ObjectiveTo analyze the current research status, hotspots and future trends of the application of digital health technology (DHT) in the rehabilitation of upper limb motor function after stroke. MethodsRelevant literature on the application of DHT in upper limb motor rehabilitation for stroke patients published between January, 2015 and December, 2025 was retrieved from Web of Science Core Collection, CNKI and Wanfang database. CiteSpace 6.4.R1 was used for visualized bibliometric analysis. ResultsA total of 1 295 publications were included, comprising 454 in Chinese and 841 in English. The annual number of publications generally showed an upward trend. China ranked first in publication output in English literature. The institutions with the highest numbers of publications were Huashan Hospital Affiliated to Fudan University and Swiss Federal Institute of Technology in Zurich. Both Chinese and English keywords formed ten clustering groups. Chinese clusters mainly involved occupational therapy, neural mechanisms and home-based rehabilitation, whereas English clusters focused on virtual reality, brain-computer interfaces and machine learning. High-frequency keywords included virtual reality, brain-computer interface, machine learning and deep learning. Chinese keywords with a strong burst included rehabilitation training, while deep learning showed a strong burst in English keywords. Stroke was the most frequently cited journal. Highly cited journals covered multiple disciplines, including rehabilitation medicine, neuroscience and computer science, reflecting the interdisciplinary characteristics of this field. ConclusionResearches on DHT for upper limb motor function rehabilitation in stroke are increasing annually, focusing on core interaction technologies, neural mechanism and artificial intelligence. Future research trends may include inter-disciplinary integration of artificial intelligence with core rehabilitation technologies, neuroimaging-guided targeted interventions, optimisation of home-based rehabilitation systems, and development of multidimensional quantitative assessment models.

Background Long working hours, as a common risk factor for occupational stress, is closely related to the occurrence of depressive symptoms. Understanding how long working hours affect occupational stress and depressive symptoms will inform occupational health interventions. Objective To quantify the impact of long working hours exposure on occupational stress and depressive symptoms among Internet industry employees, translate black-box outputs into actionable insights, and demonstrate the value of interpretable machine learning for early-warning occupational-health surveillance. Methods A dataset was derived from a cross-sectional survey involving 2866 internet industry employees in China. This survey was part of the project Risk Assessment Of Long Working Hour Exposure And Its Adverse Health Effects, conducted by the National Institute for Occupational Health and Poisoning Control, Chinese Center for Disease Control and Prevention, from 2021 to 2023. Working hours, occupational stress and depressive symptoms were quantified with a set of structured questionnaires including the Core Occupational Stress Scale and the Patient Health Questionnaire. Pairwise associations were screened by Mantel tests and variance-inflation factors. Key predictors identified through feature selection were fed into six machine-learning risk-prediction models. Visual interpretation was provided by feature importance, Shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME), while directed causal effects and intervention impacts of prolonged working hours exposure on occupational stress and depressive symptoms were dissected with causal explanation of features techniques. Results The positive rates of occupational stress and depressive symptoms among internet employees were 12.9% and 77.8% respectively. Twelve core features for occupational stress and nine for depressive symptoms were retained after selection. After these features were supplied to six predictive algorithms and evaluated on five metrics, the Light Gradient Boosting Machine (LGBM) achieved the highest accuracy—0.89 for occupational stress and 0.79 for depressive symptoms on the hold-out test set. The feature-importance rankings converged on fatigue accumulation and life satisfaction as dominant drivers for both outcomes, whereas weekly working hours and daily overtime emerged as the principal exposure-related predictors. The SHAP summary plots revealed that longer weekly hours and daily overtime systematically elevated the probability of occupational stress. The causal feature explanation further quantified that ascending one category in weekly working hours increased the probability of occupational stress by 7.04%. Conclusion Exposure to long working hours is associated with both occupational stress and depressive symptoms among internet industry employees. Interpretable machine-learning frameworks translate these associations into transparent, defensible drivers, enabling precise identification of the pivotal factors and their interplay. This evidence base equips occupational-health practitioners with actionable insights for designing targeted prevention and intervention strategies.

ObjectiveTo assess the efficiency of a Sophora flavescens Ait (S. flavescens, Ku Shen)-soluble microneedle (SFA-MN) for improving skin lesion symptoms in mice with psoriasis.MethodsSFA-MNs were prepared using a two-mold molding process with 20% w/v polyvinylpyrrolidone and 15% w/v polyvinyl alcohol. The SFA-MNs were assessed for morphology, mechanical properties, in vitro dissolution, identification of components, and skin lesion improvement in imiquimod-induced psoriasis mice.ResultsThe SFA-MNs demonstrated good mechanical properties for efficiently penetrating the dermis, facilitating efficient drug delivery. Furthermore, they effectively inhibited mast cell levels in the dorsal lesion area of psoriasis mice and reduced the expression of the T-lymphocyte factor cluster of differentiation 3 and tumor necrosis factor-α. In addition, this system alleviated skin inflammation, splenic swelling, and thymic atrophy in the psoriasis-like mouse model. Seven major components were detected from SFA-MNs by comparison of the mass-to-nucleus ratios (m/z) of the secondary fragments N-methylcytisine, 5α, 9α-dihydroxymatrine, sophoramine, matrine, oxysophocarpine, oxymatrine, and kushenol O.ConclusionThe drug delivery strategy combining traditional herbal S. flavescens with soluble microneedle technology provides more targeted and effective immune regulation for treating psoriasis-like mice models, enabling enhanced therapeutic effects compared with the control group.

Airway assessment is an important prerequisite for airway management.Difficult airway(DA)is a major challenge in anes-thesia management,and failure to predict DA in advance may lead to serious adverse events such as failed intubation and hypoxemia.Accurate assessment and prediction of possible DA before surgery is of vital importance.Traditional assessment systems rely on the ex-perience of the physician experience for interpretation,with a low sensitivity and difficulties in identifying hidden anatomical abnor-malities.Relying on powerful learning and data analysis capabilities,artificial intelligence(AI)establishes a predictive model for DA by integrating multimodal data on the basis of machine learning and deep learning,thereby improving the efficiency and accuracy of identifying abnormal anatomical structures in the airway,which helps to significantly improve prediction efficiency,develop a targeted regimen,implement effective management,and ensure the safety and quality of anesthesia.This article reviews the research on the ap-plication of AI in predicting DA in recent years.

Background Occupational stress has emerged as a critical public health concern affecting the physical and mental well-being of workers in the manufacturing sector. However, researchers typically evaluate its core driver—cumulative fatigue—using a crude binary “present/absent” variable, thereby overlooking the high-dimensional complexity and heterogeneity inherent in fatigue characteristics. This oversimplification constrains both the precision and predictive performance of occupational stress risk assessment model. Objective Leveraging a data-driven approach, to survey data on cumulative fatigue among manufacturing employees, and then use this new classification to develop and validate an occupational stress prediction model, with an ultimate aim of enhancing the accuracy and effectiveness of occupational stress assessment. Methods A set of cross-sectional survey data on 3871 manufacturing employees in 2021 were derived from the “Long working hours exposure and its adverse health effect risk assessment” program of the National Institute for Occupational Health and Poison Control, Chinese Center for Disease Control and Prevention. Occupational stress was assessed using the Core Occupational Stress Measurement Scale, while cumulative fatigue was evaluated via the Worker’s Self-Diagnostic Questionnaire for Fatigue Accumulation. Boruta method was applied to screen core variables from 20 occupational stress influencing factors. Dimensionality reduction of cumulative fatigue features was performed using a Bayesian sparse autoencoder (BASE), followed by comparison and application of clustering methods to achieve multi-class classification of the reduced features. Six machine learning classification models were then selected including logistic regression, support vector machine, decision tree, random forest, adaptive boosting, and lightweight gradient boosting machine (LightGBM) to construct and compare two occupational stress prediction models involving cumulative fatigue combined with ten other core variables: one utilizing the original binary cumulative fatigue label as a core variable, and another employing the novel fatigue classification proposed herein as a core variable. Results The positive rate of occupational stress among the manufacturing employees was 38.9%. The Boruta method identified 11 core variables of occupational stress: depressive symptoms, cumulative fatigue, age, length of service, tenure in current position, average weekly working hours, average daily overtime hours, average monthly income, low levels of exercise, life satisfaction, and sleep quality. The BSAE reduced the original cumulative fatigue factors to 12 dimensions, while the K-means clustering grouped the reduced fatigue features into three categories: no fatigue, moderate fatigue, and sever fatigue. Six occupational stress prediction models were constructed using the three-category labels of cumulative fatigue and ten additional factors. The results indicated that the LightGBM model performed best, achieving an area under the curve (AUC) of 0.78, an accuracy of 0.77, and an F1 score of 0.72. Compared with the model incorporating the traditional binary labels for cumulative fatigue, the best prediction AUC (0.72), accuracy (0.66), and F1 score (0.65) improved by 6%, 11%, and 7%, respectively. Conclusion Cumulative fatigue is a significant predictor of occupational stress among manufacturing employees. Applying data dimensionality reduction combined with three-class cluster analysis to characterize cumulative fatigue improves the performance of occupational stress prediction models. From a data-driven perspective, machine learning methods demonstrate strengths in processing complex datasets, capturing nonlinear relationships, and generating predictions based on key variables. This study therefore confirms that refined processing of core factors substantially enhances the predictive capability of machine learning models in assessing occupational stress risk in the manufacturing workforce.

Interstitial cystitis/bladder pain syndrome (IC/BPS) is a refractory condition characterized by chronic inflammation of the bladder wall, disruption of the urothelial barrier, and neural sensitization. Current therapies, such as oral pentosan polysulfate sodium (PPS) or intravesical hyaluronic acid instillations, offer limited efficacy due to transient effects and an inability to reverse tissue fibrosis. Platelet-rich plasma (PRP), a regenerative medicine approach, has demonstrated significant therapeutic potential in urological disorders through the synergistic actions of its multiple growth factors. This review summarizes the latest advances in PRP therapy for IC/BPS, revealing that the underlying mechanisms primarily involve the release of diverse growth factors, suppression of inflammatory responses, restoration of the urothelial barrier, and modulation of nerve axonal regeneration. Clinically, PRP therapy significantly alleviates symptoms including pelvic/bladder pain, urinary frequency, nocturia episodes, and improves patients'quality of life. Furthermore, it offers advantages such as convenient administration, a favorable safety profile, and strong feasibility, presenting new therapeutic methods and options for the clinical treatment of IC/BPS.

Invasive fungal infections (IFIs) have become prominent global health threats, escalating the burden on public health systems. The increasing occurrence of invasive fungal infections is due primarily to the extensive application of chemotherapy, immunosuppressive therapies, and broad-spectrum antifungal agents. At present, therapeutic practices utilize multiple categories of antifungal agents, such as azoles, polyenes, echinocandins, and pyrimidine analogs. Nevertheless, the clinical effectiveness of these treatments is progressively weakened by the emergence of drug resistance, thereby substantially restricting their therapeutic utility. Consequently, there is an imperative need to expedite the discovery of novel antifungal agents. This review seeks to present an exhaustive synthesis of novel antifungal drugs and candidate agents that are either under current clinical investigation or anticipated to progress into clinical evaluation. These emerging compounds exhibit unique benefits concerning their modes of action, antimicrobial spectra, and pharmacokinetic characteristics, potentially leading to improved therapeutic outcomes relative to conventional antifungal regimens. It is anticipated that these novel therapeutic agents will furnish innovative treatment modalities and enhance clinical outcomes in managing invasive fungal infections.

Objective:To explore the prevalence and risk factors of insomnia in Chinese Air Force servicemen deployed to highland areas.Methods:A total of 718 Air Force servicemen deployed to Qinghai-Tibetan plateau were recruited at May 2024.Sleep quality was assessed with the Pittsburgh Sleep Quality Index.Social-demograph-ic,military service,and psychological characteristics were measured with a self-administered general question-naire.Bivariable and multivariable logistic regressions were performed to identify independent risk factors.Missing data were handled by the multiple imputation.Results:The average sleep duration was(6.9±1.2)h and the aver-age PSQI score was(5.9±4.1).Totally 53.8％of participants experienced clinically significant insomnia.The multivariable analysis revealed that age≥35(aOR=4.07,95％CI=1.11-17.76),stressful event(aOR=3.27,95％CI=2.00-5.49),dysfunctional sleep beliefs and attitudes(aOR=2.59,95％CI=1.75-3.85),and caffeine product usage(aOR=1.69,95％CI=1.17-2.43)were risk factors for insomnia,while Tibetan-indigenous ethnic(aOR=0.44,95％CI=0.20-0.91),higher perceived social support(aOR=0.96,95％CI=0.96-0.99),and positive coping style(aOR=0.96,95％CI=0.93-0.99)were protective factors.Conclusion:Air force service-men deployed to highland areas have sufficient sleep time,but reduced sleep quality.Age,exposed to stress event during deployment,dysfunctional sleep beliefs and attitudes,and caffeine product usage are risk factors for insomni-a,while Tibetan-indigenous ethnic,higher perceived social support and positive coping style act as protective fac-tors.

Objective:To investigate the risk factors associated with the development of venous thromboembolism with abdominal infection following pancreatic cancer surgery.Methods:A retrospective analysis was conducted on the clinical data of 70 patients with pancreatic cancer who underwent pancreaticoduodenectomy at the Department of Hepatobiliary Surgery of the First Affiliated Hospital of Air Force Medical University from March 2020 to August 2024, of which 31 were male and 39 were female, aged (58.5±6.8) years. Two groups were divided into two groups according to the occurrence of venous thromboembolism with abdominal infection in the postoperative period: the complication group ( n=28) and the non-complication group ( n=42). The general data, laboratory indexes and surgery-related indexes of the two groups were recorded, and logistic regression model was used to analyse the risk factors for the occurrence of venous thromboembolism with abdominal cavity infection after pancreaticoduodenectomy, and receiver operating characteristic (ROC) curve was used to assess the predictive value of the factors for the occurrence of venous thromboembolism with abdominal cavity infection after pancreatic cancer surgery. Results:Multifactorial analysis showed that the combination of diabetes mellitus ( OR=1.216, 95% CI: 1.225-1.657, P=0.035), occurrence of postoperative pancreatic fistula ( OR=1.292, 95% CI: 1.095-1.829, P=0.022), occurrence of postoperative biliary fistula ( OR=1.239, 95% CI: 1.171-2.618, P=0.005), high D-dimer ( OR=137.030, 95% CI: 3.214-5 843.128, P=0.010), prolonged operation time ( OR=1.058, 95% CI: 1.002-1.117, P=0.042), and low serum albumin ( OR=0.711, 95% CI: 0.508-0.995, P=0.046) in pancreatic cancer patients with a high risk of postoperative venous thromboembolism with the development of abdominal infection. ROC curve analysis showed that diabetes mellitus, pancreatic fistula, biliary fistula, D-dimer, duration of surgery, serum albumin, and co-prediction had certain predictive value for the occurrence of postoperative venous thromboembolism with abdominal infection after pancreatic cancer surgery, in which, the area under the curve for co-prediction in assessing the occurrence of postoperative venous thromboembolism with abdominal infection after pancreatic cancer surgery was 0.993 (95% CI: 0.982-1.000), with a sensitivity of 1.000 and a specificity of 0.929. Conclusion:Comorbid diabetes mellitus, pancreatic fistula, biliary fistula, D-dimer, prolonged duration of surgery, and serum albumin are independent risk factors for the development of postoperative venous thromboembolism with abdominal infection in patients with pancreatic cancer, all of which have diagnostic value and a high joint predictive value. The predictive value of co-prediction is relatively high.

Objective:To investigate the risk factors associated with the development of venous thromboembolism with abdominal infection following pancreatic cancer surgery.Methods:A retrospective analysis was conducted on the clinical data of 70 patients with pancreatic cancer who underwent pancreaticoduodenectomy at the Department of Hepatobiliary Surgery of the First Affiliated Hospital of Air Force Medical University from March 2020 to August 2024, of which 31 were male and 39 were female, aged (58.5±6.8) years. Two groups were divided into two groups according to the occurrence of venous thromboembolism with abdominal infection in the postoperative period: the complication group ( n=28) and the non-complication group ( n=42). The general data, laboratory indexes and surgery-related indexes of the two groups were recorded, and logistic regression model was used to analyse the risk factors for the occurrence of venous thromboembolism with abdominal cavity infection after pancreaticoduodenectomy, and receiver operating characteristic (ROC) curve was used to assess the predictive value of the factors for the occurrence of venous thromboembolism with abdominal cavity infection after pancreatic cancer surgery. Results:Multifactorial analysis showed that the combination of diabetes mellitus ( OR=1.216, 95% CI: 1.225-1.657, P=0.035), occurrence of postoperative pancreatic fistula ( OR=1.292, 95% CI: 1.095-1.829, P=0.022), occurrence of postoperative biliary fistula ( OR=1.239, 95% CI: 1.171-2.618, P=0.005), high D-dimer ( OR=137.030, 95% CI: 3.214-5 843.128, P=0.010), prolonged operation time ( OR=1.058, 95% CI: 1.002-1.117, P=0.042), and low serum albumin ( OR=0.711, 95% CI: 0.508-0.995, P=0.046) in pancreatic cancer patients with a high risk of postoperative venous thromboembolism with the development of abdominal infection. ROC curve analysis showed that diabetes mellitus, pancreatic fistula, biliary fistula, D-dimer, duration of surgery, serum albumin, and co-prediction had certain predictive value for the occurrence of postoperative venous thromboembolism with abdominal infection after pancreatic cancer surgery, in which, the area under the curve for co-prediction in assessing the occurrence of postoperative venous thromboembolism with abdominal infection after pancreatic cancer surgery was 0.993 (95% CI: 0.982-1.000), with a sensitivity of 1.000 and a specificity of 0.929. Conclusion:Comorbid diabetes mellitus, pancreatic fistula, biliary fistula, D-dimer, prolonged duration of surgery, and serum albumin are independent risk factors for the development of postoperative venous thromboembolism with abdominal infection in patients with pancreatic cancer, all of which have diagnostic value and a high joint predictive value. The predictive value of co-prediction is relatively high.