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.

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.

Objective:To investigate the clinical effect of probiotic-assisted minocycline in the treatment of elderly patients with chronic periodontitis(CP).Methods:95 elderly patients with CP in our hospital from January 2021 to March 2023 were selected and divided into observation group(49 cases)and control group(46 cases)by random number table method.Both groups received oral health education,the control group received standard periodontal treatment,the observation group was given probiotic lozenges,and both groups were treated for 4 weeks.The therapeutic effect and occurrence of adverse reactions in the 2 groups were analyzed,and the periodontal index[loss of attachment(AL),probing depth(PD),bleeding index(BI)],oral flora(concentrations of Lac-tobacillus salivary and porphyromonas gingivalis)and oral health status[oral health assessment tool(OH AT)score]before and after treatment were compared.Results:No cases were excluded in 2 groups.After treatment,the total effective rate of observation group was 95.92％higher than that of control group 80.43％(P＜0.05).After treatment,the values of AL,PD and BI in the 2 groups were lower than before treatment(P＜0.05),and the values of AL,PD and BI in the observation group were lower than those in the control group(P＜0.05).After treatment,the concentration of porphyromonas gingivalis in saliva in 2 groups was lower than that be-fore treatment,and the concentration of lactobacillus in saliva was higher than that before treatment(P＜0.05).The concentration of porphyromonas gingivalis in saliva in observation group was lower than that in control group,and the concentration of lactobacillus in saliva was higher than that in control group(P＜0.05).After treatment,the OH AT score of the 2 groups was lower than that before treatment(P＜0.05),and the OHAT score of the observation group was lower than that of the control group(P＜0.05).There was no significant difference in the incidence of adverse reactions between the two groups(P＞0.05).Conclusion:Probiotics assisted mino-cycline in the treatment of elderly patients with CP can reduce periodontal index,regulate oral flora and improve oral health without increasing adverse reactions.

Objective To construct a decision aid scheme for surgical methods of thyroid cancer patients and explore its application effect.Methods A retrospective analysis was conducted on 692 patients with thyroid cancer who were treated in the department of breast and thyroid surgery in the First Affiliated Hospital of Army Medical University from January 1 to December 31,2022,patients who underwent surgery from January 1 to June 30,2022 were selected as the control group(n=346),while patients who underwent surgery from July 1 to December 31,2022 were selected as the observation group(n=346).Patients in the control group chose surgical methods after conventional education,while patients in the observation group chose the surgical methods through the decision aid scheme.The decision conflict and decision-making at admission and 1 day before surgery of patients in the two groups were assessed.The psychological state at admission,1 day before surgery,and 1 month after surgery of patients in the two groups were evaluated.The decision satisfaction of patients in the two groups were assessed 1,3,and 6 months after surgery.Results There was no significant difference in the decision conflict score,decision making score or anxiety and depression scores at admission of patients between the two groups(P>0.05).One day before surgery,the decision conflict score of patients in the observation group was significantly lower than that in the control group(t=21.099,P<0.001),and the decision-making score was significantly higher than that in the control group(t=8.806,P<0.001).The anxiety and depression scores of patients in the two groups decreased over time,among which the anxiety and depression scores 1 day before surgery and 1 month after surgery of patients in the observation group were lower than those in the control group(P<0.05).The decision satisfaction scores 1,3 and 6 months after surgery of patients in the observation group were higher than those in the control group(P<0.05).Conclusion The implementation of decision aid scheme can effectively reduce decision conflicts of patients with thyroid cancer regarding the choice of surgical methods,relieve negative emotions,and enhance decision satisfaction.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

Objective:To investigate the effect of changes in metabolic syndrome status and persistence on carotid plaque risk.Methods:This retrospective cohort study analyzed individuals who underwent routine health check-ups at the health management center of the Second Affiliated Hospital of Dalian Medical University from 2014 to 2023. Participants with at least three carotid ultrasound records meeting the inclusion criteria were classified into 4 groups based on changes in metabolic status: persistently metabolic health, transitioning from metabolic health to unhealth, transitioning from metabolic unhealth to health, and persistently metabolic unhealth. The cumulative incidence of carotid plaque in these groups was compared. A Cox proportional risk model was used to evaluate the relationship between changes in metabolic syndrome status, the number of metabolic syndrome components, and the risk of carotid plaque development. Restricted cubic spline analysis was applied to explore the association between changes in individual metabolic syndrome components and carotid plaque risk.Results:Compared to the persistently metabolic health group, the persistent unhealth group had the highest risk of developing carotid plaque( HR=1.35, 95% CI 1.05-1.74, P=0.021), followed by those who transitioned from metabolic health to unhealth and those who improved from metabolic unhealth to health. Furthermore, the risk of carotid plaque increased progressively with the number of metabolic syndrome components. Restricted cubic spline analysis revealed a nonlinear relationship between fasting blood glucose change and carotid plaque risk, while systolic blood pressure, diastolic blood pressure, waist circumference, triglycerides, and high-density lipoprotein-cholesterol showed a linear dose-response relationship with carotid plaque. Conclusions:The change of metabolic syndrome is associated with the risk of developing carotid plaque, and maintaining metabolic health, recovering from metabolic syndrome, or minimizing the number of metabolic syndrome components may be effective strategies to prevent carotid plaque formation.

Objective:To explore the relationship between thyroid hormone sensitivity and metabolic dysfunction-associated steatotic liver disease(MASLD) in a population with normal thyroid function, with a particular focus on sex-specific differences.Methods:This retrospective study included 41 355 euthyroid cases who underwent routine health examinations at the Health Management Centre of the Second Affiliated Hospital of Dalian Medical University from January 2014 to December 2023 were included. The free triiodothyronine(FT 3) to free thyroxine(FT 4) ratio(FT 3/FT 4) was calculated in order to reflect the peripheral sensitivity of the thyroid gland. Similarly, thyroid feedback quantile-based index(TFQI), thyrotrophic thyroxine resistance index(TT 4RI), and the FT 3-based TFQI-derived index(TFQI-FT 3) were calculated in order to reflect the central sensitivity of the thyroid gland. A Logistic regression was employed to analyse the effect of sex-specific thyroid hormone sensitivity indices on the prevalence of MASLD. The restricted cubic spline was used to analyse the non-linear relationship between the thyroid sensitivity hormone indices and MASLD. Furthermore, the correlation between the thyroid hormone sensitivity indices and MASLD in different subgroups was also analysed. Results:The prevalence of MASLD in the study population was 28.8%. After adjusting the model for confounders, the risk of MASLD increased by 7%, 3%, 10%, and 5% for each standard deviation increase in FT 3/FT 4, TFQI, TFQI-FT 3, and TT 4RI in the total population, respectively. The risk of MASLD increased by 6% and 5% for each standard deviation increase in FT 3/FT 4 and TFQI-FT 3 in men, respectively. For each standard deviation increase in FT 3/FT 4, TFQI, TFQI-FT 3, and TT 4RI in women, the risk of MASLD increased by 6%, 5%, 11%, and 5%, respectively. Higher FT 3/FT 4 and TFQI-FT 3 were positively associated with the risk of developing MASLD in men, and higher FT 3/FT 4, TFQI, TFQI-FT 3, and TT 4RI were positively associated with the risk of developing MASLD in women. There was a non-linear, inverted U-shaped relationship between TFQI and risk of MASLD in women. Subgroup analyses showed positive associations between FT 3/FT 4, TFQI, TFQI-FT 3, and MASLD. Conclusions:The thyroid hormone sensitivity indices may provide a basis for clinical prevention and management of MASLD in individuals with normal thyroid function. Additionally, FT 3/FT 4 and TFQI-FT 3 may indicate the risk of MASLD in the general population, while TFQI and TT 4RI are more suitable for assessing the risk of MASLD in women.