2025年 · 第01卷第04期

To the Editor:

Proper folding of a protein is essential for its diverse cellular functions. Improper protein folding may lead to the onset of various conformational diseases, including neurodegenerative diseases. Fortunately, multiple mechanisms maintain protein homeostasis in cells, where molecular chaperones perform critical functions. Chaperones are responsible for folding nascent proteins and directing aberrantly folded proteins for proteolytic clearance. Chaperones are considered the first defensive molecules against misfolded proteins. However, the identification of chaperones in disease-associated inclusion bodies signifies their involvement in the pathogenesis of neuronal degenerative diseases. Here, we highlight mechanistic insights into the role of heat shock protein 70 (Hsp70) molecular chaperones in several diseases such as Alzheimer’s, Parkinson’s, Huntington’s, and amyotrophic lateral sclerosis (ALS). Furthermore, we explore the different Hsp70-based molecular mechanisms implicated in neurodegeneration. Comprehensive information on these molecular mechanisms can aid in the development of better chaperone-based therapeutic strategies.

Epigenetics aver that environmental factors mediate various mechanisms that activate or deactivate the activity and expression of gene coding in deoxyribonucleic acid (DNA) and ribonucleic acid (RNAs), and these can ultimately result in neurocognitive conditions. Complex interactions between myriad factors, including biological, genetic, environmental, and psychological factors, can synergistically produce diverse neurocognitive conditions, such as addiction, attention-deficit hyperactivity disorder (ADHD), anxiety disorders, autism spectrum disorder, bipolar spectrum disorder, posttraumatic stress disorder, and schizophrenia. The genetic architecture of neurocognitive conditions is complex. However, recent developments in the field of neurocognitive conditions have led to a renewed interest in the contributions of epigenetics to these disorders. The more we examine the multifactorial etiologic complexity of respective conditions, which is the central aim of this review, the more we discover that environmental risk factors, such as early traumatic life experiences or exposure to numerous substances, ultimately determine whether a genetic tendency for a specific neurocognitive condition is expressed. Further, these advances have exposed the myriad commonalities underlying neurocognitive conditions, not the least of which include genetic, epigenetic, symptomatic, prevention, and treatment factors.

Brain aging is a complex biological process characterized by a progressive decline in cognitive function and increased vulnerability to neurodegenerative diseases. Although aging is inevitable, its trajectory varies significantly among individuals and is influenced by genetic, molecular, and environmental factors. The increase in global life expectancy has intensified the need for innovative strategies to address age-related cognitive disorders, shifting the focus from reactive treatments to proactive and precision-based interventions. This review summarizes the current knowledge on the mechanistic underpinnings of brain aging, including mitochondrial dysfunction, chronic neuroinflammation, epigenetic alterations, and the disruption of proteostasis. Cutting-edge diagnostic tools were highlighted, such as fluid biomarkers, advanced neuroimaging, and artificial intelligence (AI)-driven predictive models that enable the early detection of pathological changes. Emerging therapeutic approaches, ranging from senolytics and metabolic modulators to lifestyle interventions, have been discussed in the context of personalized therapies. Furthermore, this review summarized the challenges of data interoperability, ethical considerations of genetic risk disclosure, and the imperative for equitable access to these advances. By integrating multi-omics data with real-world metrics, precision medicine offers a transformative framework for preserving cognitive resilience. This review underscores the potential of tailored interventions to mitigate brain aging while addressing the scientific, clinical, and ethical hurdles that remain.

This systematic review and network meta-analysis evaluated the safety and efficacy of Bruton’s tyrosine kinase (BTK) inhibitors in patients with multiple sclerosis (MS). By assessing their impact on disease progression and adverse events, this study aims to provide insights into the therapeutic potential of BTK inhibition for the management of MS.

A comprehensive search was conducted in PubMed, Cochrane Central Register of Controlled Trials (CENTRAL), Scopus, and Web of Science databases for articles published until May 5, 2025. Randomized controlled trials (RCTs) comparing BTK inhibitors with placebo or other medications in patients with MS were included. Data extraction and risk-of-bias assessment were performed following the Cochrane guidelines. Traditional and network meta-analyses were performed to compare the efficacy outcomes, including gadolinium-enhancing lesions, annualized relapse rate (ARR), and new or enlarged T2 lesions, along with safety profiles.

In total, 5 RCTs (N = 3812) were included in this study. Evobrutinib 45 and 75 mg twice daily (BID) significantly reduced gadolinium-enhancing lesions compared to placebo (mean difference [MD] = -3.23, 95 % confidence interval [CI]: [–5.12, -1.34], P = 0.0008) and had similar efficacy to teriflunomide 14 mg once daily (QD) (MD = –3.41, 95% CI: [–5.30, –1.51], P = 0.0004). Tolebrutinib (60 mg QD) reduced new/enlarged T2 lesions (MD = 1.37, 95 % CI: [–2.13, -0.60], P = 0.0005) compared to placebo, but other doses lacked significant efficacy. Neither BTK inhibitor showed a significant reduction in ARR. Evobrutinib was associated with elevated alanine aminotransferase (ALT) (>3× upper limit of normal [ULN]: 11.14 %, 127/1140), followed by teriflunomide (10.82 %, 124/1146) and tolebrutinib (3.67 %, 32/871), necessitating liver monitoring. Tolebrutinib had fewer hepatotoxic effects but lacked robust efficacy on key outcomes.

Evobrutinib shows efficacy in reducing magnetic resonance imaging (MRI) lesions in MS, comparable to teriflunomide, though without significant ARR reduction. Tolebrutinib’s efficacy appears dose-dependent, warranting further investigation. Hepatotoxicity with evobrutinib and teriflunomide highlights the need for careful monitoring. Further large-scale RCTs are needed to confirm clinical benefits, optimal dosing, and neuroprotective potential.

Alzheimer’s disease and related dementias are increasing rapidly, presenting substantial public health challenges due to complex diagnostic protocols and the necessity for prolonged care. Conventional clinical assessments and manual magnetic resonance imaging (MRI) analyses are inherently time-intensive, prone to subjectivity, and susceptible to interobserver variability, constraining timely and accurate diagnosis.

To mitigate these limitations, this study introduced a hybrid deep learning framework integrating convolutional neural networks (CNNs) with self-attention mechanisms to improve model interpretability and diagnostic accuracy. For the cross-sectional Open Access Series of Imaging Studies (OASIS)-1 cohort, a 2-dimensional DenseNet-121 backbone followed by a lightweight multi-head Transformer encoder was deployed, enabling efficient slice-level feature extraction and global context modeling. The architecture, tailored to the OASIS-2 longitudinal MRI dataset, employed a 3-dimensional (3D) DenseNet structure augmented with self-attention blocks to enhance volumetric brain feature extraction and capture long-range dependencies. Data augmentation techniques such as random rotation, flipping, and CutMix, alongside regularization techniques including dropout, label smoothing, and early stopping, are utilized to improve generalization and address class imbalance.

For the OASIS-1 dataset, the model achieved 91.67 % accuracy, with 100 % precision, 85.71 % sensitivity, and an F1-score of 92.31. The proposed hybrid model achieved superior classification performance on the OASIS-2 dataset, attaining 97.33 % accuracy, precision, and sensitivity, along with an F1-score of 98.51%. These results exceeded those of existing baseline models, demonstrating the efficacy of the hybrid approach in accurately delineating cognitive impairment stages based on longitudinal MRI data.

The integration of 3D CNNs with attention mechanisms enhances the robustness of Alzheimer’s disease classification, facilitating their application in real-time automated diagnostic tools within clinical neuroimaging workflows. This study presents a robust and explainable framework designed to support clinicians in the early detection and intervention of Alzheimer’s disease.

The cytoplasm is a dynamic environment enriched with various cytoskeletal components, including microtubules, which play pivotal roles in cellular processes such as cell division, motility, and intracellular organization, especially in neurodegenerative diseases. Microtubules function in concert with the microtubule-associated proteins (MAPs), to regulate their assembly and stability. MAPs are a subgroup of microtubule-binding proteins (MTBPs), a broad category of proteins that interact with microtubules. Dysregulation of MAPs can lead to cytoskeletal alterations that affect physiological processes and contribute to the onset of severe neurodegenerative disorders. The increasing prevalence of these disorders poses a significant challenge to global health. The development of effective therapeutic interventions requires a thorough understanding of the underlying mechanisms. Traditionally, the identification of MAPs and MTBPs relies on labor-intensive experimental screening in wet laboratories.

A computational approach was developed that leverages machine learning algorithms including support vector machine (SVM) and random forest (RF). The models were trained and optimized using the 11 calculated features, which were subsequently cross-validated to evaluate the precision, recall, and accuracy.

The SVM-based classifier achieved a recall of 98 % and a precision of 93 %, whereas the RF-based classifier demonstrated a recall of 97 % and a precision of 91 %. Five features consistently yielding high classification accuracy (>90 %) across both algorithms were integrated into a novel Python-based prediction tool, "microtubule-associated and binding proteins predictor (MTBPred)." This freely accessible tool (available at www.bioinfoindia.org/mtbpred) offers a user-friendly platform for the accurate prediction of MTBPs, and provides valuable insights for accelerating research in this field.

Our study presents a robust machine-learning-based approach for the accurate prediction of microtubule-associated and binding proteins. The high precision and recall achieved by SVM and RF classifiers highlight the reliability of our method. MTBPred serves as a valuable tool to streamline MTBPs identification, facilitating future research and therapeutic advancements.

Neurological disorders pose a severe public health challenge in China, with substantial impacts on mortality, disability, and socioeconomic burden, especially in the context of population aging. This study synthesizes key findings from the China Neurological Disorders Report 2024 (compiled by the National Center for Neurological Disorders) to systematically outline the epidemiology of ten major neurological disorders in China, including cerebrovascular disease, dementia/Alzheimer's disease (AD), Parkinson's disease, epilepsy, central nervous system (CNS) tumors, traumatic brain injury (TBI), multiple sclerosis (MS), myasthenia gravis (MG), headache and pain syndromes, and amyotrophic lateral sclerosi (ALS). Cerebrovascular disease remains a leading cause of death, with ischemic stroke accounting for approximately 72.8 % of stroke cases and distinct north-south geographic disparities in China. AD and related dementias affect 16.99 million Chinese (29.8 % of the global total), with higher prevalence and mortality in females. Parkinson's disease prevalence is rising rapidly, with an estimated 3.62 million current patients and forecasts of 4.79 million by 2030. Epilepsy impacts over nine million people, while CNS tumors, TBI, MS, and MG exhibit unique demographic patterns. Notable trends include increasing disease burdens from neurodegenerative disorders linked to aging, sex disparities (higher female prevalence in AD, higher male incidence in Parkinson's disease and CNS tumors), and regional variations. These standardized, multi-source data address gaps in fragmented prior research, providing a robust scientific basis for healthcare resource allocation, clinical practice optimization, and policy development. Future efforts should focus on expanding data coverage, long-term cohort studies, and interdisciplinary research to enhance precise prevention and management of neurological disorders in China.