Background Gestational weight gain is closely related to maternal and infant health outcomes. Pregnant women are simultaneously exposed to four metals—lithium (Li), vanadium (V), uranium (U), and bismuth (Bi)—through inhalation of fine particulate matter and consumption of contaminated food and water. Existing studies suggest that exposure to these metals may be associated with gestational weight gain. However, no study has yet explored the complex relationships between exposure to mixtures of these four metals and weight gain at different stages of pregnancy. Objective To investigate the associations between mixed exposure to Li, V, U, and Bi in early pregnancy and the average weekly gestational weight gain during both early pregnancy and mid-to-late pregnancy. Methods This prospective study recruited eligible women in early pregnancy from an obstetrics clinic of a tertiary hospital in Jinan, China, between September 2021 and July 2023. Pre-pregnancy weight, current weight (at 11⁺⁰ to 13⁺⁶ weeks of gestation), and spot urine samples (≥5.0 mL) were collected at enrollment. Urinary concentrations of Li, V, Bi, and U were determined using inductively coupled plasma mass spectrometry. Participants were followed up in late pregnancy (≥28 weeks of gestation) to collect information on physical activity via questionnaire; weight measurements at the last antenatal visit (35⁺⁰ to 37⁺⁶ weeks of gestation) were obtained from the hospital information system. After adjusting for covariates, multiple linear regression and generalized additive models were used to assess the associations of individual metals with weekly weight gain in early pregnancy and in mid-to-late pregnancy. Bayesian kernel machine regression (BKMR) and quantile-based g-computation (Qgcomp) were applied to evaluate the joint effects of the metal mixture exposure on weekly weight gain at the two gestational stages. Results A total of 313 pregnant women were included. The geometric means of urinary Li, V, U, and Bi concentrations were 37.07, 0.20, 0.06, and 0.04 μg·L⁻¹, respectively; after creatinine adjustment, the corresponding values were 46.82, 0.25, 0.07, and 0.05 μg·g⁻¹ (Cr). The mean weekly gestational weight gain was (0.19±0.25) kg in early pregnancy and (0.53 ± 0.18) kg in mid-to-late pregnancy. Both multiple linear regression and generalized additive models showed that urinary V concentration was positively associated with average weekly gestational weight gain in early pregnancy, while no significant associations were found for other metals or for gestational weight gain in mid-to-late pregnancy. In the BKMR model with early-pregnancy weight gain as the outcome, V had the strongest association [posterior inclusion probability (PIP)=0.773]. When other metals were fixed at their medians, V showed a positive non-linear association with the outcome. A significant single-metal effect of V and its interaction with Li were observed. Compared with the 50th percentile of the metal mixture, the average weekly weight gain in early pregnancy increased by 0.016 (95%CI: 0.003, 0.029) and 0.018 (95%CI: 0.001, 0.036) at the 60th and 65th percentiles, respectively; conversely, at the 25th percentile, it decreased by 0.026 (95%CI: 0.002, 0.050). Overall, the joint effect of the metal mixture on early- pregnancy weight gain showed an upward trend. In the BKMR model for mid-to-late pregnancy gestational weight gain, all PIPs were<0.5, and no significant single-metal effects, interactions, or joint effects were identified. Qgcomp results confirmed a positive association between the metal mixture and early-pregnancy weight gain (b=0.031, 95%CI: 0.010, 0.051; P<0.01), with V contributing the highest positive weight (0.71). No significant association was found for weight gain in mid-to-late pregnancy (b=0.007, P=0.339). Conclusion Higher levels of co-exposure to the Li, V, Bi, and U metal mixture during early pregnancy may be associated with increased average weekly weight gain in early pregnancy. Among these metals, V exhibits a predominant role and appears to interact with Li. No association is observed between early-pregnancy metal mixture exposure and average weekly gestational weight gain in mid-to-late pregnancy. These findings suggest that monitoring and managing metal exposure during early pregnancy may be crucial for the rational regulation of gestational weight gain.

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.

Objective: To evaluate the effectiveness of a family-school collaborative intervention on sleep health among fifth-grade primary school students, so as to provide scientific evidence for promoting sleep health among primary school students. Methods: From April to June 2024, a baseline survey was conducted on 342 fifth-grade students and their parents from one primary school in a district of Beijing, using the convenient sampling method. A semester of sleep health education was implemented from September to December 2024, covering cognitive, behavioral and environmental factors that affected timely bedtime; and the program took various forms, including student courses, popular-science articles for parents, and personalized feedback reports. Another primary school in the same area was conveniently selected as the control group, where 313 fifth-grade students and their parents received routine health education. The subjects were assessed for their baseline and end-point (March to June 2025) sleep conditions using sleep diaries (222 participants in the intervention group and 225 in the control group) and accelerometers (120 participants in the intervention group and 88 in the control group), respectively. The independent samples t-test and the Wilcoxon rank-sum test were used to compare characteristics between groups，and linear mixed models were employed to evaluate the intervention effects. Results: In the sleep diary evaluation, the intervention group had an earlier weekend wake up time and a smaller social jetlag than the control group at baseline ( t/Z =3.42, 7.21); in the accelerometer evaluation, the intervention group exhibited a smaller social jetlag than the control group at baseline ( Z =3.90) (all P <0.05). In the sleep diary evaluation, compared with the control group, the intervention group showed a reduced pre-to-post change in school-day bedtime ( β = -6.70), whereas the pre-to-post changes in school-day sleep duration, weekend wake-up time, weekend sleep duration, and average sleep duration were all increased at baseline and at the end ( β =8.41, 22.58, 27.03,13.74) (all P <0.05). In the accelerometer evaluation, compared with the control group, the intervention group showed reduced pre-to-post changes in school-day sleep onset time and school-day wake time ( β =-14.75, -6.24), whereas the pre-to-post change in school-day sleep duration increased at baseline and at the end ( β =8.01) (all P <0.05). Conclusion A family-school collaborative sleep health intervention may be an effective strategy for improving sleep among fifth-grade primary school students.

OBJECTIVE To investigate the effects of Tripterygium wilfordii multiglycoside （TWM） on renal injury in diabetic nephropathy （DN） rats through tumor protein p53/microRNA-214 （miR-214）/UNC-51-like kinase 1 （ULK1） axis. METHODS Male SD rats were randomly divided into normal group （n＝6） and modeling group （n＝28）； the modeling group was fed with high fat and high glucose plus intraperitoneal injection of streptozotocin to establish DN model. The modeled rats were randomly divided into model group， valsartan group [8.33 mg/（kg·d）] and TWM group[6.25 mg/（kg·d）]， with 8 rats in each group. Rats in each group were gavaged with the corresponding medication or normal saline， once a day， for 6 consecutive weeks. After the last medication， liver and renal function indexes [24 h urinary total protein （24 h-UTP）， blood urea nitrogen （BUN）， serum creatinine （SCr）， albumin （ALB）， alanine transaminase （ALT）]， blood lipid indexes （triglycerides， total cholesterol） and blood glucose index （fasting blood glucose） in urine/blood sample of rats were detected in each group. Renal pathologic change was observed， protein and mRNA expressions of p53， ULK1， Beclin-1 and microtubule-associated protein 1 light chain 3 （LC3）， and expression of miR-214 in renal tissue were also determined. RESULTS Compared with the normal group， the renal tubular epithelium of rats in the model group showed obvious edema， cell swelling， accompanied by lymphocyte infiltration； the levels of 24h-UTP， BUN， SCr， ALT and glycolipid indexes， the expressions of p53 protein and mRNA， as well as the expression of miR-214 in rats in the model group and administration groups were significantly increased or up-regulated， while ALB level， LC3-Ⅱ/LC3-Ⅰ， the expressions of LC3 mRNA， the expressions of ULK1， Beclin-1 protein and mRNA were significantly decreased or down-regulated （P＜0.01）. Compared with the model group， the histopathological damage of the kidney in rats was improved in administration groups； the levels of 24 h-UTP， BUN， SCr， ALT and glycolipid indexes， the expressions of p53 protein and mRNA， as well as the expression of miR-214 were all significantly decreased or down-regulated， while ALB level， LC3-Ⅱ/LC3-Ⅰ， the expressions of LC3 mRNA， the expressions of ULK1 and Beclin-1 protein and mRNA were significantly increased or up-regulated （P＜0.01）. CONCLUSIONS TG can alleviate renal damage in DN rats， and improve their liver and renal function， as well as glucose and lipid levels. These effects may be related to the regulation of the p53/miR-214/ULK1 axis and the restoration of cellular autophagy.

ObjectiveTo analyze the detected results of CD4⁺T lymphocytes and viral load in newly identified human immunodeficiency virus (HIV) infected patients in Huangpu District of Shanghai in 2023, to explore the correlation between them, so as to provide a scientific basis for the development of targeted prevention and control measures and antiviral treatment programs. MethodsThe data of CD4 cell count, viral load and demographic characteristics of the newly infected patients living with HIV in Huangpu District, Shanghai in 2023 were collected and analyzed by using descriptive epidemiological method. ResultsThe mean CD4 cell count of the 67 newly identified HIV infected patients in Huangpu District was (301.22±235.19) cells·µL^-1, with a mean viral load of (5.15±1.28) ×10⁵ copies·mL^-1.There were statistically significant differences in CD4 cell count and viral load among different age groups (P<0.05), but there were no statistically significant differences by gender and marital status (both P>0.05). The CD4 cell count and CD4/CD8 ratio both were negatively correlated with the lg value of viral load (r=-0.290, -0.378; P=0.027, 0.002). ConclusionThe CD4 cell counts of the newly identified HIV infected patients in Huangpu District in 2023 were generally low, the proportion of patients with high viral load was high, but the risk for elderly infected with HIV was high. The elderly have gradually become the key population for AIDS prevention and control in Huangpu District. It is recommended to expand HIV screening in the elderly to reduce the risk of HIV transmission and increase the rate of early detection and treatment.

OBJECTIVE To assess the scientific rigor， clarity and feasibility of the recommendations of the Guidelines for Evidence-based Use of Biological Agents for the Clinical Treatment of Osteoporosis （hereinafter referred to as the Guideline） through external review， in order to further revise and improve the Guideline recommendations. METHODS This study employed a cross-sectional survey research design， a convenience sampling method was adopted to select frontline medical workers in the field of osteoporosis （including clinical doctors， clinical pharmacists， and nurses） as well as patients or their family members. External review was conducted through a combination of closed-ended and open-ended electronic questionnaires to get feedback from them on the appreciation，clarity and feasibility of the 32 preliminary recommendations in the Guideline. RESULTS A total of 90 external review subjects from 15 hospitals were collected， including 45 clinical doctors， 15 clinical pharmacists， 15 nurses and 15 patients or their family members. The overall appreciation degree of recommendations was 99.38%， the overall clarity degree of recommendations was 98.92%， and the overall feasibility degree of recommendations was 99.65%. At the same time， 111 subjective suggestions were collected， which provided an important reference for the further improvement of the Guideline recommendations. Based on the above feedback， the Guideline steering committee and core expert group revised the wording of 12 draft recommendations without deletion， and finally determined 32 recommendations. CONCLUSIONS The external review provides an important basis for the final formation of the Guideline， further improves the scientific rigor， clarity and feasibility of the recommendations， and ensures the standardization， practicality and implementability of the Guideline.

Progressive pulmonary fibrosis (PPF) is a progressive and lethal condition with few effective treatment options. Improvements in quality of life for patients with PPF remain limited even while receiving treatment with approved antifibrotic drugs. Traditional Chinese medicine (TCM) has the potential to improve cough, dyspnea and fatigue symptoms of patients with PPF. TCM treatments are typically diverse and individualized, requiring urgent development of efficient and precise design strategies to identify effective treatment options. We designed an innovative Bayesian adaptive two-stage trial, hoping to provide new ideas for the rapid evaluation of the effectiveness of TCM in PPF. An open-label, two-stage, adaptive Bayesian randomized controlled trial will be conducted in China. Based on Bayesian methods, the trial will employ response-adaptive randomization to allocate patients to study groups based on data collected over the course of the trial. The adaptive Bayesian trial design will employ a Bayesian hierarchical model with "stopping" and "continuation" criteria once a predetermined posterior probability of superiority or futility and a decision threshold are reached. The trial can be implemented more efficiently by sharing the master protocol and organizational management mechanisms of the sub-trial we have implemented. The primary patient-reported outcome is a change in the Leicester Cough Questionnaire score, reflecting an improvement in cough-specific quality of life. The adaptive Bayesian trial design may be a promising method to facilitate the rapid clinical evaluation of TCM effectiveness for PPF, and will provide an example for how to evaluate TCM effectiveness in rare and refractory diseases. However, due to the complexity of the trial implementation, sufficient simulation analysis by professional statistical analysts is required to construct a Bayesian response-adaptive randomization procedure for timely response. Moreover, detailed standard operating procedures need to be developed to ensure the feasibility of the trial implementation. Please cite this article as: Zhang C, Nie YS, Zhang CT, Yang HJ, Zhang HR, Xiao W, Cui GF, Li J, Li SJ, Huang QS, Yan SY. An adaptive Bayesian randomized controlled trial of traditional Chinese medicine in progressive pulmonary fibrosis: Rationale and study design. J Integr Med. 2025; 23(2): 138-145.
Female ; Humans ; Male ; Bayes Theorem ; Disease Progression ; Drugs, Chinese Herbal/therapeutic use* ; Medicine, Chinese Traditional/methods* ; Pulmonary Fibrosis/therapy* ; Quality of Life ; Randomized Controlled Trials as Topic ; Research Design ; Adaptive Clinical Trials as Topic

Objective: To identify core acupoint patterns and elucidate the molecular mechanisms of acupuncture for primary depressive disorder (PDD) through data mining and network analysis. Methods: A comprehensive literature search was conducted across PubMed, Embase, Ovid Technologies (OVID), Web of Science, Cochrane Library, China National Knowledge Infrastructure (CNKI), China National Knowledge Infrastructure Database (VIP), Wanfang Data, and SinoMed Database from database foundation to January 31, 2025, for clinical studies on acupuncture treatment of PDD. Descriptive statistics, high-frequency acupoint analysis, degree and betweenness centrality evaluation, and core acupoint prescription mining identified predominant therapeutic combinations for PDD. Network acupuncture was used to predict therapeutic target for the core acupoint prescription. Subsequent protein-protein interaction (PPI) network and molecular complex detection (MCODE) analyses were conducted to identify the key targets and functional modules. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses explored the underlying biological mechanisms of the core acupoint prescription in treating PDD. Results: A total of 57 acupoint prescriptions underwent systematic analysis. The core therapeutic combinations comprised Baihui (GV20), Yintang (GV29), Neiguan (PC6), Hegu (LI4), and Shenmen (HT7). Network acupuncture analysis identified 88 potential therapeutic targets (79 overlapping with PDD), while PPI network analysis revealed central regulatory nodes, including interleukin (IL)-6, IL-1β, tumor necrosis factor (TNF)-α, toll-like receptor 4 (TLR4), IL-10, brain-derived neurotrophic factor (BDNF), transforming growth factor (TGF)-β1, C-X-C motif chemokine ligand 10 (CXCL10), mitogen-activated protein kinase 3 (MAPK3), and nitric oxide synthase 1 (NOS1). MCODE-based modular analysis further elucidated three functionally coherent clusters: inflammation-homeostasis (score = 6.571), plasticity-neurotransmission (score = 3.143), and oxidative stress (score = 3.000). GO and KEGG analyses demonstrated significant enrichment of the MAPK, phosphoinositide 3-kinase/protein kinase B (PI3K/Akt), and hypoxia-inducible factor (HIF)-1 signaling pathways. These mechanistic insights suggested that the antidepressant effects mediated through mechanisms of neuroinflammatory regulation, neuroplasticity restoration, and immune-oxidative stress homeostasis. Conclusion This study reveals that acupuncture alleviates depression through a multi-level mechanism, primarily involving the neuroinflammation suppression, neuroplasticity enhancement, and oxidative stress regulation. These findings systematically clarify the underlying mechanisms of acupuncture’s antidepressant effects and identify novel therapeutic targets for further mechanistic research.

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.

Liquiritigenin(LG)is a flavone of pharmacological importance,however,its application potential is severely limited due to its poor water solubility.LG could be disassociated slightly in water to form phenolate anion,therefore,better solubilization effect is expected by inclusion with cationic cyclodextrins(CCDs).In this work,four kinds of CCDs modified with amino groups at the primary face were synthesized,and their solid inclusion complexes with LG were successfully prepared by preparing their saturated solutions.The formation of the solid inclusion complexes was confirmed by scanning electron microscopy(SEM)and powder X-ray diffraction(PXRD),and their supramolecular binding behavior in solution was studied using multiple techniques.A 1∶1 inclusion stoichiometry of inclusion complexation was defined using Job plot by ultraviolet-visible(UV-vis)spectroscopy,and their binding stability constants(Ks)were determined as 2862.77,3494.70,6521.85 and 9599.48 L/mol using UV-vis spectroscopic titration,far more superior to that of nativeβ-CD(Ks=236.79 L/mol).This indicated that the amino side chains on CCDs could actively participate in the inclusion complexation through anion-cation interactions,significantly strengthening the host-guest binding between CCDs and LG.The inclusion modes were further elucidated based on proton and two-dimensional rotating-frame overhauser enhancement spectroscopy(2D-ROESY)nuclear magnetic resonance(NMR)experiments and molecular docking.Water solubility of LG was dramatically promoted up to 4.9 mg/mL,which was 70-fold higher than that of native LG.This study could draw inspiration for the binding and solubilization of phenols such as flavones by design of cationic macrocyclic molecules.