Work-related musculoskeletal disorders (WMSDs) represent a significant occupational health challenge among healthcare professionals globally, posing substantial threats to physical and mental well-being as well as work sustainability. Adopting preventive behaviors—including ergonomic postural adjustments, optimized work-rest scheduling, proper use of protective and assistive equipment, and regular physical activity—is essential for mitigating the risk of WMSDs. Guided by the social ecological model, the review synthesized current evidence on the determinants of WMSDs preventive behaviors across four levels: intrapersonal characteristics, work environment conditions, interpersonal support, and policy/institutional factors. The findings suggest that higher educational attainment, favorable health-related behavioral patterns, optimized ergonomic work environments, adoption of supportive collaborative systems, strong organizational support, as well as policy safeguards facilitate preventive behavior adoption. Conversely, limited prevention-related knowledge, low risk perception, insufficient physical activity, excessive workload, lack of appropriate protective equipment, inadequate ergonomic training, a prevailing culture of presenteeism, and inadequate policy implementation constitute significant barriers. Multi-dimensional intervention strategies targeting these determinants are warranted to enhance preventive behaviors, reduce the risk of WMSDs, and strengthen occupational health protection for healthcare professionals.

Electroencephalography (EEG) is a non-invasive neurophysiological monitoring technique. It records the electrical activity of the cerebral cortex using electrodes placed on the scalp surface. Owing to its high safety, portability, and millisecond-level temporal resolution, EEG has been widely utilized in a variety of fields, including clinical diagnosis, brain-computer interfaces (BCIs), and cognitive neuroscience research. However, due to its microvolt-level amplitude, EEG is highly susceptible to various artifacts, including electrooculographic (EOG), electrocardiographic (ECG), electromyographic (EMG), and power line interference (PLI). These artifacts can obscure genuine neural activity and introduce spurious electrophysiological features. Consequently, they may compromise EEG signal quality, thereby reducing the reliability of downstream analyses. To address this issue, numerous EEG artifact removal methods have been developed, including both traditional denoising techniques and deep learning-based approaches. Traditional EEG denoising methods have long served as the primary solutions for artifact removal. Representative approaches include filtering, regression, and blind source separation. Although these methods have demonstrated effectiveness in specific scenarios, they suffer from several inherent limitations. Filtering assumes that artifacts and EEG signals can be separated in the frequency domain, but many artifacts, such as EOG and EMG, overlap with EEG spectra, which may lead to the loss of valuable neural information. Regression methods require high-quality artifact references to estimate and subtract contaminations, limiting their effectiveness in reference-free scenarios. Blind source separation can remove artifacts without external references, but it typically requires the number of EEG channels to exceed the number of sources, restricting its application in single- or low-channel EEG recordings. Deep learning-based EEG denoising methods address these limitations effectively. First, they learn the nonlinear mapping between contaminated and clean EEG directly from data in an end-to-end manner. This approach does not rely on assumptions about spectral separability, thereby preserving neural activity more completely. Second, the reference information is incorporated during the training phase, allowing the trained model to perform artifact removal independently without external references. Third, deep learning models can be flexibly designed to accommodate various recording setups, achieving robust denoising for both high-density and single-channel EEG. Collectively, these advantages enable deep learning-based methods to overcome the main challenges of traditional approaches, providing more accurate and reliable EEG signal recovery. The superior denoising performance of deep learning-based EEG denoising methods has attracted increasing attention in EEG artifact removal research. As a result, many deep learning-based denoising methods have been developed and successfully applied in neural engineering areas. However, a systematic review of the techniques and applications in this field is still lacking. To address this gap, this paper reviews recent advances in deep learning-based EEG denoising from four perspectives: technical principle, benchmark dataset, denoising model, and evaluation method. Representative applications in neural signal analysis and BCI decoding are also summarized. Furthermore, the advantage, existing challenge, and future research direction of deep learning-based EEG denoising are discussed. This review aims to provide valuable theoretical insights and technical guidance for researchers. It is also expected to promote further advances and broader applications of deep learning-based EEG denoising techniques.

Alzheimer’s disease (AD) is a central neurodegenerative disease characterized by progressive cognitive decline and memory impairment in clinical. Currently, there are no effective treatments for AD. In recent years, a variety of therapeutic approaches from different perspectives have been explored to treat AD. Although the drug therapies targeted at the clearance of amyloid β-protein (Aβ) had made a breakthrough in clinical trials, there were associated with adverse events. Neuroinflammation plays a crucial role in the onset and progression of AD. Continuous neuroinflammatory was considered to be the third major pathological feature of AD, which could promote the formation of extracellular amyloid plaques and intracellular neurofibrillary tangles. At the same time, these toxic substances could accelerate the development of neuroinflammation, form a vicious cycle, and exacerbate disease progression. Reducing neuroinflammation could break the feedback loop pattern between neuroinflammation, Aβ plaque deposition and Tau tangles, which might be an effective therapeutic strategy for treating AD. Traditional Chinese herbs such as Polygonum multiflorum and Curcuma were utilized in the treatment of AD due to their ability to mitigate neuroinflammation. Non-steroidal anti-inflammatory drugs such as ibuprofen and indomethacin had been shown to reduce the level of inflammasomes in the body, and taking these drugs was associated with a low incidence of AD. Biosynthetic nanomaterials loaded with oxytocin were demonstrated to have the capability to anti-inflammatory and penetrate the blood-brain barrier effectively, and they played an anti-inflammatory role via sustained-releasing oxytocin in the brain. Transplantation of mesenchymal stem cells could reduce neuroinflammation and inhibit the activation of microglia. The secretion of mesenchymal stem cells could not only improve neuroinflammation, but also exert a multi-target comprehensive therapeutic effect, making it potentially more suitable for the treatment of AD. Enhancing the level of TREM2 in microglial cells using gene editing technologies, or application of TREM2 antibodies such as Ab-T1, hT2AB could improve microglial cell function and reduce the level of neuroinflammation, which might be a potential treatment for AD. Probiotic therapy, fecal flora transplantation, antibiotic therapy, and dietary intervention could reshape the composition of the gut microbiota and alleviate neuroinflammation through the gut-brain axis. However, the drugs of sodium oligomannose remain controversial. Both exercise intervention and electromagnetic intervention had the potential to attenuate neuroinflammation, thereby delaying AD process. This article focuses on the role of drug therapy, gene therapy, stem cell therapy, gut microbiota therapy, exercise intervention, and brain stimulation in improving neuroinflammation in recent years, aiming to provide a novel insight for the treatment of AD by intervening neuroinflammation in the future.

Aim To elucidate whether Astragaloside Ⅳcould ameliorate pathological myocardial hypertrophy and fibrosis via the EGR1-SIRT1-PPARα-SCAD signa-ling pathway in TAC mice.Methods After randomi-zing mice into groups,the Sham+AS-Ⅳ group and TAC+AS-Ⅳ group were intragastrically administered 20 mg·kg-1AS-Ⅳ once daily,whereas the Sham+NS group and TAC+NS group were given equivalent saline.Six weeks post-surgery,an evaluation of cardiac function was conducted,heart weight index was compu-ted,morphological alterations in heart were noted,vari-ations in collagen and myocardial hypertrophy indexes were analyzed,ATP content,free fatty acid content,hydroxyproline content,SCAD expression,and enzyme activity were measured,and an initial investigation into the protein expression of EGR1-SIRT1-PPARα-SCAD in myocardial tissues was undertaken.Results After AS-Ⅳ intervention,the heart weight index of TAC mice decreased(P＜0.01),LVAWd,LVAWs,LVPWd and LVPWs values decreased(P＜0.01,P＜0.05),EF％and FS％values increased(all P＜0.01),myocardial hypertrophy markers and collagen area decreased,FFA content,HYP content and collagen expression de-creased(all P＜0.01),SCAD enzyme activity and ex-pression increased(P＜0.01,P＜0.05),and ATP content increased(P＜0.01).The expression of EGR1 protein decreased,and the expression of SIRT1 and PPARα protein increased(all P＜0.01).Conclu-sions AS-Ⅳ may improve fatty acid oxidation via the EGR1-SIRT1-PPARα-SCAD signaling pathway,thereby ameliorating pathological myocardial hypertrophy and fibrosis in TAC model mice.

Aim To explore the mechanism by which dioscin regulates IL-17+γδT cells in the treatment of arthritis.Methods A collagen-induced arthritis(CIA)model was established in DBA/1 mice using bovine type Ⅱ collagen.The mice were randomly divid-ed into the CIA model group,methotrexate(MTX)positive control group,and dioscin low-dose(Dioscin-L),medium-dose(Dioscin-M),and high-dose(Dios-cin-H)groups.After intervention,the therapeutic effects were evaluated using scoring methods.Joint pathological damage was analyzed by hematoxylin and eosin(HE)staining.The levels of anti-collagen-spe-cific antibodies and the pro-inflammatory cytokine IL-17 were measured by ELISA.The expressions of γδT cells and their subtypes,as well as the secretion level of IL-17,were detected by flow cytometry.Results Dioscin significantly reduced the arthritis severity score in collagen-induced arthritis(CIA)mice,alleviated joint pathological damage,inhibited the production of IL-17 by splenic lymphocytes and the levels of anti-col-lagen-specific antibodies total IgG and IgG3,and de-creased the proportion of γδT cells in the lymph nodes,splenic γδT cells,and the Vδ4+T-cell subset.The level of IL-17 produced by the Vδ4 subtype in the lymph nodes of the intervention groups was lower than that in the model group,but the difference was not sta-tistically significant.Conclusion Dioscin has signifi-cant therapeutic effect on CIA,and its mechanism may be through the inhibition of γδT cells,but it is unlikely to be related to IL-17 derived from γδT cells.

Objective To construct a medical consumables selection indicator system based on analytic hierarchy process(AHP)and fuzzy cluster method.Methods Firstly,a medical consumables selection indicator system was established preliminarily with the literature research results and actual situation of hospital consumables management;secondly,13 experts in related fields were selected to execute two rounds of online questionnaires,the expert weights were determined with AHP,the importance of each selection indicator was scored by the expert evaluation method and fuzzy cluster method,and consistency analysis was carried out on the two rounds of expert evaluation results;finally,the final medical consumables selection indicator system was built with the 100-point scale.Results The constructed medical consumables selection indicator system was composed of 5 primary indicators,12 secondary indicators and 38 tertiary indicators.The primary indicators included consumables quality,clinical demand,cost-effectiveness,supply capacity and after-sales service,with the percentage-based scores being 27,25,14,26 and 8,respectively.Conclusion The medical consumables selection indicator system based on AHP and fuzzy cluster method with high reliability provides effective and reliable references for medical institutions to select medical consumables.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Objective To investigate whether successful percutaneous coronary intervention(PCI)could improve symptoms and quality of life(QOL)in left main disease and/or 3-vessel disease patients with diabetes.Methods Patients with left main disease and/or 3-vessel disease who underwent PCI in the First Affiliated Hospital of Air Force Medical University from April 2018 to May 2021 were consecutively enrolled and subdivided into 2 groups:diabetes and no diabetes.Detailed baseline characteristics,symptoms,including dyspnea and angina,assessed with the Rose dyspnea scale(RDS),Seattle angina questionnaire(SAQ),the European quality of life-5 dimensions(EQ-5D)and 12-item short-form health survey(SF-12)questionnaire respectively,procedural details,and 1 month and 1 year follow-up data were collected.Results Among 440 left main disease and/or 3-vessel disease patients,disease was present in 176(40.00％),who had more hypertension,peripheral artery disease,and LCX lesion(all P＜0.05).The incidence of major adverse cardiovascular events(MACE)and all-cause mortality were similar between the two groups(both P＞0.05)at 1 month follow-up,while all-cause mortality in diabetes patients was significantly higher than those without diabetes at 1 year follow-up(P=0.013).Low left ventricular ejection fraction was an independent risk factor for MACE and all-cause mortality at 1 month and 1 year follow-up after successful revascularization(all P＜0.05).Most importantly,symptoms,including dyspnea and angina,and QOL were markedly improved regardless of diabetes both at 1 month and 1 year follow-up(all P＜0.05).Diabetes patients showed improved dyspnea and QOL at similar degree to the non-diabetes patients(all P＞0.05)and a more significantly relieved angina(P=0.013).Additionally,the number of chronic total occlusion(CTO)per patient was identified as an independent risk factor of dyspnea(OR 0.723,95％CI 0.525～0.997,P=0.048)and angina relief(OR 0.686,95％CI 0.473～0.995,P=0.047),and the contrast volume(OR 0.995,95％CI 0.992～0.999,P=0.008)as an independent risk factor of QOL improvement in diabetic patients.Conclusions Successful PCI is beneficial for relieving symptoms and improving quality of life in patients with diabetes who have left main disease and/or 3-vessel disease.

The scientific research and innovation capabilities of medical students are intrinsically linked to the sustained and high-quality development of national healthcare initiatives.Cultivating outstanding medi-cal students with independent scientific capabilities and innovative consciousness is a critical component in the education and training of high-level medical professionals.Our investigation revealed that within the imperfections of the cultivating model,some faculty and students at medical schools have an insufficient understanding of scientific research and innovation and lack motivation for engaging in such activities,which hinder the progression of scientific research activities.Consequently,we initiated a teaching practice and exploratory study on the"tutorial system"aimed at fostering medical students'scientific research and innovation abilities.Based on the principle of"research informing teaching,teaching and research advan-cing together,"this study implements a"tutorial system"coordinated by tutors,supplemented by graduate and undergraduate student mentors,to cultivate innovative thinking,stimulate interest in scientific re-search,and enhance practical and research skills among medical students.Through collaborative efforts within"scientific research innovation teams,"various educational methods—including preliminary re-search,in-class and extracurricular activities,intra-group and inter-group interactions,and theoretical and practical applications—are employed to improve and strengthen the cultivation of medical students'scientif-ic research and innovation abilities.This study aims to provide valuable references for optimizing medical education management systems and enhancing the quality of medical student training.

Aim To explore the mechanism by which dioscin regulates IL-17+γδT cells in the treatment of arthritis.Methods A collagen-induced arthritis(CIA)model was established in DBA/1 mice using bovine type Ⅱ collagen.The mice were randomly divid-ed into the CIA model group,methotrexate(MTX)positive control group,and dioscin low-dose(Dioscin-L),medium-dose(Dioscin-M),and high-dose(Dios-cin-H)groups.After intervention,the therapeutic effects were evaluated using scoring methods.Joint pathological damage was analyzed by hematoxylin and eosin(HE)staining.The levels of anti-collagen-spe-cific antibodies and the pro-inflammatory cytokine IL-17 were measured by ELISA.The expressions of γδT cells and their subtypes,as well as the secretion level of IL-17,were detected by flow cytometry.Results Dioscin significantly reduced the arthritis severity score in collagen-induced arthritis(CIA)mice,alleviated joint pathological damage,inhibited the production of IL-17 by splenic lymphocytes and the levels of anti-col-lagen-specific antibodies total IgG and IgG3,and de-creased the proportion of γδT cells in the lymph nodes,splenic γδT cells,and the Vδ4+T-cell subset.The level of IL-17 produced by the Vδ4 subtype in the lymph nodes of the intervention groups was lower than that in the model group,but the difference was not sta-tistically significant.Conclusion Dioscin has signifi-cant therapeutic effect on CIA,and its mechanism may be through the inhibition of γδT cells,but it is unlikely to be related to IL-17 derived from γδT cells.