Background At present, domestic research on job burnout and health-related productivity is limited to medical workers, and the impact of job burnout on health-related productivity of enterprise staff deserves attention. Objective To explore the association between job burnout and health-related productivity loss among enterprise staff. Methods A cross-sectional online questionnaire survey was conducted among enterprise staff who were selected from seven enterprises in Minhang District of Shanghai. The Chinese version of Maslach Burnout Inventory-General Survey (MBI-GS) was used to assess job burnout, and a questionnaire based on and modified from the WHO Health and Work Performance Questionnaire was used to assess the loss of health-related productivity. Logistic regression was used to analyze the impact of job burnout on health-related productivity under the control of selected demographic characteristics, socio-economic factors, and occupational factors. Results A total of 3489 questionnaires were recovered, and 3156 valid questionnaires were included in the statistical analysis. Among the 3156 valid questionnaires, 2228 (70.8%) respondents were assessed as suffering from job burnout, in which 1858 (59.0%) were mild to moderate job burnout, and 370 (11.7%) were severe job burnout; the median score (interquartile range) of MBI-GS was 2.18（2.69）, the median rates (interquartile range) of absenteeism and presenteeism were 0.00% (0.00%) and 20.00% (50.00%), respectively. The prevalence of presenteeism significantly varied by gender, education, marital status, working years, job category, exhaustion, cynicism, professional efficacy, and job burnout (P<0.05). The prevalence of absenteeism significantly varied by education, marital status, working years, job category, exhaustion, cynicism, professional efficacy, and job burnout (P<0.05). Job burnout was positively correlated with absenteeism (r=0.157) and presenteeism (r=0.412) (P<0.01). After controlling for selected demographic characteristics, social economic factors, and occupational factors, the logistic regression showed that job burnout was associated with health-related productivity loss, the OR value remained relatively stable, and referring to negative job burnout, the OR (95%CI) of severe job burnout was 6.35 (4.52-8.92). Conclusion Job burnout of enterprise staff has a negative impact on health-related productivity. Severer job burnout associates with higher health-related productivity loss. Enterprises should pay attention to the prevention and control of job burnout to reduce health-related productivity loss.

Objective To explore the relationship between PANoptosis and hepatic ischemia-reperfusion injury (HIRI), and to screen the key genes of PANoptosis in HIRI. Methods PANoptosis-related differentially expressed genes (PDG) were obtained through the Gene Expression Omnibus database and GeneCards database. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) were used to explore the biological pathways related to PDG. A protein-protein interaction network was constructed. Key genes were selected, and their diagnostic value was assessed and validated in the HIRI mice. Immune cell infiltration analysis was performed based on the cell-type identification by estimating relative subsets of RNA transcripts. Results A total of 16 PDG were identified. GO analysis showed that PDG were closely related to cellular metabolism. KEGG analysis indicated that PDG were mainly enriched in cellular death pathways such as apoptosis and immune-related signaling pathways such as the tumor necrosis factor signaling pathway. GSEA results showed that key genes were mainly enriched in immune-related signaling pathways such as the mitogen-activated protein kinase (MAPK) signaling pathway. Two key genes, DFFB and TNFSF10, were identified with high accuracy in diagnosing HIRI, with areas under the curve of 0.964 and 1.000, respectively. Immune infiltration analysis showed that the control group had more infiltration of resting natural killer cells, M2 macrophages, etc., while the HIRI group had more infiltration of M0 macrophages, neutrophils, and naive B cells. Real-time quantitative polymerase chain reaction results showed that compared with the Sham group, the relative expression of DFFB messenger RNA in liver tissue of HIRI group mice increased, and the relative expression of TNFSF10 messenger RNA decreased. Cibersort analysis showed that the infiltration abundance of naive B cells was positively correlated with DFFB expression (r=0.70, P=0.035), and the infiltration abundance of M2 macrophages was positively correlated with TNFSF10 expression (r=0.68, P=0.045). Conclusions PANoptosis-related genes DFFB and TNFSF10 may be potential biomarkers and therapeutic targets for HIRI.

Objective:To establish a set of artificial intelligence (AI) verification rules for blood routine analysis.Methods:Blood routine analysis data of 18 474 hospitalized patients from the First Hospital of Jilin University during August 1st to 31st, 2019, were collected as training group for establishment of the AI verification rules,and the corresponding patient age, microscopic examination results, and clinical diagnosis information were collected. 92 laboratory parameters, including blood analysis report parameters, research parameters and alarm information, were used as candidate conditions for AI audit rules; manual verification combining microscopy was considered as standard, marked whether it was passed or blocked. Using decision tree algorithm, AI audit rules are initially established through high-intensity, multi-round and five-fold cross-validation and AI verification rules were optimized by setting important mandatory cases. The performance of AI verification rules was evaluated by comparing the false negative rate, precision rate, recall rate, F1 score, and pass rate with that of the current autoverification rules using Chi-square test. Another cohort of blood routine analysis data of 12 475 hospitalized patients in the First Hospital of Jilin University during November 1sr to 31st, 2023, were collected as validation group for validation of AI verification rules, which underwent simulated verification via the preliminary AI rules, thus performance of AI rules were analyzed by the above indicators. Results:AI verification rules consist of 15 rules and 17 parameters and do distinguish numeric and morphological abnormalities. Compared with auto-verification rules, the true positive rate, the false positive rate, the true negative rate, the false negative rate, the pass rate, the accuracy, the precision rate, the recall rate and F1 score of AI rules in training group were 22.7%, 1.6%, 74.5%, 1.3%, 75.7%, 97.2%, 93.5%, 94.7%, 94.1, respectively.All of them were better than auto-verification rules, and the difference was statistically significant ( P<0.001), and with no important case missed. In validation group, the true positive rate, the false positive rate, the true negative rate, the false negative rate, the pass rate, the accuracy, the precision rate, the recall rate and F1 score were 19.2%, 8.2%, 70.1%, 2.5%, 72.6%, 89.2%, 70.0%, 88.3%, 78.1, respectively, Compared with the auto-verification rules, The false negative rate was lower, the false positive rate and the recall rate were slightly higher, and the difference was statistically significant ( P<0.001). Conclusion:A set of the AI verification rules are established and verified by using decision tree algorithm of machine learning, which can identify, intercept and prompt abnormal results stably, and is moresimple, highly efficient and more accurate in the report of blood analysis test results compared with auto-vefication.

Background Under the backdrop of the national innovation-driven development strategy, the increasing occupational stress and job burnout among employees are noteworthy for their impact on employees' subjective well-being. Objective To clarify the status, distribution characteristics, and the relationship between subjective well-being, occupational stress, and job burnout of employees in research and development (R&D) enterprises, in order to improve their subjective well-being. Methods A total of 3366 employees from R&D departments at 7 enterprises in Minhang District of Shanghai were selected. The well-being level of the research subjects was investigated by using the World Health Organization Well-Being Index (WHO-5) that yielded total scores from 0 to 25, and a higher total score indicated a higher well-being level; the levels of occupational stress and job burnout were investigated by using the Chinese version of the Job Content Questionnaire, and the Chinese version of the Maslach Burnout Inventory-General Survey (MBI-GS). The scores of WHO-5, JDC, and MBI-GS were incorporated into structural equation modeling (SEM) as numerical variables to analyze their relationship. Results The scores of subjective well-being, occupational stress, and job burnout of employees in the R&D enterprises were 13.30±6.09, 1.12±0.45, and 2.18±1.12, respectively. The positive rates of occupational stress and job burnout were 44.4% and 70.9% respectively, and the positive rate of severe job burnout was 11.7%. There were statistically significant differences in the score of subjective well-being among the participants by gender, age, educational level, marital status, registered residence, working seniority, and jobs (P<0.05); there were statistically significant differences in the positive rate of occupational stress by gender, educational level, marital status, working seniority, and jobs (P<0.05); there were statistically significant differences in the positive rate of job burnout by gender, age, educational level, marital status, registered residence, working seniority, and jobs (P<0.05). There was a negative correlation between subjective well-being and occupational stress (r=−0.1747, P < 0.01), a negative correlation between subjective well-being and job burnout (r=−0.2987, P < 0.01), and a positive correlation between occupational stress and job burnout (r=0.3342, P < 0.01). A structural equation containing partial mediating effect of job burnout on the relationship between occupational stress and subjective well-being was established, and the partial effect accounting for 52.5% of the total effect. Conclusion The job burnout among employees in R&D companies partially mediates the impact of occupational stress on subjective well-being. Reducing the level of job burnout will help alleviate occupational stress and thus improve employees' subjective well-being.

Background Job burnout is an early mental health condition caused by job stress and contributes to many negative effects on work and life. Employees of research and development (R&D) enterprises are exposed to constant pressure from innovation, production speed and sales expansion, and they are prone to burnout symptoms if such factors are not under effective control. Objective To evaluate the current situation of job burnout among employees of R&D enterprises in Minhang District of Shanghai and explore its influencing factors. Methods During November to December 2021, a cross-sectional study was developed and a convenient sampling method was used to enroll employees from 7 R&D enterprises in Minhang District of Shanghai. On the basis of voluntary participation with informed consent, a survey was conducted by using a self-made questionnaire (collecting data about general demographic characteristics, occupational characteristics, behavior and lifestyle), the Chinese version of the Concise Occupational Stress Questionnaire, and the Chinese version of the Maslach Burnout Inventory-General Survey. Occupational stress and its dimensions (job demand, job control, and social support) were divided into high, medium, and low levels according to tertiles. The positive rate of job burnout was reported according to score categorization (<1.5 refers to no job burnout, ≥1.5 refers to job burnout, where ≥1.5 and <3.5 refer to mild and moderate job burnout, and ≥3.5 refers to severe job burnout). Potential influencing factors of job burnout were evaluated by using one-way ANOVA, chi-square test, forward stepwise regression, and non-conditional binary logistic regression (α=0.05, two-sided test). Results A total of 3153 subjects were enrolled and 3014 samples were included in the analysis, with a valid response rate of 95.6%. Among the included subjects, 888 (29.46%) reported no job burnout, 1775 (58.89%) reported mild to moderate job burnout, and 351 (11.64%) reported severe job burnout. The mean of total job burnout score was 2.17±1.12, and the dimentional mean scores were 2.78±1.61 for emotional exhaustion, 1.60±1.60 for cynicism, and 4.05±1.57 for diminished personal accomplishment. Varied categories of sex, age, marital status, working position, sleep status, job demand, job control, and social support groups of workers resulted in significant differences in job burnout score. Compared with the low job demand group, the positive rate of job burnout was elevated in the medium and high job demand groups; the risk of job burnout in the medium job demand group was 1.42 (95%CI: 1.04-1.94) times higher, and that in the high job demand group was 2.64 (95% CI : 2.17-3.22) times higher versus the low job demand group. The risk of job burnout in the medium job control group was 1.35 (95%CI: 1.06-1.72) times higher versus the low job control group. Compared with the low social support group, job burnout was less reported in the other groups, and the OR (95%CI) values of the medium and high social support groups were 0.41 (0.31-0.53) and 0.15 (0.12-0.19) respectively. Conclusion The rate of reporting positive job burnout in R&D enterprises is high, which deserves sufficient attention. Relieving work pressure, increasing job control and social support, and maintaining adequate sleep are helpful to reduce job burnout.