This study aimed to investigate the effects of zidovudine (AZT) on high-fat diet (HFD)-induced metabolic disturbances in rats and its underlying mechanisms. The HFD rat model was established, and the animals were divided into the control group, the model group, and the AZT-treated group at low (25 mg/kg) and high (50 mg/kg) doses. Metabolic phenotype, hepatic lipid deposition, oxidative stress, mitochondrial function, and peroxisome proliferator-activated receptor (PPAR) signaling were evaluated. AZT treatment significantly mitigated HFD-induced body weight gain and reduced both the mass and adipocyte size of inguinal and epididymal white adipose tissues; it also enhanced metabolic flexibility and improved glucose tolerance without elevating blood lactate levels. High-dose AZT further lowered hepatic triglyceride accumulation, ameliorated steatosis, and additionally, attenuated hepatic oxidative stress by increasing superoxide dismutase (SOD) activity and decreasing malondialdehyde (MDA) levels. Western blot analysis revealed that AZT upregulated hepatic PPARα and carnitine palmitoyltransferase 1α (CPT1α), while downregulating PPARγ expression. In conclusion, AZT effectively ameliorates HFD-induced metabolic disorders without inducing mitochondrial toxicity, which may be related to the promotion of fatty acid oxidation, the reduction of oxidative stress, and the modulation of both the PPAR signaling pathway and pyrimidine metabolism.

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 and safety of warfarin anticoagulation therapy guided by gene test in patients undergoing left ventricular assist device （LVAD） implantation， and to analyze the influencing factors of warfarin anticoagulation efficacy. METHODS Patients who underwent LVAD implantation at the Heart and Vascular Center of Northern Jiangsu People’s Hospital from January 2023 to October 2024 and required warfarin anticoagulant therapy were selected as the study subjects. They were divided into genetic testing group （n＝51） and empirical treatment group （n＝17） based on whether they underwent CYP2C9 and VKORC1 gene test. The gene test group was given warfarin based on the predicted dose calculated by gene test， while the empirical treatment group was given warfarin by clinical doctors based on international normalized ratio （INR） experience， all patients were given warfarin once a day. Follow-up observation was conducted for 6 months to compare the effectiveness [time in therapeutic range（TTR）， the time required to reach INR for the first time， the incidence of embolic events， the incidence of INR＜1.5 events] and safety （the incidence of major and minor bleeding events，the incidence of INR＞3.5 events） of warfarin treatment between two groups of patients. According to whether the patient’s TTR was ≥60%， they were divided into TTR≥60% group （n＝20） and TTR＜60% group （n＝48）. Univariate and multivariate binary Logistic regression analysis were used to determine the factors affecting the anticoagulant effect of warfarin in patients. RESULTS The TTR of patients in the gene test group was significantly higher than that in the empirical treatment group （P＜0.05）. The incidence of INR＜1.5 events in the gene test group was significantly lower than in the empirical treatment group （P＜0.05）. The incidence of minor bleeding events and INR＞3.5 events in the gene test group were lower than in the empirical treatment group， but the difference was not statistically significant （P＞0.05）. The results of multivariate binary Logistic regression analysis showed that gene test was an independent protective factor for warfarin anticoagulant therapy [odds ratio （OR）＝10.842， 95% confidence interval （CI）： 1.211-27.037， P＝0.033]， and the combination of statins was an independent risk factor for warfarin anticoagulant therapy [OR＝0.196， 95%CI： 0.045-0.861， P＝0.031]. CONCLUSIONS Under the guidance of gene test， warfarin anticoagulation therapy for LVAD patients after implantation can improve TTR， shorten the anticoagulation target time， and has good safety； meanwhile， it should be noted that the combination of statins may enhance the anticoagulant effect of warfarin， thereby increasing the risk of bleeding in patients.

Objective To evaluate the effect of a multidisciplinary collaborative quality control management (hereinafter referred to as "QC management") on improving the performance of biological safety cabinets in hospital. Methods A total of 63 ClassⅡbiological safety cabinets in active use at Peking University Third Hospital were selected as the study subjects using the before-after study mode. Conventional management was implemented on the biological safety cabinets from 2018 to 2021. QC management was used in 2022. The compliance of biological safety cabinets management norm and performance differences under the two models were compared. Results The median and the 25th and 75th percentiles [M(P₂₅, P₇₅)] of the service life among these 63 biological safety cabinets were 3 (1,6) years. The overall performance pass rate and inflow velocity pass rate of biological safety cabinets were higher in the QC management than that in the conventional management (90.5% vs 65.1%, 96.8% vs 84.1%, both P<0.05). However, there was no significant difference in downflow velocity, high-efficiency particulate air filter integrity, cleanliness, airflow smoke pattern, noise, and illumination pass rates of biosafety cabinets before and after the implementation of QC management (79.4% vs 88.9%, 90.5% vs 100.0%, 96.8% vs 100.0%, 85.7% vs 100.0%, 100.0% vs 100.0%, and 85.7% vs 96.8%, respectively; all P>0.05). Conclusion sQC management improves the standardization of biological safety cabinet management and key performance indicators in hospital.

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

High-performance liquid chromatography(HPLC) was used to determine the content of three major components in Citri Reticulatae Semen(CRS), including limonin, nomilin, and obacunone. The chromaticity of the CRS sample during salt processing and stir-frying was measured using a color difference meter. Next, the relationship between the color and content of the salt-processed CRS sample was investigated through correlation analysis. By integrating the oil bath technique for processing simulation with HPLC, the changes in the relative content of nomilin and its transformation products were analyzed, with its structural transformation pattern during processing identified. Additionally, RAW264.7 cells were induced with lipopolysaccharides(LPSs) to establish an inflammatory model, and the anti-inflammatory activity of nomilin and its transformation product, namely obacunone was evaluated. The results indicated that as processing progressed, E~*ab and L~* values showed a downward trend; a~* values exhibited a slow increase over a certain period, followed by no significant changes, and b~* values remained stable with no significant changes over a certain period and then started to decrease. The limonin content remained barely unchanged; the nomilin content decreased, and the obacunone increased significantly. The changing trends in content and color parameters during salt-processing and stir-frying were basically consistent. The content of nomilin and obacunone was significantly correlated with the colorimetric values(L~*, a~*, b~*, and E~*ab), while limonin content showed no significant correlation with these values. By analyzing HPLC patterns of nomylin at different heating temperatures and time, it was found that under conditions of 200-250 ℃ for heating of 5-60 min, the content of nomilin significantly decreased, while the obacunone content increased pronouncedly. The in vitro anti-inflammatory activity results indicated that compared to the model group, the group with a high concentration of nomilin and the groups with varying concentrations of obacunone showed significantly reduced release of nitric oxide(NO)(P<0.01). When both were at the same concentration, obacunone showed better performance in inhibiting NO release. In this study, the obvious correlation between the color and content of major components during the processing of CRS samples was identified, and the dynamic patterns of quality change in CRS samples during processing were revealed. Additionally, the study revealed and confirmed the transformation of nomilin into obacunone during processing, with the in vitro anti-inflammatory activity of obacunone significantly greater than that of nomilin. These findings provided a scientific basis for CRS processing optimization, tablet quality control, and its clinical application.
Mice ; Animals ; Drugs, Chinese Herbal/pharmacology* ; RAW 264.7 Cells ; Limonins/chemistry* ; Chromatography, High Pressure Liquid ; Citrus/chemistry* ; Color ; Benzoxepins/chemistry* ; Anti-Inflammatory Agents/chemistry*

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*

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.