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 observe the value of CT angiography(CTA)radiomics for evaluating the risk of basilar tip aneurysm(BTA)rupture.Methods Totally 133 BTA patients were retrospectively enrolled and divided into ruptured group(n=39)and unruptured group(n=94)based on BT A ruptured or not,also divided into training set(n=93)and test set(n=40)at the ratio of 7∶3.CTA radiomics features of BTA were extracted,the best radiomics features were screened,and the radiomics score(Radscore)was calculated.Then machine learning(ML)models were established with logistic regression(LR),random forest(RF),decision tree(DT)and K-nearest neighbor(KNN)algorithms,respectively.Radscore model was also established,and finally a combined model was constructed based on clinical data,routine imaging findings and Radscore.The efficacy of the above models for evaluating the risk of BTA rupture were comparatively analyzed.Results Finally 4 radiomics features of BTA were obtained.The area under the curve(AUC)of LR,RF,DT and KNN radiomics models for differentiating ruptured and unruptured BTA in training set was 0.770,0.816,0.817 and 0.795,respectively,while that in test set was 0.795,0.793,0.786 and 0.824,respectively,both being not significant different(both P＞0.05).Patient's gender,alcohol consumption history,BTA morphology and Radscore were all independent impact factors of BT A rupture(all P＜0.05),which were used to establish a clinical-routine imaging model.For all 133 cases,AUC of the combination model for differentiating ruptured and unruptured BTA was 0.877,of Radscore model was 0.775,while that of clinical-routine imaging model was 0.677,of the former was significantly higher than of the last two(both P＜0.05).Conclusion CTA radiomics was helpful for evaluating the risk of BTA rupture.Combining with clinical data and routine imaging findings could further improve the value of CTA radiomics.

Objective:To summarize and analyze the clinical characteristics and treatment outcomes of patients with recurrent pericarditis.Methods:This observational study consecutively recruited patients with recurrent pericarditis who were hospitalized at Peking University People′s Hospital between January 2017 and February 2024. Clinical characteristics and treatment outcomes were collected and summarized during follow-up.Results:A total of 8 recurrent pericarditis patients including 3 males were included, with an age of 34.0 (22.0, 39.5) years. In terms of clinical features, all patients presented with acute-onset severe chest pain, accompanied by fever in 7 and an audible pericardial friction rub in 2 patients. Electrocardiogram showed no diffuse ST-segment elevation or PR-segment depression in any patient. Echocardiography revealed pericardial effusion in all cases, with extensive fibrinous exudate and transient pericardial thickening observed in 6 patients. CT identified concurrent pleural and/or peritoneal effusions in 6 patients. All patients exhibited marked elevations in C-reactive protein, erythrocyte sedimentation rate and D-dimer levels. Whole-exome sequencing identified MEFV gene mutations associated with familial Mediterranean fever in 3 cases. Two patients developed cardiac tamponade requiring pericardiocentesis, which revealed hemorrhagic effusion. In the aspect of treatment outcomes, the time from recurrence to first confirmed diagnosis of recurrent pericarditis of this cohort was 14.5 (13.3, 19.5) d. Upon diagnosis, all patients promptly received standard anti-inflammatory therapy with ibuprofen and colchicine, achieving rapid relief. However, during a follow-up of 12.0 (6.0, 25.3) months, 3 patients experienced recurrence, and 2 developed transient constrictive pericarditis.Conclusion:Patients with recurrent pericarditis typically exhibit characteristic clinical presentations, laboratory abnormalities, imaging findings and potential genetic associations. Although standard anti-inflammatory therapy demonstrates favorable short-term efficacy, long-term management remains challenging due to the risks of recurrence and progression to constrictive pericarditis.

Objective:To explore the correlation between serum ferritin (SF) and risk of lean non-alcoholic fatty liver disease (NAFLD), so as to provide the basis for the prevention and treatment of lean NAFLD.Methods:A total of 7 187 people without NAFLD at baseline who took at least 2 physical examinations in the Health Management Center of the Second Hospital of Dalian Medical University from January 2014 to December 2023 and met the selection criteria were selected as the research subjects, and all the subjects had no NAFLD at baseline. Subjects were divided into four groups according to baseline SF quartiles: 1 797 cases in the first quartile ( Q1) group, 1 797 cases in the second quartile ( Q2) group, 1 797 cases in the third quartile ( Q3) group, and 1 796 cases in the fourth quartile ( Q4) group. The incidence of lean NAFLD in each group were observed. Kaplan-Meier curve was plotted to calculate the cumulative incidence of lean NAFLD which compared by log-rank test. Cox proportional hazard regression model was used to analyze the correlation between SF and new-onset lean NAFLD, Q1, Q2, Q3 and Q4 of SF were taken as continuous variables into the model for trend test.The stability of the results was verified by two item sensitivity analyses. Time-dependent receiver operating characteristic curve (ROC) was plotted to evaluate the predictive value of SF for the onset of lean NAFLD. Results:The cumulative follow-up were 25 076 person-years. There were 230 new cases of lean NAFLD, and the incidence density was 9.172/1 000 person-years. The incidence densities of lean NAFLD in Q1, Q2, Q3 and Q4 groups were 6.915/1 000 person-years, 8.552/1 000 person-years, 9.641/1 000 person-years, 12.003/1 000 person-years, respectively. Kaplan-Meier curve indicated that the incidence of lean NAFLD was increased with the increment of SF, and the difference was statistically significant (log-rank test, χ2=9.92, P=0.019). Cox proportional hazard regression model results showed that the risk of developing lean NAFLD in Q4 group increased by 72.8% ( HR=1.728, 95% confidence interval (95% CI): 1.059 to 2.820) compared with Q1 group. Trend analysis revealed that the risk of lean NAFLD increased by 18.9% for each one-quartile increase of SF( HR=1.189, 95% CI: 1.012 to 1.396). Two sensitivity analyses indicated that the risk of NAFLD in Q4 group was 1.795 times ( HR=1.795, 95% CI: 1.083 to 2.975) or 1.654 times ( HR=1.654, 95% CI: 1.022 to 2.678) higher than that in Q1 group. The area under the curve (95% CI) of SF for predicting the incidence of lean NAFLD at 2-, 3-, 7- and 8-year follow-up based on time-dependent ROC were 0.645 (0.593 to 0.698), 0.652 (0.603 to 0.700), 0.605 (0.539 to 0.672) and 0.716 (0.597 to 0.836), respectively. Conclusion:SF is an independent risk factor for lean NAFLD and has predictive value for the new-onset of lean NAFLD.

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.

Follicular Helper T Cells(TFH)are a specialized subset of CD4+T cells that predominantly lo-calize within lymphoid follicles.These cells play a crucial role in facilitating B cell proliferation,differentia-tion,and antibody production,thereby thereby serving as a pivotal component of the adaptive immune re-sponse against infections.Given the significant function of TFH cells in regulating humoral immunity,they have become a focal point in the research of infectious diseases and related vaccine development in recent years.This review summarizes the surface markers of T follicular helper(Tfh)cells,their differentiation reg-ulatory mechanisms,and the latest research progress of Tfh cells in SARS-CoV-2 infection and vaccine stud-ies.It aims to provide new theoretical foundations and research insights for optimizing the design of SARS-CoV-2 vaccines and enhancing the cross-protection ability against SARS-CoV-2 variants.

Objective:To investigate intraoperative pathological findings and the interventional effects of "deep cervical lymphaticovenous anastomosis+" (dcLVA+) on deep cervical lymphatic drainage as well as the blood flow of carotid artery and jugular vein in elderly patients with cognitive impairment, and to put forward the thoughts based on the findings from the surgery.Methods:Between May 2024 and December 2024, retrospective analysis of Microsurgery Hospital, Fengcheng Hospital, Xi'an Medical College performed dcLVA+ between the deep cervical lymphatics or lymph nodes and jugular veins in 50 elderly patients with cognitive impairment (19 males and 31 females, aged 55-88 years with 69.94 years in average). Nine patients were found with Clinical Dementia Rating (CDR) score at 1 (mild), 7 with CDR score at 2 (moderate) and 34 with CDR score at 3 (severe). Intraoperative observations based on literature reviews had identified anatomical relationships between the lymphatic sacs containing cervical lymphatic chain and the carotid sheath. The lymph node count, size, distribution, thickness of fat tissue and conditions of lymphatic vessels were documented. Ultrasound was used to compare the blood flow of carotid artery and jugular vein as well as the cross-sectional areas at the planes of hyoid and cricoid cartilage before and after the closure of incisions under anaesthesia in 39 patients. Correlation analyses for Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCa), Activities of Daily Living (ADL) and Agitation behavior score before and 1 week after surgery were performed using Spearman's correlation and Wilcoxon paired tests. P<0.05 was considered statistically significant. Postoperative follow-ups were conducted via the visit of outpatient clinic and WeChat and telephone interviews. Results:In surgery, the cervical lymphatic chain was found running within an almost enclosed sac surrounding the carotid sheath. There were enlarged lymph nodes, increased fat deposition, lymphatic dilation or fibrosis in the sac. The preoperative blood flow in the carotid artery at the planes of hyoid and cricoid cartilage in the 39 patients was recorded at 150.52 ml/min±40.33 ml/min and 358.29 ml/min±129.30 ml/min, while that in the jugular vein was at 172.50 ml/min±63.94 ml/min and 317.00 ml/min±105.21 ml/min, respectively, both were far lower than the normal blood flow. There were statistically significant differences in the blood flow of carotid artery and jugular vein before and after surgery ( P<0.01). It was found that the preoperative blood flow of the internal carotid artery at the plane of hyoid was positively correlated with the preoperative scores of MMSE ( P<0.01), MoCa ( P<0.05) and ADL ( P<0.01). The blood flow of the common carotid artery at the plane of cricoid cartilage was found significantly and positively correlated with the preoperative scores of MMSE ( P<0.05). It was also noted that the blood flow of the internal carotid artery at the plane of hyoid was significantly and positively correlated with the postoperative ADL ( P<0.01) but negatively correlated with the Agitation behavior score ( P<0.05). The blood flow of the common carotid artery at the plane of cricoid cartilage was significantly and positively correlated with the postoperative scores of MMSE and MoCa ( P<0.05). The blood flow of the internal jugular vein at the plane of hyoid was negatively correlated with the Agitation behavior score ( P<0.01). The cross-sectional area of carotid artery at the plane of left hyoid was significantly and positively correlated with the MMSE score ( P<0.05). Statistically significant differences were observed in MMSE, MoCa, ADL and Agitation behavior score before and after surgery ( P<0.01). Conclusion:dcLVA+ shows a certain therapeutic benefit to the elderly patients with cognitive impairment. The intraoperatively observed pathological changes in cervical lymphatic sacs affect deep cervical lymphatic drainage and the blood flow of carotid artery and jugular vein. Further studies are necessary to find out whether the findings from this study would be the specific pathological changes and the morbidity mechanisms among the elderly patients with cognitive impairment.

Aim To explore the material basis and un-derlying mechanism of Qingre Huoxue Formula(QRHXF)in the treatment of non-small cell lung cancer(NSCLC)by applying network pharmacology,molecular docking technology and bioinformatics com-bined with animal experiments.Methods TCMSP,ECTM,and BATMAN databases were used to obtain active components and corresponding targets of QRHXF;GEO and DisGeNENT databases were con-ducted to acquire NSCLC-associated differential expres-sion genes.By intersecting them,the common targets were obtained.It was chosen to construct a herb-com-ponent-disease network and protein-protein interaction(PPI)network.Furthermore,DAVID database was used to perform gene ontology(GO)function and Kyo-to encyclopedia of genes and genomes(KEGG)path-way enrichment analyses.The molecular docking was presented by adopting Autodock Vina program to verify key targets.RNA-seq datawere downloaded from TC-GA database to obtain differential gene expression.Ka-planMeier(KM)analysis was performed to analyze the relationship between gene expression and overall sur-vival.Mouse subcutaneous tumor model of LLC was established.The effects of QRHXF on body weight,tumor volume and weight were monitored for pharmaco-dynamic analysis.Tumor tissues slides were stained with hematoxylin and eosin(HE)for histopathological examination.Immunohistochemistry(IHC)staining was employed for detecting Ki67 and EP300.Western blot was performed to measure the protein expression of TP53,CDK1 and NTRK1.Results The results of net-work pharmacology showed that a total of seven com-mon targets were screened from NSCLC and QRHXF,and the effect of QRHXF on anti-NSCLC may occur via multiple signaling pathways,including cell cycle.The results of molecular docking indicated that the main ac-tive components of QRHXF had low binding energy and stable docking conformation with the molecular target for treating NSCLC.According to bioinformatic analy-sis,there were significant differences in BRCA1,CDK1 and NTRK1 mRNA expression between tumor tissues and normal tissues,which were also prognostic factors for overall survival.Animal experimental research showed QRHXF inhibited subcutaneous tumor growth(P＜0.01)and improved the quality of life in mice with NSCLC.After QRHXF intervention,the density of tumor cells was significantly reduced,and necrotic are-as were increased.The expressions of Ki67 and EP300 were significantly decreased.Compared with the model group,Western blot showed up-regulation of TP53 and NTRKA(P＜0.05),whereas CDK1 were down-regu-lated(P＜0.05).Conclusion QRHXF exerted anti-NSCLC effects by regulating NTRK1,EP300,TP53,CDK1 and inducing cell cycle,cell cycle arrest and in-hibiting tumor growth,metastasis and angiogenesis.