Objective: To investigate the distribution frequency and molecular mechanism of the rare blood type Lu(a-b-) in Shenzhen, and to compare the polymorphisms of the Lutheran blood group system encoding gene LU and the In (Lu) phenotype-related gene KLF1 among Han Chinese, Indian, and Uyghur populations in Xinjiang. Methods: Serological methods were used to screen the Lu(a-b-) phenotype of blood donors in Shenzhen. Third-generation sequencing was employed to sequence the full-length of the LU and KLF1 genes in Lu (a-b-) phenotype samples as well as the samples from the Han Chinese, Indians, and Uyghur population, followed by analysis of gene haplotypes frequencies. Results: Ten individuals with the Lu(a-b-) phenotype were screened out of 14 367 blood donors in Shenzhen, yielding a frequency of approximately 0.07%. Only 2 cases showed mutations in the coding region of the LU gene, while all individuals showed heterozygous mutations in the coding region of the KLF1 gene. The highest mutation frequencies of the LU and KLF1 genes were observed in the Uyghur population in Xinjiang and the Han Chinese in Shenzhen, respectively. Conclusion: All Lu(a-b-) phenotypes are of the In (Lu) type, and their formation mechanism is mainly related to KLF1 gene mutations. Both the LU and KLF1 genes exhibit significant polymorphism in the Han Chinese, Indians, and Uyghur populations.

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.

This narrative review examines the challenges, strategies, and future directions in the development of young teachers within the pathophysiology departments of Chinese medical colleges. A thorough review of 49 studies published between 2013 and 2024 was carried out using PubMed, Web of Science, and various Chinese databases. The primary challenges identified include teaching innovation (cited in 84.2% of the studies), research pressure (91.2%), disciplinary characteristics (87.7%), and career development (80.7%). Medical schools have responded by enhancing training systems (94.7%), innovating teaching methods (93.0%), and bolstering research support (96.5%). Looking ahead, trends are shifting toward the application of new technologies, interdisciplinary integration, and international collaboration. The focus on cultivating young teachers is increasingly geared towards personalization and diversification, which are essential for advancing education in pathophysiology. High-quality young teachers are pivotal in raising teaching standards, fostering research innovation, and facilitating interdisciplinary exchanges. Based on these insights, we recommend several practical measures to enhance the quality of pathophysiology education in China. These include establishing comprehensive training programs that integrate teaching innovation and research skills; developing structured mentorship systems with clear pathways for career advancement; creating platforms that support technology-enhanced teaching and international collaboration; and implementing systematic evaluation mechanisms to assess teaching effectiveness. These targeted interventions will require a coordinated effort from department heads, educational institutions, and policymakers to ensure a sustained improvement in the quality of pathophysiology education.

This narrative review examines the challenges, strategies, and future directions in the development of young teachers within the pathophysiology departments of Chinese medical colleges. A thorough review of 49 studies published between 2013 and 2024 was carried out using PubMed, Web of Science, and various Chinese databases. The primary challenges identified include teaching innovation (cited in 84.2% of the studies), research pressure (91.2%), disciplinary characteristics (87.7%), and career development (80.7%). Medical schools have responded by enhancing training systems (94.7%), innovating teaching methods (93.0%), and bolstering research support (96.5%). Looking ahead, trends are shifting toward the application of new technologies, interdisciplinary integration, and international collaboration. The focus on cultivating young teachers is increasingly geared towards personalization and diversification, which are essential for advancing education in pathophysiology. High-quality young teachers are pivotal in raising teaching standards, fostering research innovation, and facilitating interdisciplinary exchanges. Based on these insights, we recommend several practical measures to enhance the quality of pathophysiology education in China. These include establishing comprehensive training programs that integrate teaching innovation and research skills; developing structured mentorship systems with clear pathways for career advancement; creating platforms that support technology-enhanced teaching and international collaboration; and implementing systematic evaluation mechanisms to assess teaching effectiveness. These targeted interventions will require a coordinated effort from department heads, educational institutions, and policymakers to ensure a sustained improvement in the quality of pathophysiology education.

This narrative review examines the challenges, strategies, and future directions in the development of young teachers within the pathophysiology departments of Chinese medical colleges. A thorough review of 49 studies published between 2013 and 2024 was carried out using PubMed, Web of Science, and various Chinese databases. The primary challenges identified include teaching innovation (cited in 84.2% of the studies), research pressure (91.2%), disciplinary characteristics (87.7%), and career development (80.7%). Medical schools have responded by enhancing training systems (94.7%), innovating teaching methods (93.0%), and bolstering research support (96.5%). Looking ahead, trends are shifting toward the application of new technologies, interdisciplinary integration, and international collaboration. The focus on cultivating young teachers is increasingly geared towards personalization and diversification, which are essential for advancing education in pathophysiology. High-quality young teachers are pivotal in raising teaching standards, fostering research innovation, and facilitating interdisciplinary exchanges. Based on these insights, we recommend several practical measures to enhance the quality of pathophysiology education in China. These include establishing comprehensive training programs that integrate teaching innovation and research skills; developing structured mentorship systems with clear pathways for career advancement; creating platforms that support technology-enhanced teaching and international collaboration; and implementing systematic evaluation mechanisms to assess teaching effectiveness. These targeted interventions will require a coordinated effort from department heads, educational institutions, and policymakers to ensure a sustained improvement in the quality of pathophysiology education.

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.