Rheumatoid arthritis （RA） is a common autoimmune disease characterised clinically by symmetrical joint pain， swelling， and stiffness. Long-term chronic synovial inflammation can lead to severe joint damage and even disability， thereby affecting quality of life for patients. Current clinical treatment of RA emphasises an integrated approach combining traditional Chinese and Western medicine， with traditional Chinese medicine offering certain advantages in reducing disease activity of RA， preventing relapses， and other aspects. Modern clinical evidence confirms that Guizhi Shaoyao Zhimutang （GSZT） is effective in improving symptoms such as immune metabolism， joint stiffness， and joint pain in RA patients. Pharmacological studies have revealed that GSZT primarily contains components such as cinnamaldehyde， total glucosides of paeony， total alkaloids of Aconiti Lateralis Radix Praeparata， glycyrrhetinic acid， zingiberone， isoimperatorin， ephedra polysaccharides， and cedrol. It improves RA symptoms via multiple mechanisms and targets， including enhancing immune responses， exerting anti-inflammatory and analgesic effects， regulating relevant signalling pathways， inhibiting cell apoptosis， and suppressing bone destruction. This paper reviewed the syndrome patterns and pharmacological basis of GSZT in the treatment of RA， as well as its clinical applications and related mechanisms， thereby providing a theoretical basis and reference for the further development and utilisation of GSZT in the treatment of RA.

Rheumatoid arthritis （RA） is a common autoimmune disease characterised clinically by symmetrical joint pain， swelling， and stiffness. Long-term chronic synovial inflammation can lead to severe joint damage and even disability， thereby affecting quality of life for patients. Current clinical treatment of RA emphasises an integrated approach combining traditional Chinese and Western medicine， with traditional Chinese medicine offering certain advantages in reducing disease activity of RA， preventing relapses， and other aspects. Modern clinical evidence confirms that Guizhi Shaoyao Zhimutang （GSZT） is effective in improving symptoms such as immune metabolism， joint stiffness， and joint pain in RA patients. Pharmacological studies have revealed that GSZT primarily contains components such as cinnamaldehyde， total glucosides of paeony， total alkaloids of Aconiti Lateralis Radix Praeparata， glycyrrhetinic acid， zingiberone， isoimperatorin， ephedra polysaccharides， and cedrol. It improves RA symptoms via multiple mechanisms and targets， including enhancing immune responses， exerting anti-inflammatory and analgesic effects， regulating relevant signalling pathways， inhibiting cell apoptosis， and suppressing bone destruction. This paper reviewed the syndrome patterns and pharmacological basis of GSZT in the treatment of RA， as well as its clinical applications and related mechanisms， thereby providing a theoretical basis and reference for the further development and utilisation of GSZT in the treatment of RA.

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.

With industrial restruction, workplace transformation, and the emergence of new materials, technologies, and processes, mental health issues among working population have become a pressing concern in occupational health. Digital interventions, supported by their accessibility and personalization capabilities, may offer a promising solution to this challenge. This article reviewed key concepts and the developmental history of digital interventions and occupational health psychology, and systematically examined representative intervention approaches and their effectiveness in addressing workplace mental health concerns. It further explored the challenges and corresponding strategies in this field, including strengthening the evidence-based foundation, integrating theoretical models, incorporating organizational and ecological factors, and addressing technological issues. In the future, strengthening interdisciplinary and cross-sector collaboration should be enhanced to optimize and promote the application of digital mental health services for working population, thereby promoting workplace well-being and advancing the sustainable development of digital interventions.

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.

Objective: To investigate the effect and mechanism of methyltransferase-like3(METTL3),which functions as m6A modification methyltransferase,on the cellular proliferation,clone formation and migration of nonsmall cell lung cancer(NSCLC) cells. Methods: The METTL3 level and patient overall survival were analyzed by bioinformatics.In the transfected A549 cells,the expression of METTL3 was detected by RT-PCR and Western blot assays,the m6A level was detected by m6A RNA Methylation Assay kit,the cellular viability and growth were detected by CCK-8 assay,the apoptosis was detected by immunofluorescent assay,the colony growth was detected by clone formation assay,the cell migration growth was detected by scratch. Results : Bioinformatics showed that METTL3 was highly expressed in NSCLC and negatively correlated with patient overall survival.The RT-PCR and Western blot data showed that the METTL3 level was higher in NSCLC cells.Moreover,higher METTL3 significantly promoted m6A level,cell proliferation,colony formation,migration,inhibited cell apoptosis,increased the mRNA and protein expressions of EMT-related marker Vimentin but inhibited apoptosis and decreased the mRNA and protein expressions of EMT-related marker E-cadherin in the A549 cells.Furthermore,the contrasting results were obtained in the A549 cells with the knockdown of METTL3. Conclusion The over-expressed METTL3 increases the m6A levels in NSCLC cells and promotes cellular proliferation,colony formation,and migration growth,thereby laying a theoretical foundation for future research on early diagnosis and targeted drug development for NSCLC by targeting METTL3.

OBJECTIVE: To study the genetic polymorphism of red blood cell blood group among in Shenzhen Han, Indian and Xinjiang Uyghur populations, to provide scientific basis for the demand prediction and collection strategy of rare blood group, and to explore the genetic differences of blood group between Han and Caucasians. METHODS: The haplotypes of antigen coding genes of 10 target blood group systems from 87 Han Chinese and 50 Indian blood donors in Shenzhen, and 49 healthy Uyghur people in Xinjiang were obtained by three-generation sequencing technology, and the polymorphism and frequency characteristics were analyzed. RESULTS: Only a single genotype was detected the Langereis and Vel blood group systems in samples from three different populations. Only one genotype of Dombrock blood group was detected in Shenzhen Han, and Junior blood group in Xinjiang Uygur populations. In the MNS, Duffy, Kidd, Dombrock and Junior blood group systems, the haplotype frequency of Indian and Uyghur people was significantly different from that of Han people. Compared with the Han ethnic group, the rare blood group s-, Fy(a-), Jk(a-b-), and Do(a+b-) have a higher frequency among the Uyghur and Indian populations. CONCLUSION Haplotype frequencies of antigen genes for MNS, Duffy, Kidd, Dombrock and Junior blood group system in Shenzhen Han, Indian and Uyghur populations displayed a polymorphic difference with unique distribution characteristics different from the ethnic groups in other regions.
Humans ; Blood Group Antigens/genetics* ; China/ethnology* ; Erythrocytes ; Ethnicity/genetics* ; Gene Frequency ; Genotype ; Haplotypes ; India/ethnology* ; Polymorphism, Genetic ; White People/genetics* ; Central Asian People/genetics* ; East Asian People/genetics*

BACKGROUND: Children's respiratory health demonstrates particular sensitivity to air pollution. Existing evidence investigating the association between short-term ozone (O₃) exposure and childhood pneumonia remains insufficient and inconsistent, especially in low- and middle-income countries (LMICs). METHOD: To provide more reliable and persuasive evidence, we implemented a multi-city, time-stratified case-crossover design with a large sample size, using data from seven representative children's hospitals across major geographical regions in China. To avoid the impact of the COVID-19 pandemic, individual-level medical records of inpatient children under 6 years of age diagnosed with pneumonia during 2016-2019 were collected. Conditional logistic regression models were fitted for each city, and city-specific estimates were pooled through a meta-analysis using a random-effects model. RESULTS: In total, the study included 137,470 pediatric pneumonia hospital admissions. The highest pooled estimate for O₃ occurred at lag0-1, with a 10 µg/m³ increase in O₃ associated with a 1.57% (95% CI: 0.67%-2.48%) higher risk of pediatric pneumonia hospital admissions. Stratified analyses indicated that the effects of O₃ were robust across different sexes, age groups, and admission seasons. We also observed a statistically significant increase in risk associated with O₃ concentrations exceeding the World Health Organization Air Quality Guidelines (WHO-AQGs). CONCLUSIONS This study revealed a significant positive association between O₃ and pediatric pneumonia hospital admissions. Our findings substantially strengthen the evidence base for the adverse health impacts of O₃, underscoring the importance of O₃ pollution control and management in reducing the public health burden of pediatric pneumonia.
Humans ; Ozone/analysis* ; China/epidemiology* ; Pneumonia/chemically induced* ; Child, Preschool ; Male ; Female ; Infant ; Cross-Over Studies ; Air Pollutants/analysis* ; Hospitalization/statistics & numerical data* ; Child ; Cities/epidemiology* ; Air Pollution/adverse effects* ; Infant, Newborn ; Environmental Exposure/adverse effects*

Apical microsurgery is accurate and minimally invasive, produces few complications, and has a success rate of more than 90%. However, due to the lack of awareness and understanding of apical microsurgery by dental general practitioners and even endodontists, many clinical problems remain to be overcome. The consensus has gathered well-known domestic experts to hold a series of special discussions and reached the consensus. This document specifies the indications, contraindications, preoperative preparations, operational procedures, complication prevention measures, and efficacy evaluation of apical microsurgery and is applicable to dentists who perform apical microsurgery after systematic training.
Microsurgery/standards* ; Humans ; Apicoectomy ; Contraindications, Procedure ; Tooth Apex/diagnostic imaging* ; Postoperative Complications/prevention & control* ; Consensus ; Treatment Outcome

Intentional tooth replantation (ITR) is an advanced treatment modality and the procedure of last resort for preserving teeth with inaccessible endodontic or resorptive lesions. ITR is defined as the deliberate extraction of a tooth; evaluation of the root surface, endodontic manipulation, and repair; and placement of the tooth back into its original socket. Case reports, case series, cohort studies, and randomized controlled trials have demonstrated the efficacy of ITR in the retention of natural teeth that are untreatable or difficult to manage with root canal treatment or endodontic microsurgery. However, variations in clinical protocols for ITR exist due to the empirical nature of the original protocols and rapid advancements in the field of oral biology and dental materials. This heterogeneity in protocols may cause confusion among dental practitioners; therefore, guidelines and considerations for ITR should be explicated. This expert consensus discusses the biological foundation of ITR, the available clinical protocols and current status of ITR in treating teeth with refractory apical periodontitis or anatomical aberration, and the main complications of this treatment, aiming to refine the clinical management of ITR in accordance with the progress of basic research and clinical studies; the findings suggest that ITR may become a more consistent evidence-based option in dental treatment.
Humans ; Tooth Replantation/methods* ; Consensus ; Periapical Periodontitis/surgery*