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.

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.

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.

ObjectiveRecent studies have highlighted the critical role of NUDT19 in the initiation, progression, and prognosis of specific cancer types. However, its involvement in pan-cancer analysis has not been fully characterized. This study aims to systematically explore the expression patterns, clinical significance, and immune-related functions of NUDT19 in various cancer types through multi-omics analysis, further revealing its potential role in cancer, particularly its functional and therapeutic target value in leukemia. MethodsTo achieve this goal, various bioinformatics approaches were employed to evaluate the expression patterns, clinical significance, and immune-related functions of NUDT19 in tumors and normal tissues. Additionally, we analyzed the mutation characteristics of NUDT19 and its relationship with epigenetic modifications. Using the single-cell analysis tool SingleCellBase, we explored the distribution of NUDT19 across different cell subpopulations in tumors. To validate these findings, qRT-PCR was used to measure NUDT19 expression levels in specific tumor cell lines, and we established acute myeloid leukemia (AML) cell lines (HL-60 and THP-1) to conduct NUDT19 knockdown and overexpression experiments, assessing its effects on leukemia cell proliferation, apoptosis, and invasion. ResultsPan-cancer analysis revealed the dysregulated expression of NUDT19 across multiple cancer types, which was closely associated with poor prognosis, clinical staging, and diagnostic markers. Furthermore, NUDT19 was significantly correlated with tumor biomarkers, immune-related genes, and immune cell infiltration in different cancers. Mutation analysis showed that multiple mutations in NUDT19 were significantly associated with epigenetic changes. Single-cell analysis revealed the heterogeneity of NUDT19 expression in cancer cells, suggesting its potentially diverse functional roles in different cell subpopulations. qRT-PCR experiments confirmed the significant upregulation of NUDT19 in various tumor cell lines. In AML cell lines, NUDT19 knockdown led to reduced cell proliferation and invasion, with increased apoptosis, while NUDT19 overexpression significantly enhanced cell proliferation and invasion while reducing apoptosis. ConclusionThis study demonstrates the diverse roles of NUDT19 in various cancer types, with a particularly prominent functional role in leukemia. NUDT19 is not only associated with tumor initiation and progression but may also influence cancer progression through the regulation of immune microenvironment and epigenetic mechanisms. Our research highlights the potential of NUDT19 as a therapeutic target, particularly for targeted therapies in malignancies such as leukemia, with significant clinical application prospects.

Objective: To construct an index system of public health risk assessment for air pollution emergency, so as to provide a tool of evaluating the public health risks of air pollution emergency. Methods: Index system of public health risk assessment for air pollution emergency was established through literature review and group discussions. The index system was determined through two rounds of Delphi expert consultations involving specialists in environmental health, toxicology, epidemiology, health emergency response, and atmospheric monitoring. The effectiveness of the consultation was evaluated by positive coefficient, authority coefficient and coordination coefficient. The weights of index were determined using a combination weighting method of the expert scoring method and the entropy weight method. Results: Fifteen experts participated in the consultation, including 11 males and 4 females. There were 8 experts with a doctor degree, 6 experts with a master degree, 1 experts with a bachelor degree. A total of 11 experts with senior professional titles, and 4 experts with associate senior professional titles. The average work experience was (23.73±10.48) years. The expert positive coefficients for the two rounds of consultations were 83.33% and 100%, respectively. The expert authority coefficients were 0.794 and 0.811, respectively. The coefficients of variation for the importance, feasibility, and sensitivity scores of each index in the two rounds of comsultations were 0.097 to 0.352, 0.078 to 0.478, 0.115 to 0.388, and 0.049 to 0.133, 0.052 to 0.153, 0.049 to 0.178, respectively. The Kendall's coefficients of concordance were 0.237 and 0.440 (both with P<0.05) for the two rounds of consultations. The constructed assessment index system included "likelihood" "hazard" "vulnerability" "controllability" with comprehensive weights of 0.206 7, 0.059 6, 0.378 1, and 0.355 5, respectively. Among the 13 second indicators, "monitoring capability" had the highest comprehensive weight of 0.192 6. Among the 40 tertiary indicators, "real-time monitoring of atmospheric pollutants" "retrospective evaluation of early forecasting results" "types, quantities, and combined effects of atmospheric pollutants" "exposure modes of the population to atmospheric pollutants" had relatively high comprehensive weights of 0.089 5, 0.043 1, 0.041 1 and 0.040 3, respectively. Conclusion The constructed index system of public health risk assessment for air pollution emergency can be applied to the public health risk assessment for air pollution emergencies.