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.

Osteoarthritis (OA), a highly prevalent degenerative joint disease worldwide, is defined by articular cartilage degradation, abnormal bone remodeling, and persistent chronic inflammation. It severely compromises patients’ quality of life, and currently, there is no radical cure. Abnormal mechanical stress is widely regarded as a core driver of OA pathogenesis, and the exploration of mechanical signal perception and transduction mechanisms has become crucial for deciphering OA’s pathophysiological processes. Piezo1, a key mechanosensitive cation channel belonging to the Piezo protein family, has recently gained significant attention due to its pivotal role in mediating cellular responses to mechanical stimuli in joint tissues. This review systematically examines Piezo1’s expression patterns, regulatory mechanisms, and pathological functions in OA, with a particular focus on its dual roles in modulating chondrocyte homeostasis and bone metabolism disorders, while also delving into the underlying molecular signaling pathways and potential therapeutic implications. Piezo1, consisting of approximately 2 500 amino acids and forming a unique trimeric propeller-like structure, is widely expressed in chondrocytes, osteocytes, mesenchymal stem cells, and synovial cells. It exhibits permeability to cations such as Ca²⁺, K⁺, and Na⁺, and directly responds to membrane tension changes induced by mechanical stimuli like fluid shear stress and mechanical overload. In OA patients and animal models, Piezo1 expression is significantly upregulated, especially in cartilage regions subjected to abnormal mechanical stress (e.g., human temporomandibular joint cartilage). This overexpression is closely associated with aggravated cartilage degeneration, increased chondrocyte apoptosis, accelerated cellular senescence, and intensified inflammatory responses. Mechanical overload and pro-inflammatory cytokines (e.g., IL-1β) are key inducers of Piezo1 upregulation: IL-1β activates the PI3K/AKT/mTOR signaling pathway to enhance Piezo1 expression, forming a pathogenic positive feedback loop that inhibits chondrocyte autophagy, promotes apoptosis, and further accelerates joint degeneration. Mechanistically, Piezo1 mediates OA progression through multiple interconnected pathways. When activated by mechanical stress, Piezo1 triggers excessive Ca²⁺ influx, leading to endoplasmic reticulum stress (ERS) and mitochondrial dysfunction, which directly induce chondrocyte apoptosis. This process involves the activation of downstream signaling cascades such as cGAS-STING and YAP-MMP13/ADAMTS5. YAP, a transcriptional regulator, upregulates the expression of matrix metalloproteinase 13 (MMP13) and aggrecanase (ADAMTS5), thereby accelerating cartilage matrix degradation. Additionally, Piezo1-driven Ca²⁺ overload promotes the accumulation of reactive oxygen species (ROS) and upregulates senescence markers (p16 and p21), accelerating chondrocyte senescence via the p38MAPK and NF-κB pathways. Senescent chondrocytes secrete senescence-associated secretory phenotype (SASP) factors (e.g., IL-6, IL-1β), further amplifying joint inflammation. In terms of bone metabolism, Piezo1 maintains joint homeostasis by promoting the differentiation of fibrocartilage stem cells into chondrocytes and balancing bone formation and resorption through regulating the FoxC1/YAP axis and RANKL/OPG ratio. Therapeutically, targeting Piezo1 shows promising potential. Preclinical studies have demonstrated that Piezo1 inhibitors (e.g., GsMTx4) can reduce joint damage and alleviate pain in OA mice. Simultaneously, siRNA-mediated co-silencing of Piezo1 and TRPV4 (another mechanosensitive channel) decreases intracellular Ca²⁺ concentration, inhibits chondrocyte apoptosis, and promotes cartilage repair. Conditional knockout of Piezo1 using Gdf5-Cre transgenic mice alleviates cartilage degeneration in post-traumatic OA models by downregulating MMP13 and ADAMTS5 expression. Despite existing challenges, such as off-target effects of inhibitors, inefficient local drug delivery, and interindividual genetic variability, strategies like developing selective Piezo1 antagonists, optimizing targeted nanocarriers, and combining Piezo1-targeted therapy with physical therapy provide viable avenues for clinical translation. The authors propose that Piezo1 serves as a critical therapeutic target for OA, and future research should focus on deciphering its context-dependent regulatory networks, developing tissue-specific intervention strategies, and validating their efficacy and safety in clinical trials to address the unmet medical needs of OA patients.

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 investigate the characteristics of public health emergencies in Jinhua City, Zhejiang Province from 2014 to 2023, so as to provide the reference for prevention and control of public health emergencies. Methods: Data of public health emergencies and related information in Jinhua City from 2014 to 2023 were collected through Emergency Public Reporting System of Chinese Disease Prevention and Control Information System. Attack rates, and distribution of time, areas and places were descriptively analyzed. Results: A total of 276 public health emergencies were reported in Jinhua City from 2014 to 2023. There were 10 324 reported cases and 7 deaths, with an attack rate of 0.32%. There were 53 Ⅳ-level (19.20%) and 223 unclassified public health emergencies (80.80%). Infectious disease emergencies were predominant types, accounting for 97.83% (270 events). The three most common infectious disease emergencies were other infectious diarrhea (42.03%), influenza (21.01%) and COVID-19 (16.30%). The reported public health emergencies peaked in November and December, with 66 and 45 events reported, respectively. The three most counties (cities, districts) included Yiwu City, Wucheng District and Lanxi City, accounting for 24.28% (67 events), 18.48% (51 events) and 11.96% (33 events), respectively. School and preschool institutions were predominant places where public health emergencies occurred (198 events, 71.74%). Conclusions The public health emergencies in Jinhua City from 2014 to 2023 were Ⅳ-level and unclassified emergencies, and infectious disease emergencies were predominant. November and December were the peak reporting periods, and schools and preschool institutions were the main places where these events occurred.

ObjectiveThe utilization of assisted reproductive technology to rescue genetically modified mouse strains with reproductive disorders provides a reference for improving techniques to preserve valuable experimental mouse strains. MethodsIn vitro fertilization-embryo transfer (IVF-ET) technology was performed on 28 strains of infertile male mice aged 9-18 months. Several indicators such as sperm density and sperm motility in infertile male mice were assessed to select the most viable sperm for IVF-ET experiments. Fertility rate, abnormal egg rate, and birth rate were recorded after the birth of the pups. An optimized ovarian transplantation procedure was applied to 12 strains of infertile female mice aged 8-18 months. 6-week-old female mice with the same genetic background were selected as recipients. One intact ovary was removed from each recipient mouse, and the contralateral oviduct was ligated. An ovary from a donor mouse was isolated and transplanted orthotopically into the side where the ovary had been removed in the recipient mouse. Twenty-one days post-surgery, recipient mice were co-housed with 8-week-old wild type male mice of the same genetic background for breeding. Data such as the pregnancy rate and live birth rate of the recipients were recorded after the birth of the pups. ResultsIVF-ET successfully rescued 28 mouse strains, with the oldest male mice being 18 months old. The success rate of the first round of IVF-ET experiments was 89.29% (25/28). The average fertility rate of IVF in infertile male mice was (51.01±14.97)%, the abnormal egg rate was (9.03±5.28)%, and the birth rate of offspring mice was (18.60±7.03)%. 39 out of 40 ovarian transplant recipient mice survived, with a pregnancy rate of 33.33% (13/39) for ovarian transplant recipients, and a live birth rate of 17.95% (7/39). Four mouse strains were successfully rescued using optimized ovarian transplantation technology, with the oldest female mice being 18 months old. 8 strains were not rescued as they failed to produce offspring that survived to sexual maturity. ConclusionIVF-ET is an effective approach for rescuing mice with reproductive disorders caused by different reasons, especially for those beyond the optimal breeding age. Ovarian transplantation technology can also be used as an alternative for aged female mice. But its success rate is relatively lower than that of IVF-ET, and carries a higher experimental risk.

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.

Background/Aims: Metabolic dysfunction-associated steatohepatitis (MASH) is a significant risk factor for gallstone formation, but mechanisms underlying MASH-related gallstone formation remain unclear. Golgi membrane protein 1 (GOLM1) participates in hepatic cholesterol metabolism and is upregulated in MASH. Here, we aimed to explore the role of GOLM1 in MASH-related gallstone formation. Methods: The UK Biobank cohort was used for etiological analysis. GOLM1 knockout (GOLM1-/-) and wild-type (WT) mice were fed with a high-fat diet (HFD). Livers were excised for histology and immunohistochemistry analysis. Gallbladders were collected to calculate incidence of cholesterol gallstones (CGSs). Biles were collected for biliary lipid analysis. HepG2 cells were used to explore underlying mechanisms. Human liver samples were used for clinical validation. Results: MASH patients had a greater risk of cholelithiasis. All HFD-fed mice developed MASH, and the incidence of gallstones was 16.7% and 75.0% in GOLM1-/- and WT mice, respectively. GOLM1-/- decreased biliary cholesterol concentration and output. In vivo and in vitro assays confirmed that GOLM1 facilitated cholesterol efflux through upregulating ATP binding cassette transporter subfamily G member 5 (ABCG5). Mechanistically, GOLM1 translocated into nucleus to promote osteopontin (OPN) transcription, thus stimulating ABCG5-mediated cholesterol efflux. Moreover, GOLM1 was upregulated by interleukin-1β (IL-1β) in a dose-dependent manner. Finally, we confirmed that IL-1β, GOLM1, OPN, and ABCG5 were enhanced in livers of MASH patients with CGSs. Conclusions In MASH livers, upregulation of GOLM1 by IL-1β increases ABCG5-mediated cholesterol efflux in an OPN-dependent manner, promoting CGS formation. GOLM1 has the potential to be a molecular hub interconnecting MASH and CGSs.

Objective: To explore the safety and feasibility of the disconnection of the left renal artery preferentially during robot-assisted inferior vena cava (IVC) thrombectomy for patients with left renal cell carcinoma and tumor emboli. Methods: Clinical data of 7 patients who underwent robot-assisted IVC thrombectomy and radical nephrectomy in the First Affiliated Hospital of Zhengzhou University during Dec.2021 and Oct.2024 were retrospectively analyzed.Thrombectomy was performed first，followed by nephrectomy. The “IVC-first, kidney-last”robotic technique was developed to minimize chances of IVC thrombus. When patients in left lateral decubitus position, the left renal artery was severed from the right side through the inferior vena cava and abdominal aorta. After removal of thrombus from IVC was completed, patients changed to the right lateral position to complete radical left nephrectomy. Results: Imaging examinations revealed that the median diameter of the renal cell carcinomas was 83(46-99) mm; the median length of the inferior vena cava cancerous emboli was 49(2-91) mm.According to the Mayo classification，the cancerous emboli were gradeⅠ in 2 cases，gradeⅡ in 4 cases，and grade Ⅲ in 1 case.All surgeries were successful.The median operation time was 248(201-331) minutes，blood loss 500(200-1000) mL，and 6 cases required intraoperative blood transfusion.The median time for transition into the intensive care unit was 1(1-4) days，and drainage tube removal 6(5-12) days.Serum creatinine increased significantly in 5 cases，4 of which returned to normal after 1 week，but 1 had renal insufficiency (creatinine 166 μmol/L).Chylous fistula occurred in 1 patient，and lower extremity venous thrombosis developed in 3 patients.Pathological examinations indicated 6 cases of renal cell carcinoma and 1 case of MiT family translocation renal cell carcinoma.During the median follow-up of 17(1-35) months，5 cases were tumor-free，while 2 had lung and retroperitoneal metastases.They received targeted therapy of axitinib combined immunotheraphy and lived with tumors. Conclusion: In the left lateral position for left renal cell carcinoma with cancerous emboli，robot-assisted laparoscopic thrombectomy by crossing the inferior vena cava and abdominal aorta and disconnecting the left renal artery first is safe and feasible.

Objective To investigate the factors influencing international normalized ratio (INR)>3.0 in patients undergoing warfarin anticoagulation therapy after mechanical heart valve replacement. Methods A retrospective analysis was performed on the clinical data of patients who underwent mechanical heart valve replacement surgery and received warfarin anticoagulation therapy at West China Hospital of Sichuan University from January 1, 2011 to June 30, 2022. Based on the discharge INR values, patients were divided into two groups: an INR≤3.0 group and an INR>3.0 group. The factors associated with INR>3.0 at the time of discharge were analyzed. Results A total of 8901 patients were enrolled, including 3409 males and 5492 females, with a median age of 49.3 (43.5, 55.6) years. The gender, body mass index (BMI), New York Heart Association (NYHA) cardiac function grading, INR, glutamic oxaloacetic transaminase, and preoperative prothrombin time (PT) were statistically different between the two groups (P<0.05). Multivariate logistic regression analysis revealed that lower BMI, preoperative PT>15 s, and mitral valve replacement were independent risk factors for INR>3.0 at discharge (P<0.05). Conclusion BMI, preoperative PT, and surgical site are factors influencing INR>3.0 at discharge in patients undergoing warfarin anticoagulation therapy after mechanical heart valve replacement. Special attention should be given to patients with lower BMI, longer preoperative PT, and mitral valve replacement to avoid excessive anticoagulation therapy.