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Depression, a mental health disorder, has emerged as one of the significant challenges in the global public health domain. Investigating the pathogenesis of depression and accurately assessing the symptomatic changes are fundamental to formulating effective clinical diagnosis and treatment strategies. Utilizing non-invasive brain imaging technologies such as functional magnetic resonance imaging and scalp electroencephalography, existing studies have confirmed that the onset of depression is closely associated with abnormal neural activities and altered functional connectivity in multiple brain regions. Magnetoencephalography, unaffected by tissue conductivity and skull thickness, boasts high spatial resolution and signal-to-noise ratio, offering unique advantages and significant value in revealing the abnormal brain mechanisms and neural characteristics of depression. This review, starting from the rhythmic characteristics, nonlinear dynamic features, and connectivity characteristics of magnetoencephalography in depression patients, revisits the research progress on magnetoencephalography features related to depression, discusses current issues and future development trends, and provides insights for the study of pathophysiological mechanisms, as well as for clinical diagnosis and treatment of depression.

Motor dysfunction is one of the most significant sequelae of stroke, with lower limb impairment being a major concern for stroke patients. Motor imagery (MI) technology based on brain-computer interface (BCI) offers promising rehabilitation potential for stroke patients by activating motor-related brain areas. However, developing a robust BCI-MI system and uncovering the underlying mechanisms of neural plasticity during stroke recovery through such systems requires large-scale datasets. These datasets are particularly needed for accurate lower limb MI in stroke patients and for longitudinal data reflecting the rehabilitation process. This study addresses this gap by collecting EEG data from 27 stroke patients, covering two enhanced paradigms and three different time points. The dataset includes raw EEG signals, preprocessed data, and patient information. An initial analysis using CSP-SVM on the dataset yielded an average classification accuracy of 80.50%. We anticipate that this dataset will facilitate research into brain neuroplasticity in stroke patients, aid in the development of decoding algorithms for lower limb stroke, and contribute to the establishment of comprehensive stroke rehabilitation systems., Competing Interests: Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)

Objective . Steady-state visual evoked potential-based brain-computer interfaces (SSVEP-BCIs) have gained significant attention due to their simplicity, high signal to noise ratio and high information transfer rates (ITRs). Currently, accurate detection is a critical issue for enhancing the performance of SSVEP-BCI systems. Approach. This study proposed a novel decoding method called Discriminant Compacted Network (Dis-ComNet), which exploited the advantages of both spatial filtering and deep learning (DL). Specifically, this study enhanced SSVEP features using global template alignment and discriminant spatial pattern, and then designed a compacted temporal-spatio module (CTSM) to extract finer features. The proposed method was evaluated on a self-collected high-frequency dataset, a public Benchmark dataset and a public wearable dataset. Main Results. The results showed that Dis-ComNet significantly outperformed state-of-the-art spatial filtering methods, DL methods, and other fusion methods. Remarkably, Dis-ComNet improved the classification accuracy by 3.9%, 3.5%, 3.2%, 13.3%, 17.4%, 37.5%, and 2.5% when comparing with eTRCA, eTRCA-R, TDCA, DNN, EEGnet, Ensemble-DNN, and TRCA-Net respectively in the high-frequency dataset. The achieved results were 4.7%, 4.6%, 23.6%, 52.5%, 31.7%, and 7.0% higher than those of eTRCA, eTRCA-R, DNN, EEGnet, Ensemble-DNN, and TRCA-Net, respectively, and were comparable to those of TDCA in Benchmark dataset. The accuracy of Dis-ComNet in the wearable dataset was 9.5%, 7.1%, 36.1%, 26.3%, 15.7% and 4.7% higher than eTRCA, eTRCA-R, DNN, EEGnet, Ensemble-DNN, and TRCA-Net respectively, and comparable to TDCA. Besides, our model achieved the ITRs up to 126.0 bits/min, 236.4 bits/min and 103.6 bits/min in the high-frequency, Benchmark and the wearable datasets respectively. Significance. This study develops an effective model for the detection of SSVEPs, facilitating the development of high accuracy SSVEP-BCI systems., (© 2025 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.)

Background: Transcutaneous auricular vagus nerve stimulation (taVNS) has emerged as a potential modulator of cognitive behavior that activates the locus coeruleus-noradrenaline (LC-NA) system. Previous studies explored both phasic and tonic taVNS by investigating their impact on LC-NA markers such as pupil dilation and heart rate variability (HRV)., Objective: Inconsistencies persist in the identification of reliable markers for assessing the effects of taVNS on noradrenergic activity. Furthermore, it remains unclear whether the effects of taVNS extend beyond pure vagal nerve responses, particularly in specific cognitive domains such as working memory. In the present study, we investigated the effects of taVNS on working memory capacity and LC-NA markers using a change-detection task., Materials and Methods: Twenty-two healthy, right-handed university students participated in a sham-controlled, randomized cross-over experiment with four sessions. We applied two types of phasic and event-related stimulation (Pre-event and Event-synchronous), tonic stimulation (Pre-task), and sham stimulation across different sessions. Pupil size and electrocardiogram data were recorded during the tasks., Results: taVNS did not significantly modulate behavioral performance on the change-detection task, specifically working memory capacity. However, both tonic and event-related phasic taVNS significantly influenced the pupillary response during the task. In addition, the Pre-task condition of the taVNS affected the low-frequency parameter of HRV., Conclusions: Our findings suggest that tonic and event-related phasic taVNS may modulate noradrenergic activity, as evidenced by pupil responses and HRV changes during the change-detection task. This study provides new evidence regarding the impact of taVNS on cognitive tasks, thus supporting the development of noninvasive neuromodulation interventions., Competing Interests: Conflict of Interest The authors reported no conflict of interest., (Copyright © 2025 International Neuromodulation Society. Published by Elsevier Inc. All rights reserved.)

Considering that the human microbiota plays a critical role in health and disease, an accurate and high-resolution taxonomic classification is thus essential for meaningful microbiome analysis. In this study, we developed an automatic system, named MultiTax pipeline, for generating de novo taxonomy from full-length 16S rRNA sequences using the Genome Taxonomy Database and other existing reference databases. We first constructed the MultiTax-human database, a high-resolution resource specifically designed for human microbiome research and clinical applications. The database includes 842,649 high-quality full-length 16S rRNA sequences, extracted from multiple public repositories and human-related studies, offering a comprehensive and accurate portrayal of the human microbiome. To validate the MultiTax-human database, we profiled the human microbiome across various body sites, identified core microbial taxa, and tested its performance using an independent data set. Additionally, the database is equipped with a user-friendly web interface for easy querying and data exploration. The MultiTax-human database is poised to serve as a valuable tool for researchers, enhancing the precision of human microbiome studies and advancing our understanding of its impact on human health and diseases.IMPORTANCEUnderstanding the human microbiome, the collection of microorganisms in and on our bodies, is essential for advancing health research. Current methods often lack precision and consistency, hindering our ability to study these microorganisms effectively. Our study presents the MultiTax-human database, a high-resolution reference tool specifically designed for human microbiome research. By integrating data from multiple sources and employing advanced classification techniques, this database offers an accurate and detailed map of the human microbiome. This resource enhances the ability of researchers and clinicians to explore the roles of microorganisms in health and disease, potentially leading to improved diagnostics, treatments, and insights into various medical conditions., Competing Interests: The authors declare no conflict of interest.

Background: Abnormalities in structural-functional connectivity (SC-FC) coupling have been identified globally in patients with major depressive disorder (MDD). However, investigations have neglected the variability and hierarchical distribution of these abnormalities across different brain regions. Furthermore, the biological mechanisms that underlie regional SC-FC coupling patterns are not well understood., Methods: We enrolled 182 patients with MDD and 157 healthy control participants and quantified the intergroup differences in regional SC-FC coupling. Extreme gradient boosting (XGBoost), support vector machine, and random forest models were constructed to assess the potential of SC-FC coupling as biomarkers for MDD diagnosis and symptom prediction. Then, we examined the link between changes in regional SC-FC coupling in patients with MDD, neurotransmitter distributions, and gene expression., Results: We observed increased regional SC-FC coupling in the default mode network (t 337  = 3.233) and decreased coupling in the frontoparietal network (t 337  = -3.471) in patients with MDD compared with healthy control participants. XGBoost (area under the receiver operating characteristic curve = 0.853), support vector machine (area under the receiver operating characteristic curve = 0.832), and random forest (p < .05) models exhibited good prediction performance. The alterations in regional SC-FC coupling in patients with MDD were correlated with the distributions of 4 neurotransmitters (p < .05) and expression maps of specific genes. These enriched genes were implicated in excitatory neurons, inhibitory neurons, cellular metabolism, synapse function, and immune signaling. These findings were replicated on 2 brain atlases., Conclusions: This work enhances our understanding of MDD and paves the way for the development of additional targeted therapeutic interventions., (Copyright © 2024 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

Gamma oscillations are essential for brain communication. The 40 Hz neural oscillation deficits in schizophrenia impair left frontotemporal connectivity and information communication, causing auditory hallucinations. Transcranial alternating current stimulation is thought to enhance connectivity between different brain regions by modulating brain oscillations. In this work, we applied a frontal-temporal-parietal 40 Hz-tACS stimulation strategy for treating auditory hallucinations and further explored the effect of tACS on functional connectivity of brain networks. 32 schizophrenia patients with refractory auditory hallucinations received 20daily 20-min, 40 Hz, 1 mA sessions of active or sham tACS on weekdays for 4 consecutive weeks, followed by a 2-week follow-up period without stimulation. Auditory hallucination symptom scores and 64-channel electroencephalograms were measured at baseline, week2, week4 and follow-up. For clinical symptom score, we observed a significant interaction between group and time for auditory hallucinations symptoms (F(3,90) = 26.964, p < 0.001), and subsequent analysis showed that the 40Hz-tACS group had a higher symptom reduction rate than the sham group at week4 (p = 0.036) and follow-up (p = 0.047). Multiple comparisons of corrected EEG results showed that the 40Hz-tACS group had higher functional connectivity in the right frontal to parietal (F (1,30) = 7.24, p = 0.012) and right frontal to occipital (F (1,30) = 7.98, p = 0.008) than the sham group at week4. Further, functional brain network controllability outcomes showed that the 40Hz-tACS group had increased average controllability (F (1,30) = 6.26, p = 0.018) and decreased modality controllability (F (1,30) = 6.50, p = 0.016) in the right frontal lobe compared to the sham group. Our polit study indicates that 40Hz-tACS combined with medicine may be an effective treatment for targeting symptoms specific to auditory hallucinations and altering functional connectivity and controllability at the network level., Competing Interests: Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)

Hydrocephalus is a prevalent neurological disorder, particularly impactful in older adults, characterized by high incidence and numerous complications that impose a significant burden on healthcare systems. This review aims to provide a comprehensive description of hydrocephalus pathogenesis, focusing on cellular and molecular insights derived from animal models. We also present the latest advances in hydrocephalus research and highlight potential therapeutic targets. Lastly, the review advocates the integration of findings from both animal and human studies to achieve better outcomes and examines the potential of emerging technologies. We wish to raise public attention about this disease in an aging society. Current animal models for hydrocephalus involve acquired hydrocephalus models and genetic/congenital hydrocephalus models. Studies from animals have shown that the main mechanisms of models can be broadly classified into nine types. A variety of drug-targeted therapy methods and non-surgical treatment methods have been used in clinical practice. But current treatment approaches primarily focus on symptomatic relief and intracranial pressure control rather than addressing the underlying pathological mechanisms. We call for the development of more accurate and representative animal models to achieve better outcomes and examine the potential of emerging technologies, such as artificial intelligence and neuroimaging. In summary, this review synthesizes recent findings in hydrocephalus research, identifies promising therapeutic targets and interventions, and critically evaluates the limitations of current research paradigms, aiming to align preclinical studies with clinical endpoints. Continued studies and multidisciplinary collaboration are essential to develop effective interventions and facilitate new treatments into bedside.

Generalization is central to motor learning. However, few studies are on the learning generalization of BCI-actuated supernumerary robotic finger (BCI-SRF) for human-machine interaction training, and no studies have explored its longitudinal neuroplasticity mechanisms. Here, 20 healthy right-handed participants were recruited and randomly assigned to BCI-SRF group or inborn finger group (Finger) for 4-week training and measured by novel SRF-finger opposition sequences and multimodal MRI. After training, the BCI-SRF group showed 350% times compared to the Finger group in the improvement of sequence opposition accuracy before and after training, and accompanied by significant functional connectivity increases in the sensorimotor region and prefrontal cortex, as well as in the intra- and inter-hemisphere of the sensorimotor network. Moreover, Granger Causality Analysis identified causal effect main transfer within the sensorimotor cortex-cerebellar-thalamus loop and frontal-parietal loop. The findings suggest that BCI-SRF training enhances motor sequence learning ability by influencing the functional reorganization of sensorimotor network., Competing Interests: Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

The gut microbiome, recognized as a critical component in the development of chronic diseases and aging processes, constitutes a promising approach for predicting host health status. Previous research has underscored the potential of microbiome-based predictions, and the rapid advancements of machine learning techniques have introduced new opportunities for exploiting microbiome data. To predict various host nonhealthy conditions, this study proposed an integrated machine learning-based estimation pipeline of Gut Age Index (GAI) by establishing a health aging baseline with the gut microbiome data from healthy individuals. We assessed the performance of GAI pipeline on two extensive cohorts - the Guangdong Gut Microbiome Project (GGMP) and the American Gut Project (AGP). In the GGMP cohort, for 20 common chronic diseases such as metabolic syndrome, obesity, and cardiovascular diseases, the proposed GAI achieved a balanced accuracy, ranging from 66 to 75%, with the prediction performance for atherosclerosis being the highest. In the AGP cohort, the balanced accuracy of GAI ranged from 58 to 72% for 10 diseases. Based on the results from these two datasets, we conclude that our proposed approach in this study can be used to predict individual health status, which offers the potential for scalable, cost-effective, and personalized health insights., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

With the high incidence of neurological diseases such as stroke and mental illness, rehabilitation treatments for neurological disorders have received widespread attention. Electroencephalography (EEG) technology, despite its excellent temporal resolution, has historically been limited in application due to its insufficient spatial resolution, and is mainly confined to preoperative assessment, intraoperative monitoring, and epilepsy detection. However, traditional constraints of EEG technology are being overcome with the popularization of EEG technology with high-density over 64-lead, the application of innovative analysis techniques and the integration of multimodal techniques, which are significantly broadening its applications in clinical settings. These advancements have not only reinforced the irreplaceable role of EEG technology in neurorehabilitation assessment, but also expanded its therapeutic potential through its combined use with technologies such as transcranial magnetic stimulation, transcranial electrical stimulation and brain-computer interfaces. This article reviewed the applications, advancements, and future prospects of EEG technology in neurorehabilitation assessment and treatment. Advancements in technology and interdisciplinary collaboration are expected to drive new applications and innovations in EEG technology within the neurorehabilitation field, providing patients with more precise and personalized rehabilitation strategies.

Enzymatic degradation offers a promising eco-friendly solution to plastic pollution, especially for polyethylene terephthalate (PET). Current efforts have focused on screening PET-degrading enzymes from microbial and metagenomic sources and obtaining superior candidates with a limited set of templates. More efficient PET hydrolases are required for PET-waste biorefinery. Here, using a structure-guided bioinformatic workflow, we identified a novel PET hydrolase, LSPET4, from Micromonospora sp. HM5-17, by screening the AlphaFold protein structure database. LSPET4 features a unique carbohydrate-binding module (CBM) and a distinctive linear substrate binding conformation. The intrinsic CBM in LSPET4 exhibited superior binding ability on PET surfaces and enhanced PET hydrolysis performance compared to the previously reported most effective CBM3. Through rational protein engineering focused on stabilizing and modifying the linear substrate binding conformation, we developed LSPET4 M6 (D130P, N127F, Y96F, Q209E, A238K, D241S), a variant that achieved a 38.79-fold improvement in activity compared to the wild type, and was comparable to the reported most effective PET hydrolase derived from IsPETase, FAST-PETase at 45 ℃. This variant also demonstrated effectiveness in degrading various commercial PET materials, including PET food sealing films, PET strawberry boxes, and PET tomato boxes used in the food industry. This study not only provides a new template for protein engineering endeavors to create efficient biocatalysts for PET recycling but also offers an effective enzyme discovery approach to uncover enzymes of interest from the AlphaFold protein structure database., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background: Stroke is one of the leading causes of disability and death worldwide. Ischemic stroke accounts for 75-90% of all stroke incidents. Assessing the size and location of the stroke lesion is crucial for treatment decisions, especially those related to urgent vascular reconstruction surgery. Magnetic resonance imaging (MRI) offers excellent soft tissue contrast and multimodal imaging characteristics, which can reflect changes in the physiological functions of brain soft tissues in patients with ischemic stroke. However, using deep learning (DL) techniques for MRI segmentation of stroke lesions still faces many challenges. On the one hand, single-modal segmentation models cannot effectively integrate multimodal information; on the other hand, there is a semantic content drift between multimodal stroke MRI images, leading to lower accuracy in subsequent multimodal image segmentation. To address these issues, we aimed to propose the stroke unsupervised registration and segmentation (St-RegSeg) framework., Methods: The St-RegSeg framework integrates an unsupervised registration model, ConvNXMorph, and a segmentation model, nnUNet-v2, enabling both registration and segmentation of multimodal MRI images. The St-RegSeg framework was evaluated on the ISLES'22 dataset from three centers., Results: The St-RegSeg framework demonstrated significant improvements in performance metrics and computational efficiency. Compared to advanced normalization tools (ANTs) [symmetric normalization (SyN)] + nnUNet-v2, the St-RegSeg framework improved the Dice similarity coefficient (DSC) by 25.31% in the registration phase, reduced mean squared error (MSE) by 17.36%, increased normalized cross-correlation (NCC) by 16.06%, and enhanced mutual information (MI) by 17.09%. Additionally, in the segmentation phase, it increased the DSC by 0.84%, and the overall inference speed was increased by 40.91 times. Compared to the suboptimal TransMorph + nnUNet-v2, the St-RegSeg framework improved the DSC by 3.68% in the registration phase, reduced MSE by 8.91%, increased NCC by 8.49%, enhanced MI by 6.18%, and in the segmentation phase, it raised the DSC by 0.5%, with the overall inference speed increased by 2.13 times., Conclusions: The St-RegSeg framework provides a highly effective solution for the registration and segmentation of multimodal MRI images in ischemic stroke cases. Its performance metrics and computational efficiency significantly outperform existing methods, making it a promising tool for clinical applications. The code is open-sourced and available at: https://github.com/Cooper-Gu/St-RegSeg., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-24-725/coif). The authors have no conflicts of interest to declare., (2024 AME Publishing Company. All rights reserved.)

Aconitum carmichaelii Debx., a traditional herb known for its potent bioactivities, has been widely used in Traditional Chinese Medicine, particularly in the forms of Chuanwu and Fuzi. Despite the therapeutic benefits of this plant, concerns have been raised regarding its neuropharmacological actions and potential neurotoxicity. This paper provides an in-depth analysis of the neuropharmacological effects, neurotoxicological mechanisms, and toxicity biomarkers of Aconitum roots. The neuropharmacological properties are linked to alterations in neurotransmitter synthesis and ion transport modulation, while the neurotoxic effects are primarily attributed to oxidative stress responses and the induction of mitochondrial apoptosis pathways. Through metabolomic profiling, we have identified several metabolic pathways affected by Aconitum roots, with a significant impact on tryptophan metabolism, which in turn influences cardiovascular and nervous system functions, liver detoxification, and energy metabolism. Furthermore, we discuss the modulation of ion channel protein activity, which is evidenced by recent studies, suggesting a critical role in the neurotoxic effects of Aconitum. An early detection strategy for toxicity biomarkers using metabonomics is proposed, emphasizing its crucial role in enhancing the diagnosis and treatment of Aconitum poisoning. It is recommended that regular monitoring of individuals at risk of Aconitum toxicity, including habitual consumers of TCM and accidental ingestion of the plant, be conducted in order to prevent toxic outcomes. This review emphasizes the importance of understanding the dual nature of Aconitum as both a therapeutic agent and a potential neurotoxin, aiming to optimize its clinical use and ensure patient safety., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Masson SAS.. All rights reserved.)

Hydrocephalus is a severe and life-threatening disease associated with the imbalance of CSF dynamics and affects millions globally at any age, including infants. One cause of pathology that is wide-ranging is genetic mutations to post-traumatic injury. The most effective current pharmacological treatments provide only symptomatic relief and do not address the underlying pathology. At the same time, surgical procedures such as VP shunts performed in lower-income countries are often poorly tolerated due to insufficient diagnostic resources and suboptimal outcomes partially attributable to inferior materials. These problems are compounded by an overall lack of funding that keeps high-quality medical devices out of reach for all but the most developed countries and even among those states. There is a massive variance in treatment effectiveness. This review indicates the necessity for innovative and low-cost, accessible treatment strategies to close these gaps, focusing on current advances in novel therapies, including Pharmacological, gene therapy, and nano-based technologies, which are currently at different stages of clinical trial phases. This review provides an overview of pathophysiology, current treatments, and promising new therapeutic strategies for hydrocephalus., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Masson SAS.. All rights reserved.)

Functional coating materials have found extensive applications across various technological fields. However, the effectiveness of these coating depends critically on the choice of an appropriate medium. In this study, we developed an advanced "molecular glue", a CsgA variant known as CsgA-pro, which can serve as a versatile medium for biotherapy. CsgA-pro exhibits a remarkable temperature-driven transition, forming millimeter-scale protein fibers at low temperatures and transitioning to a "molecular glue" state at elevated temperatures. To demonstrate its potential as a "molecular glue", CsgA-pro-GFP was first immobilized onto a gauze surface, which was subsequently treated with chloroauric acid to generate gauze loaded with gold nanoparticles (Au NPs). These Au NPs were shown to possess potent antibacterial properties and to promote migration of L929 and HUVEC cells. When applied to wound healing, the Au NP-loaded gauze significantly accelerated the healing process compared to control groups, highlighting the therapeutic potential of this approach. These findings underscore the promise of CsgA-pro as a multifunctional tool in a wide range of applications, including multi-enzyme biocatalysis and immobilization. We envision that CsgA-pro will have broad applicability across diverse fields, offering substantial contributions to both technological innovations and biomedical advancements., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2025 Elsevier B.V. All rights reserved.)

Purpose: To elucidate the structural-functional connectivity (SC-FC) coupling in white matter (WM) tracts in patients with major depressive disorder (MDD)., Methods: A total of 178 individuals diagnosed with MDD and 173 healthy controls (HCs) were recruited for this study. The Euclidean distance was calculated to assess SC-FC coupling. The primary analyses focused on investigating alterations in SC-FC coupling in WM tracts of individuals with MDD. Additionally, we explored the association between coupling and clinical symptoms. Secondary analyses examined differences among three subgroups of MDD: those with suicidal ideation (SI), those with a history of suicidal attempts (SA), and those non-suicidal (NS)., Results: The study revealed increased SC-FC coupling mainly in the middle cerebellar peduncle and bilateral corticospinal tract (P FDR  < 0.05) in patients with MDD compared with HCs. Additionally, right cerebral peduncle coupling strength exhibited a significant positive correlation with Hamilton Anxiety Scale scores (r = 0.269, P FDR  = 0.041), while right cingulum (hippocampus) coupling strength showed a significant negative correlation with Nurses' Global Assessment of Suicide Risk scores (r = -0.159, P FDR  = 0.036). An increase in left anterior limb of internal capsule (P Bonferroni  < 0.01) and left corticospinal tract (P Bonferroni  < 0.05) coupling has been observed in MDD with SI. Additionally, a decrease in right posterior limb of internal capsule coupling has been found in MDD with SA (P Bonferroni  < 0.05)., Conclusions: This study emphasizes the variations in SC-FC coupling in WM tracts in individuals with MDD and its subgroups, highlighting the crucial role of WM networks in the pathophysiology of MDD., Competing Interests: Declaration of competing interest All authors declare that they have no competing interests., (Copyright © 2025 Elsevier B.V. All rights reserved.)

Generalized anxiety disorder (GAD) is a common anxiety disorder characterized by excessive, uncontrollable worry and physical symptoms such as difficulty concentrating and sleep disturbances. Although functional magnetic resonance imaging (fMRI) studies have reported aberrant network-level activity related to cognition and emotion in GAD, its low temporal resolution restricts its ability to capture the rapid neural activity in mental processes. EEG microstate analysis offers millisecond-resolution for tracking the dynamic changes in brain electrical activity, thereby illuminating the neurophysiological mechanisms underlying the cognitive and emotional dysfunctions in GAD. This study collected 64-channel resting-state EEG data from 28 GAD patients and 28 healthy controls (HC), identifying five microstate classes (A-E) in both groups. Results showed that GAD patients exhibited significantly lower duration (p < 0.01), occurrence (p < 0.05), and coverage (p < 0.01) of microstate class D, potentially reflecting deficits in attention-related networks. Such alterations may contribute to the impairments in attention maintenance and cognitive control. Additionally, GAD patients displayed reduced transition probabilities in A → D, B → D, C → D, and E → D (all corrected p < 0.05), but increased in C → E (corrected p < 0.05) and E → C (corrected p < 0.01). These results highlight a significant reduction in the brain's ability to transition into microstate class D, alongside overactivity in switching between the default mode network and the salience network. Such neurophysiological changes may underlie cognitive control deficits, increased spontaneous rumination, and emotional regulation challenges observed in GAD. Together, these insights provide a new perspective for understanding the neurophysiological and pathological mechanisms underlying GAD., Competing Interests: Declaration of competing interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2025 Elsevier B.V. All rights reserved.)

The Artificial Intelligence (AI) technology holds immense potential in the realm of automated diagnosis for Major Depressive Disorder (MDD), yet it is not without potential shortcomings. This paper systematically reviews the research progresses of integrating AI technology with depression diagnosis and provides a comprehensive analysis of existing research findings. In this context, we observe that the knowledge-driven first-generation of depression diagnosis methods could only address deterministic issues in structured information, with the selection of depression-related features directly influencing identification outcomes. The data-driven second-generation of depression diagnosis methods achieved automatic learning of features but required substantial high-quality clinical data, and the results were often obtained solely from the black-box models which lack sufficient explainability. In an effort to overcome the limitations of the preceding approaches, the third-generation of depression diagnosis methods combined the strengths of knowledge-driven and data-driven approaches. Through the fusion of information, the diagnostic accuracy is greatly enhanced, but the interpretability remains relatively weak. In order to enhance interpretability and introduce diagnostic criteria, this paper offers a new approach using Large Language Models (LLMs) as AI agents for assisting the depression diagnosis. Finally, we also discuss the potential advantages and challenges associated with this approach. This newly proposed innovative approach has the potential to offer new perspectives and solutions in the diagnosis of depression., Competing Interests: Authors HL and YD were employed by Shanxi Tongchuang Technology Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2024 Liu, Wu, Yang, Ren, Dong, Zhang and Li.)

The reconstruction of damaged neural circuits is critical for neurological repair after brain injury. Classical brain-computer interfaces (BCIs) allow direct communication between the brain and external controllers to compensate for lost functions. Importantly, there is increasing potential for generalized BCIs to input information into the brains to restore damage, but their effectiveness is limited when a large injured cavity is caused. Notably, it might be overcome by transplantation of brain organoids into the damaged region. Here, we construct innovative BCIs mediated by implantable organoids, coined as organoid-brain-computer interfaces (OBCIs). We assess the prolonged safety and feasibility of the OBCIs, and explore neuroregulatory strategies. OBCI stimulation promotes progressive differentiation of grafts and enhances structural-functional connections within organoids and the host brain, promising to repair the damaged brain via regenerating and regulating, potentially directing neurons to preselected targets and recovering functional neural networks in the future., (© 2024. The Author(s).)

Human brain organoids represent a remarkable platform for modelling neurological disorders and a promising brain repair approach. However, the effects of physical stimulation on their development and integration remain unclear. Here, we report that low-intensity ultrasound significantly increases neural progenitor cell proliferation and neuronal maturation in cortical organoids. Histological assays and single-cell gene expression analyses revealed that low-intensity ultrasound improves the neural development in cortical organoids. Following organoid grafts transplantation into the injured somatosensory cortices of adult mice, longitudinal electrophysiological recordings and histological assays revealed that ultrasound-treated organoid grafts undergo advanced maturation. They also exhibit enhanced pain-related gamma-band activity and more disseminated projections into the host brain than the untreated groups. Finally, low-intensity ultrasound ameliorates neuropathological deficits in a microcephaly brain organoid model. Hence, low-intensity ultrasound stimulation advances the development and integration of brain organoids, providing a strategy for treating neurodevelopmental disorders and repairing cortical damage., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Periprosthetic infection is one of the most devastating complications following orthopaedic surgery. Rapid detection of an infection can change the treatment pathway and improve outcomes for the patient. In here, we propose a miniaturized lactate biosensor developed on a flexible substrate and integrated on a small-form bone implant to detect infection. The methods for lactate biosensor fabrication and integration on a bone implant are fully described within this study. The system performance was comprehensively electrochemically characterised, including with L-lactate solutions prepared in phosphate-buffered saline and culture medium, and interferents such as acetaminophen and ascorbic acid. A proof-of-concept demonstration was then conducted with ex vivo ovine femoral heads incubated with and without exposure to Staphylococcus epidermidis. The sensitivity, current density and limit-of-detection levels achieved by the biosensor were 1.25 μA mM -1 , 1.51 μA.M -1 .mm -2 and 66 μM, respectively. The system was insensitive to acetaminophen, while sensitivity to ascorbic acid was half that of the sensitivity to L-lactate. In the ex vivo bone model, S. epidermidis infection was detected within 5 h of implantation, while the control sample led to no change in the sensor readings. This pioneering work demonstrates a pathway to improving orthopaedic outcomes by enabling early infection diagnosis., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Despite numerous studies demonstrate that genetics and epigenetics factors play important roles on smoking behavior, our understanding of their functional relevance and coordinated regulation remains largely unknown. Here we present a multiomics study on smoking behavior for Chinese smoker population with the goal of not only identifying smoking-associated functional variants but also deciphering the pathogenesis and mechanism underlying smoking behavior in this under-studied ethnic population. After whole-genome sequencing analysis of 1329 Chinese Han male samples in discovery phase and OpenArray analysis of 3744 samples in replication phase, we discovered that three novel variants located near FOXP1 (rs7635815), and between DGCR6 and PRODH (rs796774020), and in ARVCF (rs148582811) were significantly associated with smoking behavior. Subsequently cis-mQTL and cis-eQTL analysis indicated that these variants correlated significantly with the differential methylation regions (DMRs) or differential expressed genes (DEGs) located in the regions where these variants present. Finally, our in silico multiomics analysis revealed several hub genes, like DRD2, PTPRD, FOXP1, COMT, CTNNAP2, to be synergistic regulated each other in the etiology of smoking., (© 2024. The Author(s), under exclusive licence to Springer Nature Limited.)

Vigilance state is crucial for the effective performance of users in brain-computer interface (BCI) systems. Most vigilance estimation methods rely on a large amount of labeled data to train a satisfactory model for the specific subject, which limits the practical application of the methods. This study aimed to build a reliable vigilance estimation method using a small amount of unlabeled calibration data. We conducted a vigilance experiment in the designed BCI-based cursor-control task. Electroencephalogram (EEG) signals of eighteen participants were recorded in two sessions on two different days. And, we proposed a contrastive fine-grained domain adaptation network (CFGDAN) for vigilance estimation. Here, an adaptive graph convolution network (GCN) was built to project the EEG data of different domains into a common space. The fine-grained feature alignment mechanism was designed to weight and align the feature distributions across domains at the EEG channel level, and the contrastive information preservation module was developed to preserve the useful target-specific information during the feature alignment. The experimental results show that the proposed CFGDAN outperforms the compared methods in our BCI vigilance dataset and SEED-VIG dataset. Moreover, the visualization results demonstrate the efficacy of the designed feature alignment mechanisms. These results indicate the effectiveness of our method for vigilance estimation. Our study is helpful for reducing calibration efforts and promoting the practical application potential of vigilance estimation methods., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Continuous theta burst stimulation (cTBS) is a noninvasive brain stimulation technique. cTBS modulation is an effective treatment for motor dysfunction rehabilitation in post-stroke patients. However, there's currently a lack of research on the effects of cTBS stimulation on the contralesional hemisphere. To better understand the role of cTBS in motor rehabilitation, we investigated the neuroregulatory mechanisms of cTBS in the contralateral cortex using transcranial magnetic stimulation-evoked electroencephalography (TMS-EEG). In this randomized, sham-controlled, single-blind study, 18 healthy subjects received two separate stimulation conditions: cTBS or sham stimulation applied to the left primary motor cortex (M1). TMS-EEG measurements were taken before and immediately after stimulation. We investigated the TMS-evoked potentials (TEPs), evoked oscillatory responses (EOR), and phase synchronization index (PSI) of TMS-EEG. The effects of cTBS were analyzed using two-way repeated-measures analysis of variance (RMANOVA). There was a significant "cTBS condition × time" interaction effect on the theta and gamma bands of EOR, and on interhemisphere PSI (inter-PSI) and global PSI in both cTBS stimulation conditions. (theta: F = 4.526, P = 0.041; gamma: F = 5.574, P = 0.024; inter-PSI: F = 5.028, P = 0.032; global PSI: F = 5.129, P = 0.030). After real cTBS modulation, the energy in the theta and gamma frequency bands was significantly higher than before (theta: F = 5.747, P = 0.022; gamma: F = 5.545, P = 0.024). The inter-PSI and global PSI significantly increased after real cTBS modulation (inter-PSI: F = 6.209, P = 0.018; global PSI: F = 6.530, P = 0.015). cTBS modulation significantly increased EOR and PSI in contralateral brain regions, thereby enhancing cortical excitability and cortical functional connectivity throughout the brain. This provides a theoretical basis for cTBS neuromodulation in patients with stroke. NEW & NOTEWORTHY In right-handed individuals, the left hemisphere exhibits higher excitability. According to hemispheric competition theory, applying continuous theta burst stimulation (cTBS) to inhibit excitability in the left hemisphere can reduce its inhibitory effect on the right, thereby promoting neural excitability. This study applied cTBS to the left M1 of healthy individuals and, for the first time, recorded transcranial magnetic stimulation-evoked electroencephalography (TMS-EEG) from the right M1 to analyze the effects of cTBS on cortical oscillations and network connectivity in the contralateral cortex.

IN the above article [1], we found the formula (1) is presented incorrectly because of an error in the formula editing process. The correction is as follows: ITR=[log 2 K+Plog 2 P+(1-p)log 2 (1-P/K-1)]×60/T (1).

Currently, spatiotemporal convolutional neural networks (CNNs) for electroencephalogram (EEG) signals have emerged as promising tools for seizure prediction (SP), which explore the spatiotemporal biomarkers in an epileptic brain. Generally, these CNNs capture spatiotemporal features at single spectral resolution. However, epileptiform EEG signals contain irregular neural oscillations of different frequencies in different brain regions. Therefore, it may be underperforming and uninterpretable for the CNNs without capturing complex spectral properties sufficiently. This study proposed a novel interpretable multi-branch architecture for spatiotemporal CNNs, namely MultiSincNet. On the one hand, the MultiSincNet could directly show the frequency boundaries using the interpretable sinc-convolution layers. On the other hand, it could extract and integrate multiple spatiotemporal features across varying spectral resolutions using parallel branches. Moreover, we also constructed a post-hoc explanation technique for multi-branch CNNs, using the first-order Taylor expansion and chain rule based on the multivariate composite function, which demonstrates the crucial spatiotemporal features learned by the proposed multi-branch spatiotemporal CNN. When combined with the optimal MultiSincNet, ShallowConvNet, DeepConvNet, and EEGWaveNet had significantly improved the subject-specific performance on most metrics. Specifically, the optimal MultiSincNet significantly increased the average accuracy, sensitivity, specificity, binary F1-score, weighted F1-score, and AUC of EEGWaveNet by about 7%, 8%, 7%, 8%, 7%, and 7%, respectively. Besides, the visualization results showed that the optimal model mainly extracts the spectral energy difference from the high gamma band focalized to specific spatial areas as the dominant spatiotemporal EEG feature.

At present, at least 30% of refractory epilepsy patients in the world cannot be effectively controlled and treated. The suddenness and unpredictability of seizures greatly affect the physical and mental health and even the life safety of patients, and the realization of early prediction of seizures and the adoption of interventions are of great significance to the improvement of patients' quality of life. In this paper, we firstly introduce the design process of EEG-based seizure prediction methods, introduce several databases commonly used in the research, and summarize the commonly used methods in pre-processing, feature extraction, classification and identification, and post-processing. Then, based on scalp EEG and intracranial EEG respectively, we reviewed the current status of epileptic seizure prediction research from five commonly used feature analysis methods, and make a comprehensive evaluation of both. Finally, this paper describes the reasons why the current algorithms cannot be applied to the clinic, summarizes their limitations, and gives corresponding suggestions, aiming to provide improvement directions for subsequent research. In addition, deep learning algorithms have emerged in recent years, and this paper also compares the advantages and disadvantages of deep learning algorithms with traditional machine learning methods, in the hope of providing researchers with new technologies and new ideas and making significant breakthroughs in the field of epileptic seizure prediction., Competing Interests: Conflict of interestThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© The Author(s), under exclusive licence to Springer Nature B.V. 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

Emotion recognition is crucial for human-computer interaction, and electroencephalography (EEG) stands out as a valuable tool for capturing and reflecting human emotions. In this study, we propose a hierarchical hybrid model called Mixed Attention-based Convolution and Transformer Network (MACTN). This model is designed to collectively capture both local and global temporal information and is inspired by insights from neuroscientific research on the temporal dynamics of emotions. First, we introduce depth-wise temporal convolution and separable convolution to extract local temporal features. Then, a self-attention-based transformer is used to integrate the sparse global emotional features. Besides, channel attention mechanism is designed to identify the most task-relevant channels, facilitating the capture of relationships between different channels and emotional states. Extensive experiments are conducted on three public datasets under both offline and online evaluation modes. In the multi-class cross-subject online evaluation using the THU-EP dataset, MACTN demonstrates an approximate 8% enhancement in 9-class emotion recognition accuracy in comparison to state-of-the-art methods. In the multi-class cross-subject offline evaluation using the DEAP and SEED datasets, a comparable performance is achieved solely based on the raw EEG signals, without the need for prior knowledge or transfer learning during the feature extraction and learning process. Furthermore, ablation studies have shown that integrating self-attention and channel-attention mechanisms improves classification performance. This method won the Emotional BCI Competition's final championship in the World Robot Contest. The source code is available at https://github.com/ThreePoundUniverse/MACTN., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Ischemic stroke (IS) is characterized by high mortality, disability rates, and a high risk of recurrence. Motor dysfunction, such as limb hemiparesis, dysphagia, auditory disorders, and speech disorders, usually persists after stroke, which imposes a heavy burden on society and the health care system. Traditional rehabilitation therapies may be ineffective in promoting functional recovery after stroke, and alternative strategies are urgently needed. The Food and Drug Administration (FDA) has approved invasive vagus nerve stimulation (iVNS) for the improvement of refractory epilepsy, treatment-resistant depression, obesity, and moderate to severe upper limb motor impairment following chronic ischemic stroke. Additionally, the FDA has approved transcutaneous vagus nerve stimulation (tVNS) for the improvement of cluster headaches and acute migraines. Recent studies have demonstrated that vagus nerve stimulation (VNS) has neuroprotective effects in both transient and permanent cerebral ischemia animal models, significantly improving upper limb motor impairments, auditory deficits, and swallowing difficulties. Firstly, this article reviews two potential neuronal death pathways following IS, including autophagy and inflammatory responses. Then delves into the current status of preclinical and clinical research on the functional recovery following IS with VNS, as well as the potential mechanisms mediating its neuroprotective effects. Finally, the optimal parameters and timing of VNS application are summarized, and the future challenges and directions of VNS in the treatment of IS are discussed. The application of VNS in stroke rehabilitation research has reached a critical stage, and determining how to safely and effectively translate this technology into clinical practice is of utmost importance. Further preclinical and clinical studies are needed to elucidate the therapeutic mechanisms of VNS., Competing Interests: Conflict of interestConflict of interest This work was supported in part by the National Key Research and Development Program of China under Grant 2021YFF1200600; in part by the National Natural Science Foundation of China under Grant 62,376,190, Grant 81,925,020, and Grant 82,001,939; and in part by the Natural Science Foundation of Tianjin under Grant 22JCYBJC00210, and Grant 22JCYBJC01430., (© The Author(s), under exclusive licence to Springer Nature B.V. 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

Enhancing protein stability is pivotal in the field of protein engineering. Protein self-cyclization using peptide a tagging system has emerged as an effective strategy for augmenting the thermostability of target proteins. In this study, we utilized a novel peptide tagging system, ReverseTag/ReverseCatcher, which leverages intramolecular ester bond formation. Initially, we employed GFP as a model to validate the feasibility of cyclization mediated by ReverseTag/ReverseCatcher in improving the protein thermostability. Cyclized GFP (cGFP) retained 30 % of its relative fluorescence after a 30-min incubation at 100 °C, while both GFP and linear GFP (lGFP) completely lost their fluorescence within 5 min. Additionally, we applied this method to exo-inulinase (EXINU), resulting in a variant named cyclized EXINU (cEXINU). The T 50 and t 1/2 values of cEXINU exhibited significant enhancements of 10 °C and 10 min, respectively, compared to EXINU. Furthermore, post-cyclization, EXINU demonstrated a broad operational pH range from 5 to 10 with sustained catalytic activity, and cEXINU maintained a half-life of 960 min at pH 5 and 9. Molecular dynamics simulations were conducted to elucidate the mechanisms underlying the enhanced thermostability and pH robustness of EXINU following cyclization. This study highlights that cyclization substanitially enhances the stability of both highly stable protein GFP and low-stable protein EXINU, mediated by the ReverseTag/ReverseCatcher tagging system. The ReverseTag/ReverseCatcher tagging system proves to be a potent conjugation method, with potential applications in improving thermostability, pH robustness, and other areas of protein engineering., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Introduction: Melanogenesis, the process responsible for melanin production, is a critical determinant of skin pigmentation. Dysregulation of this process can lead to hyperpigmentation disorders., Method: In this study, we identified a novel Reed Rhizome extract, (1'S, 2'S)-syringyl glycerol 3'-O-β-D-glucopyranoside (compound 5), and evaluated its anti-melanogenic potential in zebrafish models and in vitro assays. Compound 5 inhibited melanin synthesis by 36.66% ± 14.00% and tyrosinase in vivo by 48.26% ± 6.94%, surpassing the inhibitory effects of arbutin. Network pharmacological analysis revealed key targets, including HSP90AA1, HRAS, and PIK3R1, potentially involved in the anti-melanogenic effects of compound 5., Results: Molecular docking studies supported the interactions between compound 5 and these targets. Further, gene expression analysis in zebrafish indicated that compound 5 up-regulates hsp90aa1.1, hrasa, and pik3r1, and subsequently down-regulating mitfa, tyr, and tyrp1, critical genes in melanogenesis., Conclusion: These findings suggest that compound 5 inhibits melanin production via PI3K-Akt and Ras-Raf-MEK-ERK signaling pathways, positioning it as a promising candidate for the treatment of hyperpigmentation., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Indole-3-propionic acid (IPA) has anti-inflammatory properties, which can be beneficial for weaned piglets with underdeveloped immune systems. The study explores the impact of IPA supplementation on growth performance, oxidative stress, and inflammation response in weaned piglets. In Exp. 1, 90 weaned piglets were divided into six groups (5 replicates per group, 3 pigs per replicate), with each group receiving a basal diet with varying amounts of IPA (0, 50, 100, 200, 400, or 600 mg/kg) for 42 d. Piglets fed the diets with 50, 100, and 200 mg/kg of IPA exhibited reduced feed conversion ratios (F:G) compared to the control piglets ( P  = 0.035). Notably, 50 and 100 mg/kg IPA treatments significantly reduced diarrhea incidence and serum interleukin (IL)-6 content ( P <  0.05). Conversely, a high dosage of 600 mg/kg IPA led to increased serum contents of tumor necrosis factor (TNF)-α, and IL-6 ( P <  0.05). Optimal antioxidant benefits were observed at 100 mg/kg IPA supplementation, which significantly reduced malondialdehyde levels while enhancing serum total antioxidant capacity and total superoxide dismutase activity on d 14 ( P  < 0.05). Exp. 2 investigated the effects of IPA on lipopolysaccharide (LPS) challenge in weaned piglets. The study consisted of 32 weaned piglets allocated into 4 groups, with 8 replicates per group and 1 piglet per replicate: a control group, a LPS challenge group, a LPS challenge group supplemented with 100 mg/kg IPA, and a group supplemented with 100 mg/kg IPA alone. Upon administration of LPS or saline injection, the results indicated that dietary IPA supplementation in challenged piglets enhanced villus height: crypt depth, modulated IL-8 and IL- 22 mRNA relative expression, and increased the tight junction protein claudin-1 mRNA relative expression in the intestinal mucosa ( P  < 0.05). These findings suggest that dietary supplementation of IPA at specific concentrations significantly improves growth performance, reduces diarrhea incidence, and mitigates inflammation and oxidative stress in weaned piglets. It may be concluded that incorporating IPA into the diet of weaned piglets can effectively improve their health and development., Competing Interests: We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, and there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the content of this paper., (© 2024 The Authors.)

Background: The loss of gait automaticity is a key cause of motor deficits in Parkinson's disease (PD) patients, even at the early stage of the disease. Action observation training (AOT) shows promise in enhancing gait automaticity. However, effective assessment methods are lacking. We aimed to propose a novel gait normalcy index based on dual task cost (NIDTC) and evaluate its validity and responsiveness for early-stage PD rehabilitation., Methods: Thirty early-stage PD patients were recruited and randomly assigned to the AOT or active control (CON) group. The proposed NIDTC during straight walking and turning tasks and clinical scale scores were measured before and after 12 weeks of rehabilitation. The correlations between the NIDTCs and clinical scores were analyzed with Pearson correlation coefficient analysis to evaluate the construct validity. The rehabilitative changes were assessed using repeated-measures ANOVA, while the responsiveness of NIDTC was further compared by t tests., Results: The turning-based NIDTC was significantly correlated with multiple clinical scales. Significant group-time interactions were observed for the turning-based NIDTC (F = 4.669, p = 0.042), BBS (F = 6.050, p = 0.022) and PDQ-39 (F = 7.772, p = 0.011) tests. The turning-based NIDTC reflected different rehabilitation effects between the AOT and CON groups, with the largest effect size (p = 0.020, Cohen's d = 0.933)., Conclusion: The turning-based NIDTC exhibited the highest responsiveness for identifying gait automaticity improvement by providing a comprehensive representation of motor ability during dual tasks. It has great potential as a valid measure for early-stage PD diagnosis and rehabilitation assessment. Trial registration Chinese Clinical Trial Registry: ChiCTR2300067657., (© 2024. The Author(s).)

Alcohol dependence is a heritable disorder, yet its genetic basis and underlying mechanisms remain poorly understood, especially in Chinese population. In this study, we conducted gene-based and transcript-based association tests and found a significant association between ARVCF expression in the cortex and hippocampus of the brain and alcohol use in a cohort of 1,329 individuals with Chinese ancestry. Further analysis using the effective-median-based Mendelian randomization framework for inferring the causal genes (EMIC) revealed a causal relationship between ARVCF expression in the frontal cortex and alcohol use. Moreover, leveraging extensive European alcohol dependence data, our gene association tests and EMIC analysis showed that ARVCF expression in the nucleus accumbens was significantly associated with alcohol dependence. Finally, animal studies indicated that Arvcf knockout mice lacked conditioned place preference for alcohol. Together, our combined human genetic and animal studies indicate that ARVCF plays a crucial role in alcohol dependence., Competing Interests: The authors declare no competing interests., (© 2024 The Author(s).)

Implantable neural electrodes are crucial in neurological diagnosis and therapy because of their ultra-high spatial resolution, but they are constrained by high impedance and insufficient charge injection capacity, resulting in noise that often obscures valuable signals. Emerging nanotechnologies are powerful tools to improve sensitivity and biocompatibility. Herein, we developed quantized 2D MoS 2 electrodes by incorporating bioactive MoS 2 nanosheets onto bare electrodes, achieving sensitive, compatible recording. The 2D materials can create tiny nanowells, which behaved as quantized charge storage units and thus improved sensitivity. The key sensitivity indicators, impedance and cathode charge storage capacity, showed a multifold increase. The 17.7-fold improvement in catalytic activity of MoS 2 electrodes facilitated effective current transmission and reduced inflammatory response. In vivo recording showed that the sensitivity of local field potentials increased throughout frequency range and peaked at a 4.7-fold in β rhythm. This work provides a general strategy for achieving effective diagnoses of neurological disorders., Competing Interests: The authors declare no competing interests., (© 2024 The Author(s).)

Introduction: Brain organoids are believed to be able to regenerate impaired neural circuits and reinstate brain functionality. The neuronal activity of organoids is considered a crucial factor for restoring host function after implantation. However, the optimal stage of brain organoid post-transplantation has not yet been established. External electrical signal plays a crucial role in the physiology and development of a majority of human tissues. However, whether electrical input modulates the development of brain organoids, making them ideal transplant donors, is elusive., Methods: Bioelectricity was input into cortical organoids by electrical stimulation (ES) with a multi-electrode array (MEA) to obtain a better-transplanted candidate with better viability and maturity, realizing structural-functional integration with the host brain., Results: We found that electrical stimulation facilitated the differentiation and maturation of organoids, displaying well-defined cortical plates and robust functional electrophysiology, which was probably mediated via the pathway of calcium-calmodulin (CaM) dependent protein kinase II (CAMK II)-protein kinase A (PKA)-cyclic-AMP response binding protein (pCREB). The ES-pretreated D40 organoids displayed superior cell viability and higher cell maturity, and were selected to transplant into the damaged primary sensory cortex (S1) of host. The enhanced maturation was exhibited within grafts after transplantation, including synapses and complex functional activities. Moreover, structural-functional integration between grafts and host was observed, conducive to strengthening functional connectivity and restoring the function of the host injury., Conclusion: Our findings supported that electrical stimulation could promote the development of cortical organoids. ES-pretreated organoids were better-transplanted donors for strengthening connectivity between grafts and host. Our work presented a new physical approach to regulating organoids, potentially providing a novel translational strategy for functional recovery after brain injury. In the future, the development of 3D flexible electrodes is anticipated to overcome the drawbacks of 2D planar MEA, promisingly achieving multimodal stimulation and long-term recordings of brain organoids., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Production and hosting by Elsevier B.V.)

Fine sensory modalities play an essential role in perceiving the world. However, little is known about how the cortico-cortical distinguishes between dynamic and static tactile signals. This study investigated oscillatory connectivity during a tactile discrimination task of dynamic and static stimulation via electroencephalogram (EEG) recordings and the fast oscillatory networks across widespread cortical regions. While undergoing EEG recordings, the subject felt an electro-tactile presented by a 3-dot array. Each block consisted of 3 forms of stimulation: Spatio-temporal (dynamic), Spatial (static), and Control condition (lack of electrical stimulation). The average event-related potential for the Spatial and Spatio-temporal conditions exhibited statistically significant differences between 25 and 75, 81 and 121, 174 and 204 and 459 and 489 ms after stimulus onset. Based on those times, the sLORETA approach was used to reconstruct the inverse solutions of EEG. Source localization appeared superior parietal at around 25 to 75 ms, in the primary motor cortex at 81 to 121 ms, in the central prefrontal cortex at 174 to 204 and 459 to 489 ms. To better assess spectral brain functional connectivity, we selected frequency ranges with correspondingly significant differences: for static tactile stimulation, these are concentrated in the Theta, Alpha, and Gamma bands, whereas for dynamic stimulation, the relative energy change bands are focused on the Theta and Alpha bands. These nodes' functional connectivity analysis (phase lag index) showed 3 distinct distributed networks. A tactile information discrimination network linked the Occipital lobe, Prefrontal lobe, and Postcentral gyrus. A tactile feedback network linked the Prefrontal lobe, Postcentral gyrus, and Temporal lobe. A dominant motor feedforward loop network linked the Parietal cortex, Prefrontal lobe, Frontal lobe, and Parietal cortex. Processing dynamic and static tactile signals involves discriminating tactile information, motion planning, and cognitive decision processing., (© The Author(s) 2024. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permission@oup.com.)

Adding supernumerary robotic limbs (SRLs) to humans and controlling them directly through the brain are main goals for movement augmentation. However, it remains uncertain whether neural patterns different from the traditional inherent limbs motor imagery (MI) can be extracted, which is essential for high-dimensional control of external devices. In this work, we established a MI neo-framework consisting of novel supernumerary robotic sixth-finger MI (SRF-MI) and traditional right-hand MI (RH-MI) paradigms and validated the distinctness of EEG response patterns between two MI tasks for the first time. Twenty-four subjects were recruited for this experiment involving three mental tasks. Event-related spectral perturbation was adopted to supply details about event-related desynchronization (ERD). Activation region, intensity and response time (RT) of ERD were compared between SRF-MI and RH-MI tasks. Three classical classification algorithms were utilized to verify the separability between different mental tasks. And genetic algorithm aims to select optimal combination of channels for neo-framework. A bilateral sensorimotor and prefrontal modulation was found during the SRF-MI task, whereas in RH-MI only contralateral sensorimotor modulation was exhibited. The novel SRF-MI paradigm enhanced ERD intensity by a maximum of 117% in prefrontal area and 188% in the ipsilateral somatosensory-association cortex. And, a global decrease of RT was exhibited during SRF-MI tasks compared to RH-MI. Classification results indicate well separable performance among different mental tasks (88.1% maximum for 2-class and 88.2% maximum for 3-class). This work demonstrated the difference between the SRF-MI and RH-MI paradigms, widening the control bandwidth of the BCI system.

Depression, a multifactorial mental disorder, characterized by cognitive slowing, anxiety, and impaired cognitive function, imposes a significant burden on public health. Photobiomodulation (PBM), involving exposure to sunlight or artificial light at a specific intensity and wavelength for a determined duration, influences brain activity, functional connectivity, and plasticity. It is recognized for its therapeutic efficacy in treating depression, yet its molecular and cellular underpinnings remain obscure. Here, we investigated the impact of PBM with 468 nm light on depression-like behavior and neuronal damage in the chronic unpredictable mild stress (CUMS) murine model, a commonly employed animal model for studying depression. Our results demonstrate that PBM treatment ameliorated behavioral deficits, inhibited neuroinflammation and apoptosis, and notably rejuvenates the hippocampal synaptic function in depressed mice, which may be mainly attributed to the up-regulation of brain-derived neurotrophic factor signaling pathways. In addition, in vitro experiments with a corticosterone-induced hippocampal neuron injury model demonstrate reduced oxidative stress and improved mitochondrial function, further validating the therapeutic potential of PBM. In summary, these findings suggest PBM as a promising, non-invasive treatment for depression, offering insights into its biological mechanisms and potential for clinical application., Competing Interests: Declaration of Competing Interest The authors have no relevant financial or non-financial interests to disclose., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Mental workload (MWL) assessment is critical for accident prevention and operator safety. However, achieving cross-task generalization of MWL classification models is a significant challenge for real-world applications. Classifiers trained on labeled samples from one task often experience a notable performance drop when directly applied to samples from other tasks, limiting its use cases. To address this issue, we propose a semi-supervised cross-task domain adaptation (SCDA) method using power spectral density (PSD) features for MWL recognition across tasks (MATB-II and n-back). Our results demonstrated that the SCDA method achieved the best cross-task classification performance on our data and COG-BCI public dataset, with accuracies of 90.98% ± 9.36% and 96.61% ± 4.35%, respectively. Furthermore, in the cross-task classification of cross-subject scenarios, SCDA showed the highest average accuracy (75.39% ± 9.56% on our data, 90.98% ± 9.36% on the COG-BCI public dataset). The findings indicate that the semi-supervised transfer learning approach using PSD features is feasible and effective for cross-task MWL assessment.

Purpose: Knee adduction, flexion moment, and adduction angle are often used as surrogate parameters of knee medial force. To verify whether these parameters are suitable as surrogates under different walking states, we investigated the correlation between knee medial loading with the surrogates during walking and turning., Methods: Sixteen healthy subjects were recruited to complete straight walk (SW), step turn (ST), and crossover turn (CT). Knee joint moments were obtained using inverse dynamics, and knee medial force was computed using a previously validated musculoskeletal model, Freebody . Linear regression was used to predict the peak of knee medial force with the peaks of the surrogate parameters and walking speed., Results: There was no significant difference in walking speed among these three tasks. The peak knee adduction moment (pKAM) was a significant predictor of the peak knee medial force (pKMF) for SW, ST, and CT ( p < 0.001), while the peak knee flexion moment (pKFM) was only a significant predictor of the pKMF for SW ( p = 0.034). The statistical analysis showed that the pKMF increased, while the pKFM and the peak knee adduction angle (pKAA) decreased significantly during CT compared to those of SW and ST ( p < 0.001). The correlation analysis indicated that the knee parameters during SW and ST were quite similar., Conclusions: This study investigated the relationship between knee medial force and some surrogate parameters during walking and turning. KAM was still the best surrogate parameter for SW, ST, and CT. It is necessary to consider the type of movement when comparing the surrogate predictors of knee medial force, as the prediction equations differ significantly among movement types.

Prolonged disorders of consciousness (pDOC) are pathological conditions of alterations in consciousness caused by various severe brain injuries, profoundly affecting patients' life ability and leading to a huge burden for both the family and society. Exploring the mechanisms underlying pDOC and accurately assessing the level of consciousness in the patients with pDOC provide the basis of developing therapeutic strategies. Research of non-invasive functional neuroimaging technologies, such as functional magnetic resonance (fMRI) and scalp electroencephalography (EEG), have demonstrated that the generation, maintenance and disorders of consciousness involve functions of multiple cortical and subcortical brain regions, and their networks. Invasive intracranial neuroelectrophysiological technique can directly record the electrical activity of subcortical or cortical neurons with high signal-to-noise ratio and spatial resolution, which has unique advantages and important significance for further revealing the brain function and disease mechanism of pDOC. Here we reviewed the current progress of pDOC research based on two intracranial electrophysiological signals, spikes reflecting single-unit activity and field potential reflecting multi-unit activities, and then discussed the current challenges and gave an outlook on future development, hoping to promote the study of pathophysiological mechanisms related to pDOC and provide guides for the future clinical diagnosis and therapy of pDOC.

Many protein-protein interactions (PPIs) affect the ways in which small molecules bind to their constituent proteins, which can impact drug efficacy and regulatory mechanisms. While recent advances have improved our ability to independently predict both PPIs and ligand-protein interactions (LPIs), a comprehensive understanding of how PPIs affect LPIs is still lacking. Here, we examined 63 pairs of ligand-protein complexes in a benchmark dataset for protein-protein docking studies and quantified six typical effects of PPIs on LPIs. A multi-chain dynamics perturbation analysis method, called mc DPA, was developed to model these effects and used to predict small-molecule binding regions in protein-protein complexes. Our results illustrated that the mc DPA can capture the impact of PPI on LPI to varying degrees, with six similar changes in its predicted ligand-binding region. The calculations showed that 52% of the examined complexes had prediction accuracy at or above 50%, and 55% of the predictions had a recall of not less than 50%. When applied to 33 FDA-approved protein-protein-complex-targeting drugs, these numbers improved to 60% and 57% for the same accuracy and recall rates, respectively. The method developed in this study may help to design drug-target interactions in complex environments, such as in the case of protein-protein interactions.

The relationship between amino acid mutations and enzyme bioactivity is a significant challenge in modern bio-industrial applications. Despite many successful designs relying on complex correlations among mutations at different enzyme sites, the underlying mechanisms of these correlations still need to be explored. In this study, we introduced a revised version of the residual-contact network clique model to investigate the additive effect of double mutations based on the mutation occurrence topology, secondary structures, and physicochemical properties. The model was applied to a set of 182 double mutations reported in three extensively studied enzymes, and it successfully identified over 90% of additive double mutations and a majority of non-additive double mutations. The calculations revealed that the mutation additivity depends intensely on the studied mutation sites' topology and physicochemical properties. For example, double mutations on irregular secondary structure regions tend to be non-additive. Our method provides valuable tools for facilitating enzyme design and optimization. The code and relevant data are available at Github.

Objective: Individual differences challenge the treatment of vancomycin, linezolid and voriconazole in severe infections. This study aimed to build a simple and economical method for simultaneous determination of the three antibiotics in human plasma by ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) and provided a reference for therapeutic drug monitoring (TDM) of infected patients., Methods: The plasma samples were precipitated by acetonitrile and detected and separated on a shim-pack GIST C18 column following the gradient elution within 5 min. Mass quantification was performed on multiple reaction monitoring mode under positive electrospray ionization., Results: The linear ranges of vancomycin, linezolid and voriconazole were 1.00-100.00, 0.10-15.00 and 0.10-20.00 μg·mL-1, respectively, with good linearity (R2 > 0.99). The accuracy and precision, matrix effect, extraction recovery and stability were validated, and the results all meet the acceptance criteria of China Food and Drug Administration (CFDA) guidelines., Conclusion: The UHPLC-MS/MS method was established and validated for the simultaneous determination of vancomycin, linezolid and voriconazole in human plasma and successfully applied to routine TDM for individualized treatment., (© The Author(s) 2024. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

The global prevalence rate for congenital hydrocephalus (CH) is approximately one out of every five hundred births with multifaceted predisposing factors at play. Genetic influences stand as a major contributor to CH pathogenesis, and epidemiological evidence suggests their involvement in up to 40% of all cases observed globally. Knowledge about an individual's genetic susceptibility can significantly improve prognostic precision while aiding clinical decision-making processes. However, the precise genetic etiology has only been pinpointed in fewer than 5% of human instances. More occurrences of CH cases are required for comprehensive gene sequencing aimed at uncovering additional potential genetic loci. A deeper comprehension of its underlying genetics may offer invaluable insights into the molecular and cellular basis of this brain disorder. This review provides a summary of pertinent genes identified through gene sequencing technologies in humans, in addition to the 4 genes currently associated with CH (two X-linked genes L1CAM and AP1S2, two autosomal recessive MPDZ and CCDC88C). Others predominantly participate in aqueduct abnormalities, ciliary movement, and nervous system development. The prospective CH-related genes revealed through animal model gene-editing techniques are further outlined, focusing mainly on 4 pathways, namely cilia synthesis and movement, ion channels and transportation, Reissner's fiber (RF) synthesis, cell apoptosis, and neurogenesis. Notably, the proper functioning of motile cilia provides significant impulsion for cerebrospinal fluid (CSF) circulation within the brain ventricles while mutations in cilia-related genes constitute a primary cause underlying this condition. So far, only a limited number of CH-associated genes have been identified in humans. The integration of genotype and phenotype for disease diagnosis represents a new trend in the medical field. Animal models provide insights into the pathogenesis of CH and contribute to our understanding of its association with related complications, such as renal cysts, scoliosis, and cardiomyopathy, as these genes may also play a role in the development of these diseases. Genes discovered in animals present potential targets for new treatments but require further validation through future human studies., (© 2024. The Author(s).)

Hydrocephalus can affect brain function and motor ability. Current treatments mostly involve invasive surgeries, with a high risk of postoperative infections and failure. A successful animal model plays a significant role in developing new treatments for hydrocephalus. Hydrocephalus was induced in Sprague-Dawley rats by injecting 25% kaolin into the subarachnoid space at the cerebral convexities with different volumes of 30, 60 and 90 μL. Magnetic resonance imaging (MRI) was performed 1 month and 4 months after kaolin injection. The behavioral performance was assessed weekly, lasting for 7 weeks. The histopathological analyses were conducted to the lateral ventricles by hematoxylin-eosin (HE) staining. Transcriptomic analysis was used between Normal Pressure Hydrocephalus (NPH) patients and hydrocephalus rats. MRI showed a progressive enlargement of ventricles in hydrocephalus group. Kaolin-60 μL and kaolin-90 μL groups showed larger ventricular size, higher anxiety level, bigger decline in body weight, motor ability and cognitive competence. These symptoms may be due to higher-grade inflammatory infiltrate and the damage of the structure of ependymal layer of the ventricles, indicated by HE staining. The overlap upregulated genes and pathways mainly involve immunity and inflammation. Transcriptomic revealed shared pathogenic genes CD40, CD44, CXCL10, and ICAM1 playing a dominance role. 60 μL injection might be recommended for the establishment of hydrocephalus animal model, with a high successful rate and high stability. The hydrocephalus model was able to resemble the inflammatory mechanism and behavioral performance observed in human NPH patients, providing insights for identifying therapeutic targets for hydrocephalus., Competing Interests: The authors declare they have no conflicts of interest., (© 2024 The Author(s). FASEB BioAdvances published by Wiley Periodicals LLC on behalf of The Federation of American Societies for Experimental Biology.)

Studies have indicated that medium- to long-duration spaceflight may adversely affect astronauts' emotional and social functioning. Emotion modulation can significantly impact astronauts' well-being, performance, mission safety and success. However, with the increase in flight time, the potential alterations in emotional and social performance during spaceflight and their underlying mechanisms remain to be investigated, and targeted therapeutic and preventive interventions have yet to be identified. We evaluated the changes of emotional and social functions in mice with the extension of the time in simulated space complex environment (SSCE), and simultaneously monitored changes in brain tissue of vascular endothelial growth factor (VEGF), matrix metalloproteinase-9 (MMP-9), and inflammation-related factors. Furthermore, we assessed the regulatory role of repetitive transcranial magnetic stimulation (rTMS) in mood and socialization with the extension of the time in SSCE, as well as examining alterations of VEGF signaling in the medial prefrontal cortex (mPFC). Our findings revealed that mice exposed to SSCE for 7 days exhibited depressive-like behaviors, with these changes persisting throughout SSCE period. In addition, 14 days of rTMS treatment significantly ameliorated SSCE-induced emotional and social dysfunction, potentially through modulation of the level of VEGF signaling in mPFC. These results indicates that emotional and social disorders increase with the extension of SSCE time, and rTMS can improve the performance, which may be related to VEGF signaling. This study offers insights into potential pattern of change over time for mental health issues in astronauts. Further analysis revealed that rTMS modulates emotional and social dysfunction during SSCE exposure, with its mechanism potentially being associated with VEGF signaling., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Committee on Space Research (COSPAR). Published by Elsevier B.V. All rights reserved.)

Asparagus officinalis (ASP) has antioxidation, anti-inflammatory, antiaging, and immune system-enhancing effects. We explored the preventive and therapeutic consequences of ASP on the brain damage elicited by fluorosis through network pharmacology and in vivo experimental validation. We ascertained the pharmaceutically active ingredients and drug targets of ASP from the Traditional Chinese Medicine Systems Pharmacology database, predicted the disease targets of fluorosis-induced brain injury using GeneCards and Online Mendelian Inheritance in Man databases, obtained target protein-protein interaction networks in the Search Tool for the Retrieval of Interacting Genes/Proteins database, used Cytoscape to obtain key targets and active ingredients, and conducted enrichment analyses of key targets in the Database for Annotation, Visualization and Integrated Discovery. Enrichment analyses showed that "mitogen-activated protein kinase" (MAPK), "phosphoinositide 3-kinase/protein kinase B" (PI3K-Akt), "nuclear factor-kappa B" (NF-κB), and the "neurotrophin signaling pathway" were the most enriched biological processes and signaling pathways. ASP could alleviate fluorosis-based injury, improve brain-tissue damage, increase urinary fluoride content, and improve oxidation levels and inflammatory-factor levels in the body. ASP could also reduce dental fluorosis, bone damage, fluoride concentrations in blood and bone, and accumulation of lipid peroxide. Upon ASP treatment, expression of silent information regulator (SIRT)1, brain-derived neurotrophic factor (BDNF), tropomyosin receptor kinase B (TrkB), MAPK, NF-κB, PI3K, Akt, and B-cell lymphoma-2 in rat brain tissue increased gradually, whereas that of Bax, caspase-3, and p53 decreased gradually. We demonstrated that ASP could regulate the brain damage caused by fluorosis through the SIRT1/BDNF/TrkB signaling pathway, and reported the possible part played by ASP in preventing and treating fluorosis., (© 2024 The Author(s). Environmental Toxicology published by Wiley Periodicals LLC.)