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.

Moringa oleifera, widely utilized in Ayurvedic medicine, is recognized for its leaves, seeds, and velamen possessing traditional effects such as vātahara(wind alleviation), sirovirecaka(brain clearing), and hridya(mental nourishment). This study aims to identify the medicinal part of ■ in the Sārasvata ghee formulation as described in the Bower Manuscript, while investigating the ameliorative effects of different medicinal parts of M. oleifera on learning and memory deficits in mice and elucidating the underlying molecular mechanisms. A total of 144 male ICR mice were randomly assigned to the following groups: control, model(scopolamine hydrobromide, Sco, 2 mg·kg~(-1)), donepezil(donepezil hydrochloride, Don, 3 mg·kg~(-1)), M. oleifera leaf low-, medium-, and high-dose groups(0.5, 1, 2 g·kg~(-1)), M. oleifera seeds low-, medium-, and high-dose groups(0.25, 0.5, 1 g·kg~(-1)), and M. oleifera velamen low-, medium-, and high-dose groups(0.31, 0.62, 1.24 g·kg~(-1)). Learning and memory abilities were assessed using the passive avoidance test and Morris water maze. Nissl and HE staining were employed to examine histopathological changes in the hippocampus. Transcriptomics and targeted metabolomics were used to screen differential genes and metabolites, with MetaboAnalyst 6.0 and O2PLS methods applied to identify key disease-related targets and pathways. RESULTS:: demonstrated that M. oleifera leaf(1 g·kg~(-1)) significantly ameliorated Sco-induced learning and memory deficits, outperforming M. oleifera seeds(0.25 g·kg~(-1)) and M. oleifera velamen(1.24 g·kg~(-1)). This was evidenced by improved behavioral performance, reversal of neuronal damage, and reduced acetylcholinesterase(AChE) activity. Multi-omics analysis revealed that M. oleifera leaf upregulated Tuba1c gene expression through the synaptic vesicle cycle, enhancing glutamate(Glu), dopamine(DA), and acetylcholine(ACh) release via Tuba1c-Glu associations for neuroprotection. M. oleifera seeds targeted the dopaminergic synapse pathway, promoting memory consolidation through Drd2-ACh associations. M. oleifera velamen was associated with the cocaine addiction pathway, modulating dopamine metabolism via Adora2a-DOPAC, with limited relevance to learning and memory. In conclusion, M. oleifera leaf exhibits superior efficacy and mechanistic advantages over M. oleifera seeds and velamen, suggesting that the ■ in the Sārasvata ghee formulation is likely M. oleifera leaf, providing scientific evidence for its identification in ancient texts.
Animals ; Moringa oleifera/chemistry* ; Male ; Mice ; Seeds/chemistry* ; Plant Leaves/chemistry* ; Mice, Inbred ICR ; Memory Disorders/psychology* ; Transcriptome/drug effects* ; Memory/drug effects* ; Learning/drug effects* ; Metabolomics ; Humans ; Drugs, Chinese Herbal/administration & dosage* ; Maze Learning/drug effects*

OBJECTIVES: To investigate the potential circular RNA (circRNA)-microRNA (miRNA)-messenger RNA (mRNA) immune regulatory network in childhood allergic asthma by analyzing microarray datasets. METHODS: GEO database was used to obtain the datasets of circRNA, miRNA, and mRNA from children with allergic asthma and healthy controls. The Limma package was used to identify differentially expressed circRNA (DEcircRNA), miRNA (DEmiRNA), and mRNA (DEmRNA). ENCORI and other tools were used to predict and construct the regulatory network of endogenous RNA. The DAVID database was used to perform GO and KEGG enrichment analyses, and CIBERSORT and Pearson were used to identify genes associated with immune cell infiltration. RESULTS: A total of 130 DEcircRNAs, 40 DEmiRNAs, and 802 DEmRNAs were identified between the asthma and control groups, and a regulatory network consisting of 12 circRNAs, 7 miRNAs, and 75 mRNAs was established. The GO analysis showed that the differentially expressed genes were mainly involved in the regulation of growth and development, and the KEGG analysis showed that they were mainly involved in the mTOR signaling pathway. The CIBERSORT analysis showed that compared with the control group, the asthma group had higher percentages of CD8⁺ T cells and resting NK cells and lower percentages of resting CD4⁺ memory T cells and activated mast cells. In addition, the Pearson correlation analysis identified six key mRNAs that were positively correlated with immune cell infiltration. CONCLUSIONS The ceRNA immune regulatory network constructed in this study provides a basis for research on the mechanism of childhood allergic asthma and potential therapeutic targets.
Humans ; Asthma/genetics* ; RNA, Circular/physiology* ; MicroRNAs/physiology* ; Child ; Gene Regulatory Networks ; RNA, Messenger/physiology* ; RNA/physiology* ; Male ; Female ; Child, Preschool

OBJECTIVE: To assess the value of the Prostate Imaging Reporting and Data System version 2.1 (PI-RADS v2.1) score combined with PSA density (PSAD) in the diagnosis of clinically significant prostate cancer (CSPCa) in the PSA grey zone by MRI-TRUS cognitive fusion-guided transperineal targeted prostate biopsy. METHODS: This retrospective study included 327 male patients with total PSA (tPSA) levels of 4-10 μg/L undergoing MRI-TRUS cognitive fusion-guided transperineal targeted prostate biopsy in our hospital between January 2021 and December 2023. According to the pathological results, we divided the patients into a CSPCa (n = 44) and a non-CSPCa group (n = 283), collected their clinical and imaging data, and subjected them to statistical analysis. RESULTS: The age, tPSA level, PSAD and PI-RADS score were significantly higher, while the free PSA (fPSA) level, f/tPSA ratio and prostate volume remarkably lower in the CSPCa than in the non-CSPCa group (P<0.05). The areas under the curve (AUCs) of PSAD, PI-RADS score and their combination were 0.772, 0.730 and 0.801, with sensitivities of 63.63%, 70.45% and 72.73%, and specificities of 84.10%, 75.62% and 83.75%, respectively (P<0.01). With PSAD 0.2 μg/(ml·cm3) as the best cut-off value and based on the PI-RADS scores, the patients were divided into two groups for analysis. In the patients with PI-RADS scores 2 and 5, the AUCs were 0.534 and 0.643, with sensitivities of 16.67% and 63.64%, and specificities of 85.14% and 64.29%, with no statistically significant differences (P= 0.784, P= 0.228), and in those with PI-RADS scores 3 and 4, the AUCs were 0.794 and 0.843, with sensitivities of 57.14% and 80.00%, and specificities of 87.14% and 81.82%, with statistically significant differences (P= 0.009, P<0.001). CONCLUSION PI-RADS v2.1 score combined with PSAD can effectively improve the diagnostic efficiency of CSPCa in the PSA grey zone by MRI-TRUS cognitive fusion-guided transperineal targeted prostate biopsy and serve as a guide for selection of prostate biopsy.
Humans ; Male ; Prostatic Neoplasms/diagnostic imaging* ; Retrospective Studies ; Prostate-Specific Antigen ; Magnetic Resonance Imaging ; Image-Guided Biopsy ; Prostate/pathology* ; Aged ; Middle Aged

Objective:To quantify thoracic and abdominal movements during breathing using inertial measurement units (IMUs) and to build a machine learning model which identifies the abdominal breathing (AB) pattern.Methods:Ten rehabilitation therapists formed the study′s professional group, while 15 patients receiving AB training comprised the validation group. Two synchronized IMUs were applied to capture breathing motions during natural breathing (NB), deep breathing (DB) and AB. Six kinematic features were extracted from each respiratory cycle, and inter-group and inter-pattern differences were analyzed. Correlation analysis was also performed with manually measured changes in thoracic and abdominal circumferences. A support vector classification model for AB pattern detection was then developed using data from the professional and validation groups.Results:A total of 1113 respiratory cycles were extracted and analyzed. The breathing pattern significantly influenced all of the kinematic features studied (0.21≤partial η 2≤0.65, all P≤0.001). The ranges of the angles in medial-lateral axis of the IMUs showed strong correlation with the changes in abdominal and thoracic circumferences (ρ1=0.928, ρ2=0.807, P≤0.001 in both cases). A greater range of abdominal angles was found during AB compared to the other patterns. The best of the models achieved an F1 score of 0.970 (sensitivity: 0.983, specificity: 0.980) in validation. Conclusions:AB generates the greatest abdominal movement. Combining IMUs and machine learning can provide real-time quantification of chest movement and accurate detection of AB during breathing training.

Objective:To assess the application effects of an energy-restricted diet combined with high-protein, high-dietary-fiber meal replacement powder and probiotics in overweight/obese adults.Methods:It was a randomized controlled trial. A consecutive sample of 150 overweight/obese adults who underwent physical examinations at the Health Care Center of the First Hospital of Hebei Medical University between November 2021 and March 2022. The participants were randomly assigned into the combined group, the high-protein group, and the common group (50 participants per group) using a random number table method. All three groups of subjects received weight loss health education, energy-restricted diet, and interventions with meal replacement powder and probiotics (or probiotic placebo). The combined group was given high-protein and high-dietary fiber meal replacement powder and probiotics. The high-protein group was given high-protein meal replacement powder and probiotic placebo. The common group was given ordinary meal replacement powder and probiotic placebo. The meal replacement powder was packaged in 35 g per bag, with main components of varying amounts of protein, fat, carbohydrates, vitamins, and trace elements. Both the probiotic powder and the probiotic placebo came in 2 g sachets. The primary components of probiotic powder were various Bifidobacterium, Lactobacillus and excipients, while the main component of probiotic placebo was excipients. The meal replacement powder and the probiotic powder or probiotic placebo were taken twice a day for a total of 12 weeks, one sachet of each time, followed by a 4-week follow-up. The body weight, body mass index, body fat mass, abdominal circumference and hip circumference were measured before the trial (week 0) and at the end of weeks 2, 4, 8, 12, and 16. The change rates of each indicator were calculated. Biochemical indicators, trace elements, and 25-hydroxyvitamin D levels were measured at the end of week 0, 4, 8, and 12. A product evaluation questionnaire was conducted at the end of week 12. A total of 19 cases dropped out due to various reasons. Finally, 46 cases in the combined group, 42 cases in the high-protein group, and 43 cases in the common group were included in the analysis. Paired-samples t test, Kruskal-Wallis H test, one-way analysis of variance, and Mann-Whitney U test were used to compare the differences in weight-loss and maintenance effects, safety and patient acceptance among the three intervention groups, and to analyze the application effect of the energy-restricted diet combined with high-protein and high-dietary fiber meal replacement powder plus probiotics in overweight/obese adults. Results:Among the 131 overweight/obese adults included in the analysis, there were 57 males and 74 females, with a mean age of (37.30±8.33) years. By the end of the week 12, the body mass index [26.87(25.77, 30.38) vs 29.61(27.96, 33.09) kg/m2; 27.10(24.70, 31.37) vs 29.40(27.20, 34.17) kg/m2; 27.98(26.43, 30.12) vs 29.88(28.22, 31.93) kg/m2] and body fat masses [22.15(17.70, 30.15) vs 30.75(25.63, 35.40) kg; 23.35(19.12, 28.70) vs 29.45(26.20, 37.05) kg; 26.80(24.10, 31.60) vs 30.00(26.00, 34.70) kg] in the combined group, the high-protein group and the common group were all lower than those at baseline (week 0) (all P<0.05). At the end of the week 12, the change rates of body fat mass and body mass index in the combined group were both higher than those in the high-protein group and the common group [(25.98%±9.58%) vs (23.88%±11.15%) and (9.35%±11.00%), 9.29%(7.23%, 11.58%) vs 7.96% (5.51%, 10.92%) and 5.77% (2.68%, 10.03%)] (all P<0.05). At the end of the week 12, the body fat mass in the combined group and the high-protein group were both lower than that in the common group [22.15(17.70, 30.15), 23.35(19.12, 28.70) vs 26.80(24.10, 31.60) kg] (both P<0.05). At the end of the week 12, the decreased values of uric acid and high-sensitivity C-reactive protein in the combined group were both higher than those in the high-protein group and the common group [17.15(13.02, 23.45) vs 1.50(0.22, 28.60) and 4.20(0.15, 19.95) μmol/L, 0.43(0.24, 0.60) vs 0.21(0.06, 0.43) and 0.28(-0.04, 0.88) mg/L](both P<0.05). No serious adverse events were observed during the intervention period and at the end of the intervention. In the product evaluation questionnaire, the combined group scored higher than the high-protein group and the common group on items such as usage frequency, taste, satiety, willingness to continue use, willingness to recommend to others, and willingness to purchase [4(3, 4) vs 3(3, 4) and 3(2, 4) points, 4(3, 4) vs 3(3, 4) and 3(2, 4) points, 4(3, 4) vs 3(3, 4) and 3(3, 3) points, 4(3, 4) vs 3(3, 4) and 3(3, 4) points, 4(3, 4) vs 3(3, 4) and 3(3, 3) points, 3(3, 4) vs 3(3, 4) and 3(2, 3) points] (all P<0.05). Conclusion:An energy-restricted diet combined with high-protein, high-dietary-fiber meal replacement powder and probiotics demonstrates superior weight-loss and weight-maintenance effects in overweight/obese adults, with high safety and great user acceptability.

Radiochemotherapy-induced oral mucositis (OM) is a common oral complication in patients with tumors following head and neck radiotherapy or chemotherapy. Erosion and ulcers are the main features of OM that seriously affect the quality of life of patients and even the progress of tumor treatment. To date, differences in clinical prevention and treatment plans for OM have been noted among doctors of various specialties, which has increased the uncertainty of treatment effects. On the basis of current research evidence, this expert consensus outlines risk factors, clinical manifestations, clinical grading, ancillary examinations, diagnostic basis, prevention and treatment strategies and efficacy indicators for OM. In addition to strategies such as basic oral care, anti-inflammatory and analgesic agents, anti-infective agents, pro-healing agents, and photobiotherapy recommended in previous guidelines, we also emphasize the role of traditional Chinese medicine in OM prevention and treatment. This expert consensus aims to provide references and guidance for dental physicians and oncologists in formulating strategies for OM prevention, diagnosis, and treatment, standardizing clinical practice, reducing OM occurrence, promoting healing, and improving the quality of life of patients.
Humans ; Chemoradiotherapy/adverse effects* ; Consensus ; Risk Factors ; Stomatitis/etiology*

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.

BACKGROUND: Disease-modifying therapies have been approved for the treatment of relapsing multiple sclerosis (RMS). The present study aims to examine the safety of teriflunomide in Chinese patients with RMS. METHODS: This non-randomized, multi-center, 24-week, prospective study enrolled RMS patients with variant (c.421C>A) or wild type ABCG2 who received once-daily oral teriflunomide 14 mg. The primary endpoint was the relationship between ABCG2 polymorphisms and teriflunomide exposure over 24 weeks. Safety was assessed over the 24-week treatment with teriflunomide. RESULTS: Eighty-two patients were assigned to variant ( n  = 42) and wild type groups ( n  = 40), respectively. Geometric mean and geometric standard deviation (SD) of pre-dose concentration (variant, 54.9 [38.0] μg/mL; wild type, 49.1 [32.0] μg/mL) and area under plasma concentration-time curve over a dosing interval (AUC tau ) (variant, 1731.3 [769.0] μg∙h/mL; wild type, 1564.5 [1053.0] μg∙h/mL) values at steady state were approximately similar between the two groups. Safety profile was similar and well tolerated across variant and wild type groups in terms of rates of treatment emergent adverse events (TEAE), treatment-related TEAE, grade ≥3 TEAE, and serious adverse events (AEs). No new specific safety concerns or deaths were reported in the study. CONCLUSION: ABCG2 polymorphisms did not affect the steady-state exposure of teriflunomide, suggesting a similar efficacy and safety profile between variant and wild type RMS patients. REGISTRATION NCT04410965, https://clinicaltrials.gov .
Humans ; Crotonates/adverse effects* ; Toluidines/adverse effects* ; Nitriles ; Hydroxybutyrates ; Female ; Male ; Adult ; ATP Binding Cassette Transporter, Subfamily G, Member 2/genetics* ; Middle Aged ; Multiple Sclerosis, Relapsing-Remitting/genetics* ; Prospective Studies ; Young Adult ; Neoplasm Proteins/genetics* ; East Asian People