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.

With the widespread use of antibiotics, drug-resistant bacterial infections have become a significant threat to human health. Finding new antibacterial strategies that can effectively control drug-resistant bacterial infections has become an urgent task. Unlike small molecule drugs that target bacterial proteins, antisense oligonucleotide (ASO) can target genes related to bacterial resistance, pathogenesis, growth, reproduction and biofilm formation. By regulating the expression of these genes, ASO can inhibit or kill bacteria, providing a novel approach for the development of antibacterial drugs. To overcome the challenge of delivering antisense oligonucleotide into bacterial cells, various drug delivery systems have been applied in this field, including cell-penetrating peptides, lipid nanoparticles and inorganic nanoparticles, which have injected new momentum into the development of antisense oligonucleotide in the antibacterial realm. This review summarizes the current development of small nucleic acid drugs, the antibacterial mechanisms, targets, sequences and delivery vectors of antisense oligonucleotide, providing a reference for the research and development of antisense oligonucleotide in the treatment of bacterial infections.

Aim To investigate the im-munomodulatory effects of Ononin on Th17/Treg bal-ance and inflammatory responses,and to evaluate its neuroinmune regulatory role in ischemic stroke.Methods A Foxp3 promoter-driven luciferase reporter assay was used to assess the effect of Ononin on Foxp3 transcriptional activity.The effect of Ononin on the dif-ferentiation of na?ve CD4+T cells into Th 17 and Treg subsets was evaluated by flow cytometry.A transient middle cerebral artery occlusion(tMCAO)model was established in mice to evaluate the effects of Ononin on infarct size,neurological recovery,body weight resto-ration,and survival rate.Flow cytometry and RT-PCR were conducted to elucidate the immunological mechanisms underlying Ononin's effects.Results On-onin significantly enhanced Foxp3 promoter activity,promoted the differentiation of na?ve CD4+T cells into Treg cells,and suppressed Th17 polarization.In the tMCAO model,Ononin significantly reduced acute in-farct size,improved survival,ameliorated long-term neurological deficits,and increased Treg cell propor-tions in ischemic brain tissue.Conclusions Ononin modulates peripheral and central inflammation by resto-ring the Th17/Treg immune balance,thereby exerting significant immunomodulatory effects.Its therapeutic benefit in ischemic stroke is closely linked to immune balance restoration.

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 impact of early invasive arterial blood pressure (IBP) monitoring on survival and neurological outcomes in out-of-hospital cardiac arrest (OHCA) patients undergoing extracorporeal cardiopulmonary resuscitation (ECPR).Methods:This retrospective cohort study analyzed 44 OHCA patients receiving ECPR between January 2021 and January 2023. Patients were divided into: Early intervention group : IBP established within 3 min of ECMO initiation; Late intervention group : IBP established after ICU admission. Baseline characteristics, ECMO parameters, and clinical outcomes were compared. Multivariable logistic regression (adjusted for age, initial rhythm, etc.) and Spearman's correlation were used.Results:This study included a total of 44 patients treated with OHCA and ECPR, divided into an early intervention group of 23 cases and a late intervention group of 21 cases. The early intervention group showed significantly higher: Survival to discharge (43.5% vs. 9.5%, P<0.05), Good neurological recovery (CPC 1-2: 34.8% vs. 9.5%, P<0.05).Early intervention independently predicted survival (adjusted OR=18.84, 95% CI:1.97-179.98, P=0.01). Stratified analysis by pH (cutoff 7.0) demonstrated consistent benefits in both pH>7.0 ( aOR=0.392, 95% CI:0.106-0.678) and pH≤7.0 subgroups ( aOR=0.385, 95% CI: 0.075-0.695; interaction P=0.183). Early IBP positively correlated with CPC scores ( ρ=0.40, P=0.007). Conclusions:Early IBP monitoring significantly improves survival and neurological outcomes in OHCA-ECPR patients, supporting its integration into standardized protocols.

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 cisplatin-induced cardiovascular toxicity and explore the protective effects and potential mechanism of curcumin co-treatment. METHODS: Forty adult male Sprague-Dawley rats were numbered and randomly divided into control group, cisplatin group (7.5 mg/kg, once a week, for 2 weeks), curcumin group (200 mg/kg per day, for 2 weeks) and cisplatin+curcumin group (cisplatin 7.5 mg/kg, once a week, and curcumin 200 mg/kg per day for 2 weeks) by a random number table method, with 10 rats in each group. Cardiac and vascular morphology and functions were assessed using hematoxylin-eosin and Masson's trichrome staining, serum indexes detection, echocardiography, electrocardiogram (ECG), blood pressure monitoring, vascular ring isometric tension measurement, and left ventricular pressure evaluation. The expressions of extracellular signal-regulated kinases (ERK) and phosphorylated-ERK (p-ERK) were analyzed by immunohistochemical staining. RESULTS: Cisplatin treatment induced notable cardiac alteration, as evidenced by changes in cardiac morphology, elevated serum enzymes (P<0.05), ECG abnormalities, and increased left ventricular end-diastolic pressure (P<0.05). Meanwhile, cisplatin significantly increased arterial pulse pressure (P<0.01), primarily due to a decrease in diastolic blood pressure. Severe fibrosis was also observed in the thoracic aorta wall. In vascular ring experiments, cisplatin treatment led to a significant reduction in phenylephrine-induced contraction (P<0.05) and acetylcholine-induced relaxation (P<0.01). Notably, Curcumin co-administration significantly alleviated cisplatin-induced cardiovascular damages, as demonstrated by improvement in these parameters. Furthermore, ERK expression in the myocardium and p-ERK expression in vascular smooth muscle cells were significantly upregulated following curcumin co-treatment. CONCLUSIONS Curcumin protects the heart and vasculature from cisplatin-induced damages, likely by upregulating ERK/p-ERK expression. These findings suggest that curcumin may serve as a promising therapeutic strategy for mitigating cisplatin-associated cardiovascular toxicity during tumor chemotherapy. In vitro cell culture experiments are needed to clarify the underlying mechanism.
Animals ; Curcumin/therapeutic use* ; Cisplatin/adverse effects* ; Rats, Sprague-Dawley ; Male ; Up-Regulation/drug effects* ; Extracellular Signal-Regulated MAP Kinases/metabolism* ; Phosphorylation/drug effects* ; Electrocardiography ; Blood Pressure/drug effects* ; Rats ; MAP Kinase Signaling System/drug effects*

Triple-negative breast cancer (TNBC) remains a refractory subtype of breast cancer due to its resistance to various therapeutic strategies. In this study, we introduce a "brake-release and accelerator-pressing" approach to engineer a microneedle patch embedded with copper-doped Prussian blue nanoparticles (Cu-PB) and the ferroptosis inducer sorafenib (SRF) for raised chemodynamic (CDT)/photothermal (PTT) combination therapy against TNBC. Upon transdermal insertion, the dissolving microneedles swiftly disintegrate and facilitate the release of SRF. Under gentle external light exposure, copper ions (Cu²⁺) and iron ions (Fe³⁺) were liberated from Cu-PB. The direct chelation of Cu²⁺ and the indirect suppression by SRF, collectively attenuate glutathione peroxidase 4 (GPX4) enzymatic function, destabilizing the cellular redox equilibrium (referred to as the "brake-release" strategy). The release of Cu²⁺ and Fe³⁺ ions instigates a Fenton/Fenton-like reaction within tumor cells, further yielding hydroxyl radicals and elevating reactive oxygen species (ROS) concentrations (referred to as the "accelerator-pressing" strategy). This overwhelming ROS accumulation, coupled with the impaired clearance of resultant lipid peroxides (LPO), ultimately triggers a robust ferroptosis cell death response. In summary, this study presents an innovative combinatorial therapeutic strategy based on dual-ferroptosis induction for TNBC, implying a promising therapeutic platform for developing ferroptosis-centered treatments for this aggressive breast cancer subtype.

A novel analytical method was developed in this study by combining online solid phase extraction with ultra performance liquid chromatography-tandem mass spectrometry(Online SPE-UPLC-MS/MS)for simultaneous determination of 50 kinds of steroid hormones in surface water.Specifically,after high-speed centrifugation of 4 mL water samples,the supernatant was directly injected into an Oasis HLB online SPE column for enrichment and purification.Subsequently,the target compounds were transferred to the analytical column via valve switching for separation and analysis.The chromatographic separation was performed on a Thermo Acclaim RSLC C18 column(100 mm×2.1 mm,2.2 μm),using a mobile phase composed of 5 mmol/L ammonium fluoride aqueous solution and acetonitrile.Mass spectrometric detection was conducted in positive ion mode,utilizing multiple reaction monitoring(MRM)with quantification achieved by the internal standard method.The method validation demonstrated that the limits of detection(LOD)for the 50 kinds of steroid hormones ranged from 0.02 to 0.50 ng/L,while the limits of quantification(LOQ)were between 0.08 and 1.67 ng/L.The average recoveries in surface water samples at spiked concentrations of 5,20 and 200 ng/L were between 74.1％and 119％,with relative standard deviations(RSDs)of 0.2％to 9.9％.This method was applied to analyze 11 surface water samples collected from sites surrounding a pharmaceutical and chemical industrial park.A total of 44 kinds of steroid hormones were detected,with concentrations ranging from 0.11 to 88.6 ng/L,revealing the presence of hormone contamination in the environmental waters surrounding industrial areas.Compared with the traditional offline SPE methods,the proposed online SPE technique significantly reduced sample volume requirements and pretreatment time,while minimizing the loss of target compounds during the pretreatment process.Moreover,compared to reported online SPE techniques,this method achieved high-throughput analysis of multiple classes of steroid hormones,with lower detection limits and higher recoveries.Overall,this method provided rapid sample preparation,high sensitivity,and excellent stability,making it suitable for the direct analysis of trace steroid hormones in surface water.