%, respectively; in the subject-independent setting, it performed near random on EEGCVPR40 but reached 91.61% and 37.63% on the other two. For image reconstruction, it obtained Fréchet Inception Distance of 62.84 and 63.12 on the first two datasets. Extensive experiments demonstrate that NeuroDecoder outperforms prior methods in both EEG classification accuracy and image reconstruction quality.},
}

@article {pmid42016063,
year = {2026},
author = {Fankhauser, CD and Röthlin, K and Baumeister, P and Mattei, A and Piatti, P and Chew, YC and Kaufmann, E},
title = {Validation of the diagnostic accuracy of a urine-based DNA methylation marker test in patients with upper urinary tract lesions.},
journal = {BJUI compass},
volume = {7},
number = {3},
pages = {e70195},
pmid = {42016063},
issn = {2688-4526},
abstract = {OBJECTIVES: This study aims to validate the diagnostic accuracy of a novel urine-based DNA methylation test in patients with suspected upper tract urothelial carcinoma (UTUC) on CT urography and to assess its potential to eliminate the need for diagnostic ureterorenoscopy (URS) in selected patients, expedite treatment and identify high-grade tumours suitable for neoadjuvant chemotherapy.

PATIENTS AND METHODS: We prospectively collected urine samples from 46 consecutive patients with suspected UTUC in computed tomography and analysed them using the Bladder CARE™ methylation test. Test performance was evaluated against final pathology from URS biopsies and/or surgical specimens. We performed Youden Index analysis to optimise diagnostic cut-off values and assessed correlations between Bladder CARE Index (BCI) levels and tumour characteristics, particularly grade differentiation.

RESULTS: Using the manufacturer's cut-off (BCI > 2.5), the test demonstrated 95% sensitivity, 69% specificity, 70% positive predictive value and 95% negative predictive value (NPV), significantly outperforming cytology (11% sensitivity). An optimised, study-derived cut-off (4.35) further improved specificity to 92% with sensitivity and NPV remaining ≥95%. Importantly, a higher threshold (BCI > 10) yielded 100% specificity and 100% PPV, although at the expense of sensitivity (65%). Median BCI values differed between high-grade (38.6) and low-grade tumours (9.45), suggesting utility for non-invasive grade assessment. BCI also correlated with tumour size (β = 12 mm per log10 increase, p = 0.08).

CONCLUSION: This novel urine-based DNA methylation test offers high diagnostic accuracy for UTUC detection. However, clinical interpretation should be threshold dependent. While BCI values >2.5 show high sensitivity, the PPV of 70% indicates a relevant proportion of false-positive results, and diagnostic URS remains warranted in this range. In contrast, high positive values (BCI > 10) demonstrated 100% specificity and PPV and could enable direct progression to definitive surgery without diagnostic URS, avoiding procedure-related complications and expediting treatment. The correlation with tumour grade addresses a critical need for identifying candidates for neoadjuvant chemotherapy without invasive tissue diagnosis.},
}

@article {pmid42018586,
year = {2026},
author = {Zhao, Y and He, D and Ren, F and Xia, Q and Xu, L and Xie, G and Zhang, X and Yang, R and Zou, S and Jiang, B},
title = {RMETNet: A cross-subject motor imagery EEG signal classification model based on TSLANet and riemannian geometry features.},
journal = {PloS one},
volume = {21},
number = {4},
pages = {e0347671},
doi = {10.1371/journal.pone.0347671},
pmid = {42018586},
issn = {1932-6203},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Signal Processing, Computer-Assisted ; Algorithms ; Deep Learning ; *Imagination/physiology ; },
abstract = {Motor imagery electroencephalogram (MI-EEG) analysis is essential for natural interaction and autonomous control in brain-computer interfaces (BCIs). However, deep learning models often struggle with inter-subject variability, which limits their ability to generalize across subjects. This study proposes RMETNet, a novel framework that integrates TSLANet, a spatio-temporal convolution module, and a multi-scale Riemannian geometry feature module. TSLANet suppresses noise and captures complex temporal patterns for preliminary signal decoding, while the spatio-temporal convolution module extracts higher-order representations. The Riemannian branch learns geometry-based distribution features across subjects, and the fused features are used for classification. To address inter-subject distribution shifts, RMETNet incorporates Maximum Mean Discrepancy (MMD) loss for domain adaptation, aligning feature distributions between source and target domains. Experiments show that on the four-class BCI Competition IV 2a (BCICIV2a) dataset, RMETNet achieved accuracies of 71.39% in the cross-subject setting and 80.71% in the subject-dependent setting; on the two-class BCI Competition IV 2b (BCICIV2b) dataset, it achieved 80.93% and 86.76%, respectively. The model consistently outperformed baseline algorithms. Ablation and visualization analyses further validated its effectiveness in reducing inter-subject feature distribution disparities and enhancing MI-EEG decoding. The code is available at: https://github.com/rokanfeermecer486/RMETNet.},
}

Humans
*Electroencephalography/methods
*Brain-Computer Interfaces
Signal Processing, Computer-Assisted
Algorithms
Deep Learning
*Imagination/physiology

@article {pmid42019053,
year = {2026},
author = {Zhang, S and Zhang, H and Wei, M and Yang, C},
title = {An Optimized Encoding BCI Framework: Implementing Massive Command with Minimal Calibration.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2026.3686710},
pmid = {42019053},
issn = {1558-0210},
abstract = {As a critical metric for brain-computer interfaces (BCIs), the number of commands directly defines the control capacity for practical applications. However, existing BCIs often suffer from limited command sets and prohibitive calibration costs. To address these problems, this study presents a functional optimizationbased encoding framework to generate massive com8 mands with high discriminability while minimizing calibration burden. Specifically, a functional optimization theory enhances command distinguishability by optimizing the encoding function, while a few-shot training strategy leverages symbol reusability to reduce calibration data. Additionally, a symbol-joint decoding approach improves recognition accuracy. Using this framework, we developed an online BCI system with an unprecedented 1,008 commands-establishing a dual state-of-the-art (SOTA) in both command scale and calibration efficiency for large-scale BCIs (>100 commands). Comparative analysis shows that the functional optimization strategy improved accuracy by 13.94% and the information transfer rate (ITR) by 26.12% over the widely adopted baseline. Remarkably, with only 72 seconds of calibration data, the system achieved an average accuracy of 86.60 ± 13.35% and an average ITR of 122.74 ± 24.64 bits/min across 15 subjects, peaking at 100%. The framework features high flexibility in command encoding and robust cross-paradigm compatibility, significantly enhancing BCI performance and practicality.},
}

@article {pmid42019267,
year = {2026},
author = {Cui, Y and Yun, R and Zhang, S and Gong, Y and Li, Z and Chen, Y and Su, M and Wu, D and Wu, J and Wang, Q and Wang, J and Tian, Q and Yuan, Y and Mei, S and Wu, L and Li, X and Zhang, B and Guo, T and Sun, J},
title = {EEG predict response to transcutaneous auricular vagus nerve stimulation in treatment-resistant schizophrenia.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {187},
number = {},
pages = {2111892},
doi = {10.1016/j.clinph.2026.2111892},
pmid = {42019267},
issn = {1872-8952},
}

@article {pmid42019559,
year = {2026},
author = {Xu, P and Chen, Y and Wei, X and Qi, J and Chen, Y and Li, L},
title = {Resting-State EEG Networks Predict Individual Differences in Cognitive Flexibility.},
journal = {Brain research bulletin},
volume = {},
number = {},
pages = {111895},
doi = {10.1016/j.brainresbull.2026.111895},
pmid = {42019559},
issn = {1873-2747},
abstract = {Cognitive flexibility, the ability to adapt behavior and switch between tasks in response to changing goals, is a core component of executive function. However, the multiscale resting-state mechanisms underlying individual differences remain poorly understood. Here, resting-state electroencephalography (EEG) from 128 healthy participants (66 male; age 18-35 years) was analyzed to characterize frequency-specific connectivity and network topology. Results show that, delta-band fronto-temporal connectivity and associated graph metrics associated with repeat task performance, whereas beta-band fronto-parietal, fronto-occipital, and prefronto-frontal connections associated with shift task performance. Individuals with low switching costs exhibited stronger intra- and inter-hemispheric alpha-, beta-, and gamma-band connectivity, which were associated with more efficient cognitive flexibility. Multivariate models using connectivity features reliably predicted repeat RT and shift RT. Together, these findings indicate that hierarchical, frequency-specific resting-state networks constitute core neural mechanisms of cognitive flexibility and highlight the potential for resting-state EEG networks to account for individual differences in executive function.},
}

@article {pmid42019892,
year = {2026},
author = {Wang, M and Xu, S and Ball, LJ},
title = {Frequency-specific prefrontal inter-brain synchrony and reinforcement learning signatures differentiate cooperative and competitive risky decision-making: an fNIRS hyperscanning study.},
journal = {NeuroImage},
volume = {},
number = {},
pages = {121942},
doi = {10.1016/j.neuroimage.2026.121942},
pmid = {42019892},
issn = {1095-9572},
abstract = {The neural and computational mechanisms that distinguish cooperative from competitive strategies in risky decision-making remain incompletely understood. In this study, we combine frequency-specific prefrontal inter-brain synchrony (IBS) measured via functional near-infrared spectroscopy (fNIRS) hyperscanning with reinforcement learning modeling to examine how social context shapes dyadic choice. Sixty female dyads performed cooperative or competitive variants of a modified Iowa Gambling Task (IGT). Behaviorally, competitive pairs achieved significantly higher cumulative earnings than cooperative pairs. Reinforcement learning analyses indicated that the Outcome Representation Learning (ORL) model provided the best account of behavior. Cooperative dyads showed increased sensitivity to win frequency (βfre), suggesting a tendency to favor frequent but suboptimal gains. In contrast, competitive dyads adopted more flexible strategies that were less dependent on reward frequency. Neuroimaging results revealed dissociable frequency related patterns. Ultra-low frequency coupling in the dorsolateral prefrontal cortex (DLPFC) within the range of 0.015 to 0.017 Hz was associated with goal directed control and higher earnings. Higher frequency coupling in the frontopolar cortex (FPC) within the range of 0.340 to 0.381 Hz was associated with opponent monitoring and sustained competitive engagement, and was reduced during cooperation, consistent with reduced individual responsibility. These findings support a dual pathway account in which competition engages both control and monitoring processes to facilitate performance, whereas cooperation may incur performance costs through socially shaped learning biases. The results provide mechanistic insight into social decision making and identify candidate neural markers for adaptive behavior in interactive contexts.},
}

@article {pmid42020458,
year = {2026},
author = {Perez-Blanco, JG and Huegel, JC and Hernández-Rojas, LG and Valdez-Calderón, A and Lizárraga-Torreblanca, H and Cruz-Ortiz, D and Ballesteros, M and Gomez-Correa, M and Antelis, JM},
title = {An EEG-EMG-kinematics dataset from wrist pointing tasks for biomarker research in neurorehabilitation.},
journal = {Scientific data},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41597-026-07287-z},
pmid = {42020458},
issn = {2052-4463},
support = {1278020//SECIHTI/ ; SECTEI/081/2024//SECTEI/ ; },
abstract = {This work presents a multimodal dataset containing synchronized electroencephalography (EEG), electromyography (EMG), and kinematic recordings acquired during wrist motor tasks performed with a three degree of freedom robotic exoskeleton (BiomechWrist) coupled to a virtual interface. Designed as a normative baseline and benchmark resource for studying electrophysiological biomarkers and motor performance in healthy individuals, the dataset includes recordings from 45 healthy participants, each completing 320 trials of standardized wrist movements. The exoskeleton operated in transparent mode (actuators de-energized) to capture voluntary movements through high resolution encoders. Data are formatted according to the Brain Imaging Data Structure (BIDS) standard and follow FAIR principles, comprising raw biosignals, encoder trajectories, event markers, and derived performance metrics. To assess data quality, we provide subject level validation analyses, including power spectral density (PSD) and event related desynchronization/synchronization (ERDS) for EEG, as well as an EMG-Kinematic coupling analysis through Electromechanical Delay (EMD), and kinematic trajectory evaluation with performance metrics (accuracy, execution time, trajectory efficiency). This dataset supports research on wrist rehabilitation technologies and biomarker driven neuromodulation therapies, while also enabling studies in biosignal processing, artifact removal, machine learning for motor intention decoding, and the development of brain computer interfaces (BCI) and assistive devices targeting wrist mobility.},
}

@article {pmid42020535,
year = {2026},
author = {Margaret, MJ and Banu, NMM and Madhumithaa, S and Pathan, AK},
title = {On the prediction models for brain signal-based emotion recognition.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-026-47622-3},
pmid = {42020535},
issn = {2045-2322},
}

@article {pmid41880655,
year = {2026},
author = {Van Ransbeeck, W and Yuan, Z and Maes, PJ and Leman, M and Verhulst, S and Botteldooren, D},
title = {An EEG-based framework for exploring adaptive rhythmic human-machine interaction.},
journal = {Journal of neural engineering},
volume = {23},
number = {2},
pages = {},
doi = {10.1088/1741-2552/ae573d},
pmid = {41880655},
issn = {1741-2552},
mesh = {Humans ; *Electroencephalography/methods ; Male ; Female ; Adult ; Young Adult ; *Brain-Computer Interfaces ; *Periodicity ; Photic Stimulation/methods ; *Man-Machine Systems ; },
abstract = {Objective.Understanding rhythmic human-human interaction and its underlying mechanisms can enhance experiential value and enjoyment by providing a tailored experience and supporting applications in medical human-machine contexts. Existing experimental paradigms often lack a unified and holistic analysis, characterised by limited ecological validity in partner realism, active engagement, and visual interaction. These can produce hidebound insights due to variable partner behaviour, inflexible design, or insufficient user experience analysis. The study presents and validates a multimodal paradigm that addresses these limitations and enables controlled evaluation of human-human rhythm interaction and its extension to virtual AI agents.Approach.Participants completed a tapping paradigm with an audio-visual drum animation driven by either a human or AI-based partner under simple and complex (polyrhythmic) conditions. Portable electroencephalography (EEG) recordings and post-trial questionnaires assessed neural and subjective responses.Main results.The framework improves ecological validity relative to existing approaches and effectively masks partner identity (human vs AI) without reducing experienced flow, arousal, or enjoyment, which remained positive overall. Notably, the AI-based partner considered a first attempt to create a virtual AI-driven interacting drummer, suitable for future consideration of alternative algorithms. Additionally, the design supports unobtrusive, portable EEG measurement of neural modulation and temporal alignment with both performed and presented stimuli.Significance.This paradigm offers a flexible foundation for studying rhythmic interaction in human-machine systems, balancing ecological realism with experimental partner control while supporting future adaptive or biofeedback-driven systems that optimise rhythm interaction in real-time.},
}

Humans
*Electroencephalography/methods
Male
Female
Adult
Young Adult
*Brain-Computer Interfaces
*Periodicity
Photic Stimulation/methods
*Man-Machine Systems

@article {pmid42009354,
year = {2026},
author = {Kim, M and Heo, D and Kim, J and Kim, SP},
title = {Enhancing the Performance of Event-Related Potential-Based Brain-Computer Interfaces under Cognitive Distraction: A Multiwindow Adaptive Approach.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2026.3685282},
pmid = {42009354},
issn = {1558-0210},
abstract = {Event-related potential (ERP)-based brain- computer interfaces (BCIs) require focused attention to presented stimuli. However, their applications in real life frequently involve environments that demand multitasking and impose cognitive distraction. Such distractions degrade ERP amplitudes and consequently reduce BCI performance. This study proposes a multiwindow adaptive model to mitigate the adverse effects of cognitive distraction on visual ERP-based BCIs. The proposed approach divides poststimulus intervals into multiple overlapping windows, each with dedicated spatial filters and classifiers that continuously update through adaptive semi-supervised learning. Offline experiments on a BCI control dataset collected during concurrent speaking demonstrate that the proposed method significantly outperforms single-window or fixed (i.e., nonadaptive) models, yielding an accuracy of 91.08%. Further validation in an online experiment confirms that the multiwindow adaptive approach effectively restores BCI performance, achieving an accuracy of 93.20% despite cognitive distraction. These findings highlight the practical benefits of temporally tailored feature extraction and continuous adaptation for real-world ERP-based BCIs, enabling robust performance even under cognitive distraction.},
}

@article {pmid42010188,
year = {2026},
author = {Mesgarani, N},
title = {From Selective Listening to Brain-Controlled Hearing: A Perspective on the Future of Auditory Technology.},
journal = {Journal of the Association for Research in Otolaryngology : JARO},
volume = {},
number = {},
pages = {},
pmid = {42010188},
issn = {1438-7573},
abstract = {Understanding speech in noisy environments is a major challenge for millions, a problem that conventional hearing aids often exacerbate by amplifying all sounds indiscriminately. Auditory Attention Decoding (AAD) offers a revolutionary alternative: a brain-computer interface that decodes a listener's attentional focus from their neural signals to selectively enhance the desired sound source. For over a decade, research has demonstrated the scientific feasibility of attention decoding, yet the field has faced a critical barrier in translating this promise into a real-time system that provides a demonstrable perceptual benefit in real-world listening conditions. This perspective charts the journey of AAD, from its foundational neuroscientific discoveries to the current engineering hurdles that must be overcome for real-world deployment. We outline the key remaining challenges, including the need to define user-centric metrics for success, develop practical and power-efficient wearable sensors, design low-latency and computationally efficient decoding algorithms, and ensure robust performance in complex, naturalistic scenes. By addressing these questions, the field can move beyond passive amplification and create the next generation of assistive technology: one that listens with the brain to restore or augment the hearing experience, making it fully aligned with the user's intent.},
}

@article {pmid42012068,
year = {2026},
author = {Ye, Y and Wu, J and Zhang, Y and Ma, H and Deng, Q and Jian, J and Tang, R and Sun, B and Zeng, Y and Song, Y and Wang, J and Lin, H and Zhao, S and Li, L},
title = {Reconfigurable in-Sensor Image Enhancement Based on Tunable Band Alignment of In2Se3/PdSe2 Heterojunction.},
journal = {Nano letters},
volume = {},
number = {},
pages = {},
doi = {10.1021/acs.nanolett.5c06055},
pmid = {42012068},
issn = {1530-6992},
abstract = {In-sensor computing has emerged as a promising paradigm to overcome power consumption and latency bottlenecks in vision systems. Here, we demonstrate a reconfigurable in-sensor image enhancement strategy based on an In2Se3/PdSe2 ferroelectric heterojunction. The photodetector exhibits a broadband spectral response (400-1550 nm) and a high external quantum efficiency exceeding 10[4]%. By synergistically leveraging electrostatic and ferroelectric fields to tune the band alignment, we achieve programmable carrier collection efficiency, leading to a gate-tunable nonlinear photocurrent response. This hardware-level nonlinearity enables dual imaging modes for adaptive imaging: a low-light signal amplification mode to boost brightness and an overexposure recovery mode to compress contrast. By implementing a programmable photoresponse into a single photodetector, our approach bypasses redundant data transmission, providing a compact and energy-efficient solution for intelligent vision systems.},
}

@article {pmid42013255,
year = {2026},
author = {Wu, X and Daly, I and Lau, AT and Chen, W and Wang, C and Cichocki, A and Jin, J},
title = {Enhancing Target Recognition Performance in SSVEP-Based Brain-Computer Interfaces via Deep Neural Networks with Pyramid Squeeze Attention.},
journal = {IEEE transactions on image processing : a publication of the IEEE Signal Processing Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TIP.2026.3684399},
pmid = {42013255},
issn = {1941-0042},
abstract = {Steady state visual evoked potential (SSVEP)-based brain-computer interfaces have been widely studied for their fast response speeds and high information transfer rates. However, how to fully utilize the potential information of existing subjects to realize the mining of common information among different subjects and then realize the information migration in a small amount of data scenarios is a difficult problem faced by current research. In order to solve the above problems, this study proposes a deep neural network based on the pyramid squeeze attention (PSA-DNN) mechanism to enhance the performance of SSVEP-BCI through common information migration. Specifically, the band-pass filtered EEG signals were first Fourier transformed to obtain the frequency domain information; subsequently, the frequency domain information is input into a deep neural network, followed by a spatial convolution step to extract spatial domain information. In order to further enhance the quality of information extraction, a pyramid attention module is introduced into the network to realize the enhancement of frequency domain and spatial domain information. Time domain information from the EEG signals is then mined using temporal convolution. Finally, the full connectivity layer is used to output the recognition results. The model is trained in a three-stage stepped approach for SSVEP target recognition. The first stage uses data from all participants in the training set for common information learning and transfers the model parameters trained in the first stage to the network model in the second stage. In the second stage, some of the information from participants in the test set is used for fine-tuning and to mine personalized information from these new participants. The third stage uses the remaining data from participants in the test set to produce classification results. The proposed method is systematically evaluated using the Benchmark and BETA datasets, where it demonstrates favorable performance compared to established baselines. These findings contribute theoretical insights and methodological references for the application of SSVEP-based brain-computer interfaces in real-world scenarios.},
}

@article {pmid42013272,
year = {2026},
author = {Zhang, J and Liu, J and Wang, L and Peng, Y and Kong, W and Cichocki, A},
title = {BR-SFDA: A Source-Target Bidirectional Refined SFDA for Privacy Preserving EEG-based BCIs.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2026.3686008},
pmid = {42013272},
issn = {2168-2208},
abstract = {Due to significant inter-subject variability in feature distributions caused by the diversity of neural activity patterns, Electroencephalography (EEG)-based brain-computer interface (BCI) systems face considerable challenges in cross-subject EEG decoding. Though transfer learning has been widely introduced for knowledge transfer from source subject(s) to target subject and exhibited great success, a non-negligible issue is that source subjects' EEG data usually contains privacy information and should be protected. To address both issues, we propose a source-target bidirectional refined source-free domain adaptation (BR-SFDA) framework in this paper for privacy preserving cross-subject EEG classification. BR-SFDA makes improvements from two aspects under the popular 'pretraining and fine-tuning' paradigm. On one hand, it locally performs data augmentation and builds a multi-criteria fused metric to select representative EEG sample for model pre-training. On the other hand, a structured graph learning strategy is employed to better guide the model finetuning in a self-supervised manner. Both improvements collaborate respectively from the front-end and back-end, leading to a bidirectional refined SFDA framework. Extensive experiments are conducted on two tasks of cross-subject motor imagery decoding and emotion recognition, and the results on four datasets demonstrate that BR-SFDA achieves superior performance to some competitive models. Besides, the effectiveness of data augmentation and filtering, structured graph learning and domain adaptation is well evaluated.},
}

@article {pmid42013704,
year = {2026},
author = {Han, L and Smagghe, G and Yang, J and Yu, J and Zheng, H and Chen, X and Wu, X},
title = {Synergistic removal of morpholine fungicides and cadmium from agricultural water by a biochar-immobilized bacterial-duckweed system: Quantifying roles of biodegradation, adsorption, and phyto-uptake.},
journal = {Journal of hazardous materials},
volume = {510},
number = {},
pages = {142125},
doi = {10.1016/j.jhazmat.2026.142125},
pmid = {42013704},
issn = {1873-3336},
abstract = {The co-contamination of agricultural water by morpholine fungicides (e.g., flumorph and dimethomorph) and cadmium (Cd) poses significant ecological threats, challenging conventional treatment approaches. This study developed an innovative bioremediation system integrating biochar-immobilized microbial consortia with phytoremediation, and quantified the individual contributions of biodegradation, biosorption, phyto-uptake, and biochar-adsorption to the synergistic removal of pesticide and Cd co-contaminants. A novel Cd-tolerant and flumorph-degrading bacterium, Alcaligenes faecalis X4, was combined with a dimethomorph-degrading strain (Bacillus cereus WL08) to form a stable consortium. This consortium was capable of simultaneously metabolizing both fungicides into less toxic products and adsorbing cadmium. The consortium was immobilized on bamboo charcoal to produce a biocomposite (BCI-X4 + WL08), which achieved high removal efficiencies under optimized conditions: 97.65% for flumorph (50 mg/L), 94.23% for dimethomorph (50 mg/L), and 82.68% for cadmium (10 mg/L). Subsequent introduction of duckweed (Lemna minor) contributed an additional 15.40-28.00% removal via phyto-accumulation. Partitioning analysis confirmed true synergistic interactions-rather than merely additive effects-enhancing overall removal by up to 3.27-fold while alleviating oxidative stress in the plants. A compound ecological filter bed incorporating both BCI-X4 + WL08 and duckweed demonstrated practical applicability under outdoor conditions, achieving average simultaneous removal rates of 94.96% (flumorph), 91.43% (dimethomorph), and 85.42% (Cd) across three consecutive seasons, along with improved water quality parameters. This work presents a scalable, eco-friendly strategy for the in situ remediation of surface waters co-contaminated with pesticides and heavy metals, and provides a quantitative assessment of the distinct microbial, plant, and biochar contributions to the synergistic remediation process.},
}

@article {pmid42014579,
year = {2026},
author = {Miao, Y and Fu, Z and Zhang, J and Tao, Y and Pang, K and Wang, C and Jiang, Q and Shen, L and Xia, T and Lu, P and Xu, Z and Xia, L and Zuo, L and Dong, R and Liu, Y and Wang, Z and Zhang, N and Song, J and Gao, C and Jiang, R and Deng, D and Zhu, Y},
title = {Theoretical quantitative model and clinical outcome predictions of conductive cardiac patches for electrophysiological treatments.},
journal = {Nature biomedical engineering},
volume = {},
number = {},
pages = {},
pmid = {42014579},
issn = {2157-846X},
support = {2024C03074//Science and Technology Department of Zhejiang Province/ ; 2024C03074//Science and Technology Department of Zhejiang Province/ ; 2024C03074//Science and Technology Department of Zhejiang Province/ ; 2024C03074//Science and Technology Department of Zhejiang Province/ ; 2024C03074//Science and Technology Department of Zhejiang Province/ ; 2024C03074//Science and Technology Department of Zhejiang Province/ ; 2024C03074//Science and Technology Department of Zhejiang Province/ ; 2024C03074//Science and Technology Department of Zhejiang Province/ ; 2024C03074//Science and Technology Department of Zhejiang Province/ ; 2024C03074//Science and Technology Department of Zhejiang Province/ ; 2024C03074//Science and Technology Department of Zhejiang Province/ ; 2024C03074//Science and Technology Department of Zhejiang Province/ ; 2024C03074//Science and Technology Department of Zhejiang Province/ ; 2019YFE0117400//Chinese Ministry of Science and Technology | Department of S and T for Social Development (Department of S&T for Social Development)/ ; 2019YFE0117400//Chinese Ministry of Science and Technology | Department of S and T for Social Development (Department of S&T for Social Development)/ ; 12225209//National Natural Science Foundation of China (National Science Foundation of China)/ ; 12225209//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {Myocardial infarction (MI) impairs cardiac electrical signal transmission, which could be partially remedied by implantable electroactive biomaterials. Here we characterize electroactive cardiac patches (eCarPs) with conductivities spanning five orders of magnitude both in vitro and in rat models. In contrast to common belief, we reveal that highly conductive eCarPs are more effective in lowering the risk of post-MI arrhythmia and preserving cardiac function with respect to eCarPs with conductivity similar to normal myocardium. We show that highly conductive eCarPs restore electrical signal conduction velocity across infarcted myocardium to healthy levels, while less conductive eCarPs fail to do this. We quantitatively demonstrate that three-dimensional cardiac simulation based on the monodomain model accurately replicates the effect of high-conductivity patches in eliminating conduction blocks in porcine myocardium and the locations of reentrant circuits in patients with MI. Our results suggest that eCarP conductivity higher than healthy human myocardium is preferred for lowering the risk of arrhythmia in patients by reducing the number of reentrants and stabilizing the reentrant routes.},
}

@article {pmid42014794,
year = {2026},
author = {Zou, J and Poeppel, D and Ding, N},
title = {Constituent-constrained word prediction during language comprehension.},
journal = {Nature neuroscience},
volume = {},
number = {},
pages = {},
pmid = {42014794},
issn = {1546-1726},
abstract = {Next-word prediction has been hypothesized as the central computational objective of the human language system, akin to that of current large language models. Here we put this conjecture to the test, investigating whether the brain predicts each upcoming word as precisely as possible when listening to connected speech. In three magnetoencephalography experiments with Mandarin Chinese speakers, we demonstrate that the response related to word unpredictability, that is, word surprisal calculated using large language models, is significantly stronger for words within an ongoing constituent than words across a major constituent boundary, and this effect is further modulated by the certainty of a constituent boundary. This constituent-boundary effect is also observed behaviorally unless speech is very slowly presented, and it is confirmed by analyzing a dataset of electrocorticography responses to natural English narratives. The constituent-boundary effect demonstrates that the language system does not solely optimize word-prediction precision; rather, it balances word-prediction contributions by constituent-constrained management of linguistic contextual representations.},
}

@article {pmid42014805,
year = {2026},
author = {Nasiraee, H and Nazari, F and Samsami-Khodadad, F and Liu, X},
title = {Neural-LWE: a biometric-anchored authenticated key agreement for post-quantum brain-computer interfaces.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-026-48527-x},
pmid = {42014805},
issn = {2045-2322},
}

@article {pmid42002556,
year = {2026},
author = {Selvam, AK and Loganathan, A},
title = {An intelligent EEG-based ensemble framework for communication assistance in Locked-In Syndrome patients.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-026-47041-4},
pmid = {42002556},
issn = {2045-2322},
}

@article {pmid42003430,
year = {2026},
author = {Maya, I and Noiret, B and Denost, Q},
title = {How to avoid APR after failure of organ preservation in ultra-low rectal cancer? A video vignette.},
journal = {Colorectal disease : the official journal of the Association of Coloproctology of Great Britain and Ireland},
volume = {28},
number = {4},
pages = {e70458},
doi = {10.1111/codi.70458},
pmid = {42003430},
issn = {1463-1318},
}

@article {pmid42004294,
year = {2024},
author = {Collyer, J},
title = {Fasciola hepatica: can the coproantigen ELISA replace the faecal egg sedimentation test?.},
journal = {Veterinary evidence},
volume = {9},
number = {4},
pages = {},
pmid = {42004294},
issn = {2396-9776},
abstract = {PICO QUESTION: In adult cattle, is the sensitivity of the coproantigen ELISA test equal or superior to the sensitivity of the faecal egg sedimentation test for the diagnosis of Fasciola hepatica?

CATEGORY OF RESEARCH: Diagnosis.

Three studies were appraised. This included two cross-sectional diagnostic accuracy studies and one case control diagnostic accuracy study.

STRENGTH OF EVIDENCE: Moderate.

OUTCOMES REPORTED: The first study reported the findings from 619 tested cattle over 3 sample periods comparing the sensitivity and specificity of the different tests. The sensitivity of the faecal egg sedimentation test varied greatly between the sample periods from 0.81 (95% beta coefficient (BCI) 0.72-0.90) to 0.58 (95% BCI 0.43-0.72) with the coproantigen ELISAs sensitivity remaining consistent at 0.77 (95% BCI 0.64-0.88) throughout.The second study reported the findings of 200 tested cattle over 2 sampling periods comparing the sensitivity and specificity of the different tests. The mean sensitivity of the coproantigen ELISA was significantly higher than the 4 g/10 g preparations of the faecal egg sedimentation tests at 94% (95% CI 87%-98%) (P < 0.001). The third study reported the findings of Coproantigen ELISA testing on 250 bovine faecal samples with 94 confirmed positive for liver fluke via faecal sedimentation testing. The sensitivity of the coproantigen ELISA was 80% and the specificity was 100% compared with 70% and 80% respectively for the faecal egg sedimentation test.

CONCLUSION: All three studies demonstrated either an increased or equivalent sensitivity of the coproantigen ELISA to the faecal sedimentation test, but only one study reported a statistically significant increase in test sensitivity. Whilst all three studies were diagnostic accuracy validity studies, the systematic sampling strategy of one study was superior to the convenience sampling method of one of the other studies and to the case control method of the other.Several sources of bias also exist within the included studies. Sampling and selection bias is present in the two of studies due to the animals selected only being sampled over one year. The results of these studies are susceptible to changes in the fluke lifecycle of that year, and the sampled animals are more likely to be fit and well-conditioned as they are presenting for slaughter, and as such are less likely to carry significant/chronic fluke burdens. All three studies are susceptible to validity issues due to an absence of clinical information regarding flukicide treatment and concurrent parasitic diseases which, whilst not impacting the efficacy of diagnostic testing, may cause issues if the studies are to be repeated.The coproantigen ELISA can be utilised as a suitable adjunctive test to aid in the diagnosis of Fasciola hepatica in adult cattle and has the scope to be used as an early diagnostic test, but whilst the results of the reported studies indicate that the coproantigen ELISA is an accurate and reliable test, it does not provide definitive evidence to warrant the discontinuation of the simple and affordable faecal egg sedimentation test. In order to come to a conclusion regarding the more sensitive test more literature is required that directly compares the coproantigen ELISA to the faecal egg sedimentation test in different clinical scenarios and exploring different diagnostic techniques.},
}

@article {pmid42004547,
year = {2026},
author = {Andong, FA and Mayowa, ES and Nwanozie, PO and Ejere, VC and Afyare, AAA},
title = {Double burden: microfilariae infection amplifies metabolic costs of moult in breeding male village weavers (Ploceus cucullatus).},
journal = {Biochemistry and biophysics reports},
volume = {46},
number = {},
pages = {102576},
pmid = {42004547},
issn = {2405-5808},
abstract = {Breeding male birds face high energetic demands due to simultaneous investment in reproduction and feather moult, yet the metabolic consequences of parasitic infection during this period are poorly understood. To address this gap, we focused on non-moulting and actively moulting breeding adult male village weavers (Ploceus cucullatus) to investigate how microfilariae infection affects host biochemical energy status and overall condition. Using plasma glucose, triglycerides, β-hydroxybutyrate, and body mass adjusted for structural size as integrative markers, we examined how infection influences energy allocation and imposes physiological costs during this critical life-history stage. Specifically, we aimed to: (i) determine whether microfilariae infection and active moult influence short-term energy availability by examining plasma glucose concentrations, and whether absolute body mass modulates the effect of infection; and (ii) evaluate the combined and independent effects of infection and moult on lipid and ketone metabolism, while incorporating absolute body mass and size-corrected body condition index (BCI) to assess overall energetic reserves and physiological trade-offs. A total of 128 breeding males were trapped and screened for microfilariae and moult status. Our results indicate infected birds that are actively moulting experienced higher β-hydroxybutyrate, lower glucose and reduced BCI, when compared with the non-infected birds that were non-moulting. On the other hand, non-infected male birds that were also non-moulting maintained higher triglyceride levels. Our regression analyses indicate both infection and moult independently increased ketone concentrations and decreased triglycerides (P < 0.05), with no significant interaction for most markers. However, for β-hydroxybutyrate, the interaction may approach significance (P = 0.08), which suggest a marginal tendency toward non-additive effects. These results highlight a 'double burden,' where concurrent parasitism and moult constrain energy allocation, shifting metabolism from carbohydrates toward lipid catabolism. This study may provide mechanistic insight into how microfilariae infection amplifies energetic costs during high-demand life-history stages in breeding male village weavers.},
}

@article {pmid42006915,
year = {2026},
author = {Taquet, L and Conway, BJ and Boerger, TF and Goetschel, K and Young, SC and Botros, NE and Raghavan, M and Schmit, BD and Krucoff, MO},
title = {The frequency-dependent effects of primary hand motor cortex stimulation on volitional finger movement.},
journal = {Clinical neurophysiology practice},
volume = {11},
number = {},
pages = {252-261},
pmid = {42006915},
issn = {2467-981X},
abstract = {OBJECTIVE: We conducted a prospective study in human patients undergoing awake craniotomies to examine whether the effects of cortical stimulation in hand primary motor cortex (M1) can be (1) frequency dependent and (2) inhibitory.

METHODS: In 11 participants undergoing clinically indicated awake craniotomies, we delivered bursts of 1-333 Hz stimulation during a finger-flexion task. Synchronized electrocorticography (ECoG), finger joint kinematics, electromyography (EMG), and video were recorded.

RESULTS: Inability to flex the index finger during subthreshold stimulation was noted in 3 participants at frequencies >250 Hz when the electrodes were in locations that induced extension of the forefinger at higher amplitudes. Other than these trials, all stimulation events either induced muscle contractions or had no measurable effect.

CONCLUSION: Data presented here represent the first evidence of (1) movement inhibition of the human hand caused by electrical stimulation of M1, as well as (2) the frequency-dependence of net downstream effects of hand M1 stimulation during task. Our findings support the hypothesis that the mechanism of movement inhibition may be activation of indirect, net-inhibitory mechanisms, as opposed to direct inhibition of the stimulated motor neurons.

SIGNIFICANCE: There is growing interest in using continuous electrical stimulation of the brain to remap anatomical-functional relationships away from invasive lesions. Achieving this type of neuroplasticity requires a better understanding of the direct and indirect effects of cortical stimulation. Here we demonstrate the frequency-dependent effects of cortical M1 stimulation on volitional finger movement.},
}

@article {pmid41253390,
year = {2026},
author = {Ji, X and Deng, S},
title = {Cognitive Change as an Early Warning for Late-Life Depression: Implications for Population Health Screening Strategies.},
journal = {Population health management},
volume = {29},
number = {1},
pages = {27-37},
doi = {10.1177/19427891251395738},
pmid = {41253390},
issn = {1942-7905},
mesh = {Humans ; Aged ; Male ; Female ; *Depression/diagnosis/epidemiology ; Longitudinal Studies ; Aged, 80 and over ; *Population Health ; *Cognitive Dysfunction/diagnosis ; *Mass Screening/methods ; *Cognition ; },
abstract = {Cognitive decline and late-life depression are intertwined public health challenges for aging populations globally. To inform effective prevention, the current study investigated the dynamic temporal associations between multidimensional cognitive functions and depressive symptoms. Using four waves of longitudinal data (2013-2020) from a large panel study of older adults, the current study employed an integrated framework combining optimized dynamic time warping, cross-lagged panel models, and network analysis to model complex, lagged relationships. Results provided strong empirical support for the "cognition-first" hypothesis, with declines in several cognitive domains-notably temporal orientation, calculation, and immediate recall-acting as significant upstream predictors of subsequent depressive symptoms. A modest but significant protective feedback effect from positive affect to cognitive maintenance was also identified, while negative affect showed no significant predictive role sample of older adults who were cognitively and emotionally healthy at baseline. These findings offer preliminary empirical support for a strategic shift in population health management from reactive treatment toward proactive prevention. Based on these results, the current study discusses a conceptual framework for integrating cognitive screening into primary care to identify at-risk older adults, an approach that warrants further investigation and validation. This proactive approach could enable timely, low-cost interventions aimed at promoting positive affect and cognitive resilience, offering a potentially cost-effective strategy to mitigate the long-term burden of mental illness and advance the goals of healthy aging.},
}

Humans
Aged
Male
Female
*Depression/diagnosis/epidemiology
Longitudinal Studies
Aged, 80 and over
*Population Health
*Cognitive Dysfunction/diagnosis
*Mass Screening/methods
*Cognition