ObjectiveTo investigate the regulatory effect and potential mechanisms of isocitrate dehydrogenase 3A （IDH3A） on cardiomyocyte hypertrophy. MethodsThe expression of IDH3A in the myocardium of healthy volunteers （n=10） and patients with heart failure （HF） （n=10）， and in the myocardium of mice subjected to transverse aortic constriction （TAC） surgery and sham operation， as well as in phenylephrine （PE）-induced neonatal rat ventricular cardiomyocytes （NRVCs）， was assessed by real-time quantitative polymerase chain reaction （RT-qPCR） and Western blot assay. The effect of adenovirus-mediated overexpression of IDH3A on the expression of hypertrophy-related genes in PE-induced NRVCs was also evaluated. The effect of IDH3A on NRVCs area was examined by phalloidin staining assay. A mutant of IDH3A with abolished enzymatic activity， IDH3A_D208A， was generated through site-directed mutagenesis. The impact of this IDH3A mutant on the hypertrophic phenotype， ATP and ROS levels in NRVCs was evaluated to investigate whether the regulatory role of IDH3A in cardiomyocyte hypertrophy was dependent on its enzymatic activity. The effect of exogenous α-ketoglutaric acid （AKG） on cardiomyocyte hypertrophy was also detected by Western blot and phalloidin staining assay， respectively. ResultsIDH3A was significantly decreased in the myocardium of HF patients， in the myocardium of TAC-operated mice， and in PE-induced NRVCs （P = 0.005 2，P = 0.026 6，P = 0.041 3 and P = 0.006 6， respectively）. Overexpression of IDH3A markedly suppressed the expression of hypertrophy-related genes and the increase of cell size of PE-induced NRVCs （P < 0.000 1， P = 0.000 1 and P = 0.000 2， respectively）. The ATP and ROS analysis indicated that IDH3A inhibited the increases of ATP and ROS levels in PE-induced NRVCs （P = 0.001 2 and P<0.000 1， respectively）， whereas the enzymatically inactive IDH3A mutant lacked this effect. Exogenous AKG provision could， but overexpression of IDH3A mutant failed to suppress PE-induced NRVCs hypertrophy. ConclusionIDH3A inhibits cardiomyocyte hypertrophy via elevating AKG level， providing scientific evidence for study on IDH3A-based treatment of cardiac hypertrophy.

Objective To explore the applicability of four risk assessment methods in evaluating occupational health risks associated with low-level benzene homologues exposure in key work sites within the automobile manufacturing industry. Methods The work sites (paint mixing and spray painting) with exposure to benzene homologues among six automobile manufacturing enterprises in Guangdong Province were selected as the study subjects using the judgmental sampling method. Qualitative risk assessment, exposure index method, non-carcinogenic risk assessment, and the International Council on Mining and Metals risk rating method were independently applied to evaluate the occupational health risks of benzene homologues at these work sites. Accuracy, consistency, and correlation of the four methods were compared. Results The air levels of benzene, toluene, xylene, and ethylbenzene in work sites of paint mixing and spray painting across all six enterprises met national occupational health standards. The median ratios of occupational exposure limits for benzene and toluene in spray painting site were higher than those in paint mixing site (0.017 vs 0.010, P<0.05). Using this ratio as a reference for evaluating method accuracy, the non-carcinogenic risk assessment method could distinguish paint mixing site from spray painting site in terms of risk level (P<0.05), whereas the other three methods could not (both P>0.05). The result of consistency testing revealed that the Kappa coefficients between the four methods ranged from -0.13-0.26, indicating poor consistency. The result of Spearman correlation analysis showed that the non-carcinogenic risk assessment and exposure index methods had higher correlation with occupational exposure limits ratios (Spearman correlation coefficients were 0.501 and 0.656, respectively, all P<0.05). Conclusion Occupational health risk assessment can serve as a supplementary tool to evaluate the hazard level of key work sites in the automobile manufacturing industry. Non-carcinogenic risk assessment shows higher accuracy.

ObjectiveTo investigate the effects of Scutellariae Radix combined with epidermal growth factor receptor-tyrosine kinase inhibitors （EGFR-TKIs） on cell proliferation， apoptosis， cancer stem cell （CSC） marker expression， and metabolism in non-small cell lung cancer （NSCLC） cells. MethodsThe anti-tumor effects of Scutellariae Radix and EGFR-TKIs （gefitinib or osimertinib） in NSCLC cells were evaluated using the cell counting kit-8 （CCK-8） and Annexin V-FITC/propidium iodide （PI） double staining apoptosis assay. The activity of Scutellariae Radix and EGFR-TKIs in three-dimensional （3D） cultures of NSCLC cells was assessed using the CellTiter-Glo® 3D cell viability assay. The mRNA and protein expression levels of CSC markers， sex determining region y box protein 2 （SOX2） and aldehyde dehydrogenase 1 family member A1 （ALDH1A1）， were detected by quantitative real-time polymerase chain reaction （Real-time PCR） and Western blot， respectively. Changes in intracellular reactive oxygen species （ROS） levels were detected by ROS staining， and the redox ratio was detected by femtosecond laser labeling free imaging （FLI）. ResultsUnder both two-dimensional （2D） and 3D culture conditions， compared with the blank group and EGFR-TKI group， the combination group showed significantly reduced cell viability and increased apoptosis rate （P<0.05）. Compared with the EGFR-TKI group， the mRNA and protein levels of CSC markers were significantly downregulated in the combination group （P<0.05）. Additionally， the redox ratio was significantly elevated （P<0.05）， and ROS levels were also increased in the combination group compared with the EGFR-TKI group. ConclusionIn NSCLC cells， Scutellariae Radix enhances the redox ratio and increases ROS levels， thereby inhibiting the expression of CSC markers and strengthening the anti-tumor effects of EGFR-TKIs. This provides a novel molecular mechanism by which Scutellariae Radix may enhance the sensitivity of targeted therapies.

Fungal infections are relatively common among patients with systemic lupus erythematosus (SLE), and patients may be infected simultaneously or sequentially with multiple fungal species. Physicians need to select appropriate antifungal agents and therapeutic regimens based on the type and severity of the infection. Herein, we report a young SLE patient who developed concurrent pulmonary infection with Pneumocystis jirovecii and Trichosporon coremiiforme. The patient presented with a variety of imaging manifestations, with specific features evolving as the disease progressed.

The incidence and mortality of hepatocellular carcinoma (HCC) in China are among the highest in the world, imposing a heavy social burden. Liver resection and liver transplantation are the primary radical treatments for HCC, although most patients are no longer able to meet the surgical requirements at initial diagnosis. Yttrium-90 microsphere selective internal radiation therapy (⁹⁰Y-SIRT) has the advantages of shrinking tumors, enlarging residual liver, regressing portal vein tumor thrombus and improving the quality of life, which can be used for conversion, downstaging and bridging therapy for HCC before surgical treatment, enabling patients regain the chance of radical treatment and reducing the postoperative recurrence rate. This review focuses on the clinical application and progress of ⁹⁰Y-SIRT in this field.

Background As a pillar industry in China, the manufacturing sector has a high incidence of non-fatal occupational injuries. The factors influencing non-fatal occupational injuries in this industry are closely related at various levels, including individual, equipment, environment, and management, making the analysis of these influencing factors complex. Objective To identify influencing factors of non-fatal occupational injuries among manufacturing workers, providing a basis for targeted interventions and surveillance. Methods A total of 2243 frontline workers from cable and shipbuilding enterprises were selected as study subjects to investigate the incidence of non-fatal occupational injuries and collect information at four levels: individual, equipment, management, and environment in past 12 months. Data balancing was performed using resampling, and LASSO regression was used to select factors of non-fatal occupational injuries. The influence degree and type of variables were judged based on the magnitude of the estimated coefficients of each variable, where variables with estimated coefficients > 0 are risk factors, and those <0 are protective factors. The area under the receiver operating characteristic (ROC)curve (AUC) was used to test the performance of the model, with an AUC value > 0.7 indicating good model performance. Results Among the 2243 frontline workers, males accounted for 77.7% (1742 out of 2243), with the main age range being 40-49 years old, representing 29.5% (661 out of 2243), 82.7% of the workers (1854 out of 2243) were married, and 55.6% (1248 out of 2243) had a junior middle school education level. The average monthly income for 51.0% (1144 out of 2243) of the workers was between 5000 and 6999 Chinese Yuan. The incidence of non-fatal occupational injuries among the manufacturing workers was 8.4% (189/2243) in the past 12 months. Among the 22 factors associated with the occurrence of non-fatal occupational injuries (P<0.05), 10 were individual-level factors, including gender, smoking, alcohol consumption, colleague relationships, average exercise duration, job burnout, work fatigue, musculoskeletal disorders, cardiovascular diseases, and neurological and sensory organ diseases; 3 were equipment-level factors, including equipment operability, hazardous workpieces, and safety hazards; 5 were environmental-level factors, including low temperatures, special operations, noise, workspace size, and dirty and disorderly environment; and 4 were management-level factors, including daily working hours, weekly working days, overtime, and pre-job technical training. The AUC value of the LASSO regression model was 0.704 and the final model retained a total of 10 variables. Among them, there were 7 risk factors for non-fatal occupational injuries (coefficient > 0), including safety hazards, musculoskeletal disorders, dangerous workpieces, job burnout, dirty and disorderly environment, smoking, and male gender; and 3 protective factors (coefficient < 0), including pre-job technical training, good colleague relationship, and long working days per week. Conclusion Manufacturing enterprises need to focus on the incidence of non-fatal occupational injuries and conduct targeted interventions for non-fatal occupational injuries by controlling potential safety hazards, providing pre-job technical training, reducing dangerous workpieces, rectifying working environment, and reasonably arranging working hours.

Background Workers in the cable manufacturing industry are exposed to high-speed machinery and equipment for a long time, coupled with heavy workload, which poses significant risks to their physical health. However, the issue of occupational injuries in this industry has not received enough attention yet. Objective To understand the incidence of occupational injury of workers in cable manufacturing industry and to analyze the influencing factors. Method A basic information questionnaire and an occupational injury questionnaire were developed to investigate the occupational injuries of 1 343 workers in a cable manufacturing enterprise in the past year, and a total of 1 225 valid questionnaires were recovered, with an effective rate of 91.2%. Descriptive statistics were used to characterize the causes, injury locations, injury types, and other characteristics of employees’ occupational injuries. Chi-square test was used to analyze the occupational injury status of groups with different demographic characteristics, occupational characteristics, lifestyles, and interpersonal relationships. Logistic regression was used to analyze the influencing factors of occupational injuries. Result The incidence of occupational injuries among workers in a cable manufacturing enterprise in the past year was 8.6%, which mainly happened in male workers (80.0%) and occurred from May to July in summer (45.7%). The main causes were mechanical injuries (32.4%) and object blows (27.6%). The main sources of damage were machinery and equipment (36.2%) as well as raw materials and products (15.2%). The main injuries were located in upper limbs (53.3%) and lower limbs (22.9%). The main types of injuries were fractures (33.3%) and abrasions/contusions/puncture wounds (19.0%). The results of univariate analysis showed that there were statistically significant variations in the incidence of occupational injuries by gender, overtime, pre-job training, years of service in current position, alcohol consumption, physical exercise per week, and co-worker relationship (P<0.05). The logistic regression model showed that workers who exercised less than twice a week, did not participate in pre-job training, worked overtime, and had fair/poor/very poor colleague relationship had a higher risk of occupational injury, while women had a lower risk of occupational injury. Conclusion The distribution of occupational injury population is mainly male, and the time distribution is mainly from May to July. Gender, physical exercise, pre-job training, overtime, and colleague relationship are the influencing factors of occupational injuries. We should strengthen pre-job training, arrange work hours reasonably, and create a good working atmosphere to reduce the occurrence of occupational injuries.

Background Social psychological factors have emerged as a key area of research in occupational injury prevention. Occupational stress, a significant component of social psychology, has garnered widespread attention due to its potential impact on occupational injury. Objective To analyze the factors influencing occupational stress among cable manufacturing workers and explore the relationship between occupational stress and occupational injury, and to provide scientific evidence for reducing occupational stress and injury. Methods A questionnaire on basic demographics, occupational injury, and occupational stress (Effort-Reward Imbalance, ERI) was used to investigate 1343 workers in a cable enterprise. Chi-square test was employed to analyze the distribution of occupational stress across different worker characteristics. Logistic regression was used to identify factors influencing occupational stress, and a decision tree model was applied to assess the impact of occupational stress on injury risk. Results A total of 1225 valid questionnaires were collected, yielding a response rate of 91.2%. The positive rate of occupational stress was 28.7%, while the incidence of occupational injury in the past year was 8.6%. The results of univariate analysis revealed that workers who had experienced occupational injuries had a higher positive rate of occupational stress compared to those without injuries (P<0.05), and there were significant differences in the distribution of occupational stress by genders, overtime work, shift work, daily working hours, fatigue levels, sleep duration, family relationships, and colleague relationships (P<0.05). The logistic regression results showed that individuals who worked shifts, had fair/poor/very colleague relationships, and frequently worked overtime had a higher risk of occupational stress (P<0.05). The final decision tree model confirmed that occupational stress was a risk factor for occupational injury. Additional factors influencing occupational injury included physical exercise, colleague relationships, pre-job training participation, and gender. Workers who exercised less than twice per week and experienced occupational stress had a significantly higher risk of injury (23.6%) compared to those without occupational stress (10.9%). Conclusion Shift work, colleague relationships, and overtime work are correlated with the positive rate of occupational stress among cable manufacturing workers. Implementing a reasonable work schedule and fostering a positive work environment are recommended. Reducing occupational stress may help lower the risk of occupational injuries in this industry.

Background Construction workers represent a high risk group for occupational injuries. Currently, domestic and international studies examining the factors affecting occupational injuries among construction workers focus on demographic and behavioural characteristics. However, there is limited attention to psychosocial, use of digital technology, and health status of workers. Objective To analyze the occurrence of occupational injuries among workers in the construction industry, explore impacts of psychosocial risk, use of digital technology, health status, and preventive measures at the workplace on occupational injuries, and provide a basis for the development of preventive measures. Methods Publicly available data from the European Union Occupational Safety and Health Administration were retrieved, comprising a sample of 2167 construction workers. The outcome indicator was the presence of occupational injuries among workers. A total of 25 variables in the dimensions of psychosocial risk, use of digital technology, health status, and preventive measures at the workplace were extracted after Chi-square test, and then a combination of Boruta's algorithm and logistic regression was applied to identify the key factors affecting occupational injuries. Results Among the 2167 construction workers surveyed, 182 (8.6%) reported experiencing occupational injuries. The Boruta algorithm identified eight characteristics which in descending order of importance were musculoskeletal disorders, job type, depression and anxiety, level of completed education, use of electronic smart products, timely solution of safety problems, overall fatigue, and age. The logistic regression results indicated that six variables had statistically significant effects on occupational injuries: age, job type, level of completed education, musculoskeletal disorders, overall fatigue, and timely solution of safety problems (P<0.05). Conclusion Occupational injuries in construction workers are influenced by a variety of factors, including age, job type, level of completed education, musculoskeletal disorders, overall fatigue, and timely solution of safety problems. Companies and workers should take targeted measures to reduce the incidence of occupational injuries.

Background Light gradient boosting machine (LightGBM) has become a popular choice in prediction models due to its high efficiency and speed. However, the "black box" issues in machine learning models lead to poor model interpretability. At present, few studies have evaluated the severity of occupational injuries from the perspective of LightGBM model and model interpretability. Objective To evaluate the application value of LightGBM models and model interpretability methods in occupational injury prediction. Methods The Mine Safety and Health Administration (MSHA) occupational injury data set of mining industry workers from 1983 to 2022 was used. Injury severity (death/fatal occupational injury and permanent/partial disability) was used as the outcome variable, and the predictor variables included the month of occurrence, age, sex, time of accident, time since beginning of shift, accident time interval from shift start, total experience, total mining experience, experience at this mine, cause of injury, accident type, activity of injury, source of injury, body part of injury, work environment type, product category, and nature of injury. Feature sets were screened using least absolute shrinkage and selection operator (Lasso) regression. A LightGBM model was then employed to predict occupational injury, with area under curve (AUC) of the model serving as the primary evaluation metric; an AUC closer to 1 indicates better predictive performance of the model. The interpretability of the model was evaluated using Shapley additive explanations (SHAP). Results Through Lasso regression, 7 key influencing factors were identified, including accident time interval from shift start, experience at this mine, cause of injury, accident type, body part of injury, nature of injury, and work environment type. A LightGBM model, constructed based on feature selection via Lasso regression, demonstrated good predictive performance with an AUC value of 0.9941 (95%CI: 0.9917, 0.9966), accuracy of 0.9743, specificity of 0.9781, and sensitivity of 0.9640. The predicted probability of fatal occupational injuries showed high consistency with the actual probability of fatal occupational injuries. In the occupational injury prediction model, the importance of each indicator was analyzed through its SHAP value, and it was found that the body part of injury and the nature of injury were the two main features that affected the prediction results of the model, and the impacts of other features were relatively small. The distribution of SHAP values across body part of injury was broad, with significant impacts on the model's prediction of fatal risk, particularly for injuries to the head and neck, as well as multi-part injuries. The nature of injury also exerted influences on the model in different directions, with suffocation/drowning, crushing, and multi-part injuries having a greater impact on the risk of fatal occupational injuries. Conclusion LightGBM model is capable of efficiently processing large-scale data and providing high-precision prediction results. Research on model interpretability aids in more accurately exploring and analyzing various key risk factors for fatal occupational injuries among mining workers, and further reveals the complex interactions among these factors. This, in turn, enables better preventive intervention and protection measures, as well as optimal resource allocation for labor workers.