Background The positive rate of work-related musculoskeletal disorders (WMSDs) among coal mine workers remains high, which seriously affects the quality of life of the workers. Objective To estimate the prevalence of WMSDs among coal miners in Shanxi Province and analyze their influencing factors. Methods From May to December 2023, 2315 coal miners from a coal mine enterprise inShanxi Province were selected by convenience sampling, and the self-administered Basic Situation Survey Scale, Musculoskeletal Disorders Questionnaire, Effort-Reward Imbalance Questionnaire, and Pittsburgh Sleep Quality Scale were used to evaluate general demographic characteristics, work organization characteristics, living habits, sleep quality, occupational stress, and occurrence of WMSDs symptoms and the corresponding absences (sickness absence) in past 1 year. The positive rate and absenteeism rate of WMSDs were caculated, and Pearson's chi-square test was used for identify influencing factors of WMSDs, and finally a binary logistic regression method was used to further studying the factors. Results A total of 2577 questionnaires were distributed in this survey, and 2315 valid questionnaires were recovered, with an effective recovery rate of 89.9%. The positive rate of WMSDs symptoms was 48.7% (1127/2315), the absenteeism rate due to WMSDs was 22.6% (523/2315), and the waist (17.5%), neck (11.7%), and knee (9.0%) were the most common areas presenting WMSDs symptoms. The results of logistic regression showed that compared with ≤10 years of service, the risk of WMSDs was higher for those with > 20 years of service (OR=2.167, 95%CI: 1.419, 3.311). Compared with the daily working time of ≤8 h, the risk of WMSDs was higher for those >8 h daily working time (OR=1.463, 95%CI: 1.211, 1.767). A higher risk of WMSDs was reported in workers with moderate labor intensity (OR=1.247, 95%CI: 1.009, 1.542) or heavy labor intensity (OR=1.570, 95%CI: 1.215, 2.027) than those with light labor intensity. The higher the education level (OR _{high school and technical secondary school}=1.866, 95%CI: 1.435, 2.425; OR _{junior college/bachelor degree or above}=1.953, 95%CI: 1.445, 2.639), the higher the risk of WMSDs. The risk of WMSDs was higher in workers reporting smoking (OR=1.225, 95%CI: 1.021, 1.470) or alcohol consumption (OR=1.345, 95%CI: 1.122, 1.612). The risk of WMSDs in coal miners with high occupational stress (OR=1.229, 95%CI:1.030, 1.466) or those with sleep disorders (OR=3.616, 95%CI:2.824, 4.629) was higher than that in workers with low occupational stress or no sleep disorders respectively. Conclusion The positive rate of WMSDs among coal mine personnel in Shanxi Province is high, and education level, length of service, daily working hours, smoking, alcohol consumption, labor intensity, sleep disorders, and occupational stress are influencing factors for WMSDs.

Background Unhealthy lifestyle behaviors may be associated with an increased risk of cardiometabolic risk factor aggregation (CMRF≥ 2), and few studies have focused on the correlation between the two in occupational populations. Objective To investigate the current status of CMRF≥2 and the compliance of healthy lifestyle in male occupational personnel, explore the effect of lifestyle on cardiometabolic risk, and provide reference for formulating healthy behavior promotion strategies and reducing cardiometabolic risk in occupational populations. Methods The study subjects were selected from male workers who completed occupational health examinations at an occupational disease prevention and control hospital in Shanxi Province from May to December 2023, and 15125 study subjects aged 18−60 years were finally included according to pre-determined inclusion and exclusion criteria. All subjects received a series of assessments including questionnaires (basic information, lifestyle habits, and occupational factors), physical examination, laboratory tests, and six cardiometabolic risk factors (central obesity, hypertension, type 2 diabetes, hypertriglyceridemia, low-density lipoprotein cholesterolemia, and hyperuricemia). A healthy lifestyle score was calculated based on six behavioral factors (smoking, drinking, physical activity, diet, sleep, sedentary behavior) and 1 point for one positive healthy behavior. The enrolled workers were then divided into three groups according to their total scores: 0−1 points (poor group), 2−3 points (moderate group), and 4−6 points (good group). Logistic regression models were used to analyze the statistical associations between lifestyle and CMRF≥2. Results The median age of the 15125 male study participants was 40 years, and the positive rate of CMRF≥2 was 53.5%. Adherence to moderate alcohol consumption was the most compliant healthy behavior (79.4%), followed by current non-smoking (41.7%), while adherence to adequate sedentary behavior (23.6%) and healthy eating (21.1%) were relatively low, and only 17.4% were able to adhere to four or more healthy lifestyle behaviors. After adjusting for confounders, when compared with the poor group high lifestyle score was found to be a protective factor for CMRF≥2 (moderate group: OR=0.70, 95%CI: 0.63, 0.77; good group: OR=0.66, 95%CI: 0.58, 0.75). In stratified analyses across different job types (coal miners, auxiliary workers, ground workers, and others), occupational stress (with or without), shift patterns (day shift, night shift, or rotating shift), dust exposure (with or without), and noise exposure (with or without), individuals with higher lifestyle scores exhibited a consistently lower risk of CMRF≥2. There was an interaction between shift pattern and lifestyle on CMRF≥2 (P_interaction<0.05). Conclusion This occupational group has a high positive rate of CMRF≥2 and a high prevalence of poor lifestyles with low adherence to some healthy behaviors. High healthy lifestyle scores are associated with a lower risk of aggregation of cardiometabolic risk factors, which may be reduced through lifestyle interventions.

Background The prevalence of hyperuricemia (HUA) among Chinese residents has been increasing annually, with occupational populations facing a higher risk of HUA due to shift work or obesity. Objective To investigate the impact of shift work and obesity on HUA among coal miners, and to provide scientific data for the prevention of HUA in this occupational group. Methods A cross-sectional study was conducted with 25619 workers from a coal mining group in 2023 as the study population, using a questionnaire survey and occupational health check-ups. Workers who were retired or on medical leave, as well as those who provided incomplete responses to the questionnaire or did not complete anthropometric measurements or blood tests, were excluded. A total of 23173 participants were included in the study. An unconditional logistic regression model was used to analyze the association between HUA and selected factors such as shift work status, years of shift work, daily shift work hours, body mass index (BMI), waist-to-height ratio (WHtR), and visceral adiposity index (VAI), as well as the interaction between obesity and shift work on HUA. Restricted cubic spline functions were used to analyze the dose-response relationship between years of shift work, daily shift work hours, and HUA. Results The average age of the included workers was (42.38 ± 9.82) years. A total of 6735 workers reported positive HUA, with a positive rate of 29.1%, and the prevalence was higher in men compared to women (31.8% vs. 13.9%). After adjusting for confounding factors, the restricted cubic spline model revealed a nonlinear dose-response relationship between years of shift work, daily shift work duration, and HUA: ① When the years of shift work exceeded 3.25 years, the risk of HUA in workers gradually increased with longer work duration. At 20 years of shift work, the risk increase showed a "saturation effect," meaning that the risk of HUA no longer increased with additional years of shift work. ② When daily shift work duration was below 10 h, the risk of HUA increased with the length of shift work, but after exceeding 10 h, the risk stabilized. After adjusting for confounding factors, the logistic regression model showed that, compared to non-shift workers, shift workers, those with 10-20 years and over 20 years of shift work experience, as well as those working less than 8 h and more than 8 h per day, had a higher risk of HUA, with odds ratios (OR) of 1.14 (95%CI: 1.06, 1.22), 2.42 (95%CI: 2.20, 2.67), 2.23 (95%CI: 2.00, 2.48), 1.41 (95%CI: 1.29, 1.54), and 1.90 (95%CI: 1.75, 2.06), respectively. Additionally, compared to individuals with normal obesity indices, those with overweight and obesity based on BMI, high and very high VAI and excessive WHtR had a higher risk of HUA, with ORs of 2.01 (95%CI: 1.85, 2.17), 3.51 (95%CI: 3.20, 3.84), 1.42 (95%CI: 1.32, 1.54), 2.10 (95%CI: 1.91, 2.30), and 1.82 (95%CI: 1.69, 1.97), respectively. The interaction analysis showed no interaction between shift work and obesity index (both P_s < 0.001). Conclusion Shift work, longer shift work years, and longer daily shift work hours increase the risk of HUA among coal miners. Additionally, the role of obesity in the development of HUA cannot be ignored.

Background Thyroid hormones are crucial for development and proper functioning of human physiological systems. Current research on the thyroid mainly focuses on the impacts of lifestyle factors on thyroid dysfunction, while less attention is paid to the factors affecting thyroid hormone levels, especially occupational hazards, which warrants further investigation. Objective To investigate the associations between occupational hazard exposure and thyroid-stimulating hormone (TSH) and thyroid hormone levels in male coal mine workers. Methods A cross-sectional study design was adopted. A total of 12564 workers who participated in the occupational health check-ups at the Xishan Coal Electricity (Group) Corporation Occupational Disease Prevention and Control Institute in 2023 and met the inclusion and exclusion criteria were selected as research subjects. A self-designed electronic questionnaire was used to collect basic information, occupational history, and lifestyle habits of all study subjects. Height, weight, thyroid function test results, and occupational hazard exposure of the study subjects were obtained through occupational health examinations and routine workplace occupational hazard detection records. Generalized linear regression was used to analyze the associations between occupational hazard exposure and the levels of TSH and thyroid hormones. Results The median (P₂₅, P₇₅) levels of triiodothyronine (T3), thyroxine (T4), free triiodothyronine (FT3), free thyroxine (FT4), and TSH in the included 12564 male coal mine workers were 1.16 (1.03, 1.29) ng·mL⁻¹, 7.70 (6.70, 8.90) μg·dL⁻¹, 3.63 (3.40, 3.84) pg·mL⁻¹, 1.19 (1.08, 1.30) ng·dL⁻¹, and 1.93 (1.36, 2.78) μIU·mL⁻¹, respectively. The overall abnormality rate of thyroid hormones was 2.83%, with the highest rate for subclinical hypothyroidism (1.83%), followed by hyperthyroidism (0.37%), hypothyroidism (0.32%), and subclinical hyperthyroidism (0.31%). After adjustment for confounding factors, the generalized linear regression model showed that long working hours were associated with higher levels of T3 and FT3, with β (95%CI) values of 0.011 (0.003, 0.019) and 0.038 (0.019, 0.057) respectively. Night shift work was linked to increased TSH levels and decreased FT4 levels, with β (95%CI) values of 0.171 (0.016, 0.326) and −0.012 (−0.019, −0.006) respectively. Coal dust exposure was associated with decreased levels of T3 and T4, with β (95%CI) values of −0.022 (−0.037, −0.008) and −0.320 (−0.434, −0.207) respectively. Noise exposure was related to decreased levels of T4 and FT4, with β (95%CI) values of −0.102 (−0.166, −0.038) and −0.020 (−0.027, −0.013) respectively. Conclusion The TSH and thyroid hormone levels of coal miners are mostly within the normal reference ranges, with a low abnormality rate. Long work hours, night shift work, coal dust, and noise are associated with the levels of TSH and thyroid hormone levels in the male miners. Coal companies should reasonably arrange working hours, optimize the night shift scheduling system, and enhance protection against coal dust and noise to promote occupational health and safeguard the physical health of miners.

Objective To analyze the structural and phylogenetic characteristics of the mitochondrial genome from Bulinus globosus, so as to provide a theoretical basis for classification and identification of species within the Bulinus genus, and to provide insights into understanding of Bulinus-schistosomes interactions and the mechanisms of parasite transmission. Methods B. globosus samples were collected from the Ruya River basin in Zimbabwe. Mitochondrial DNA was extracted from B. globosus samples and the corresponding libraries were constructed for high-throughput sequencing on the Illumina NovaSeq 6000 platform. After raw sequencing data were subjected to quality control using the fastp software, genome assembly was performed using the A5-miseq and SPAdes tools, and genome annotation was conducted using the MITOS online server. Circular maps and sequence plots of the mitochondrial genome were generated using the CGView and OGDRAW software, and the protein conservation motifs and structures were analyzed using the TBtools software. Base composition and codon usage bias were analyzed and visualized using the software MEGA X and the ggplot2 package in the R software. In addition, a phylogenetic tree was created in the software MEGA X after sequence alignment with the software MAFFT 7, and visualized using the software iTOL. Results The mitochondrial genome of B. globosus was a 13 730 bp double-stranded circular molecule, containing 2 ribosomal RNA (rRNA) genes, 22 transfer RNA (tRNA) genes, and 13 protein-coding genes, with a marked AT preference. The mitochondrial genome composition of B. globosus was similar to that of other species within the Bulinus genus. Phylogenetic analysis revealed that the complete mitochondrial genome sequence of B. globosus was clustered with B. truncatus, B. nasutus, and B. ugandae into the same evolutionary clade, and gene superfamily analysis showed that the metabolism-related proteins of B. globosus were highly conserved, notably the cytochrome c oxidase family, which showed a significant consistency. Conclusions This is the first whole mitochondrial genome sequencing to decode the compositional features of the mitochondrial genome of B. globosus from Zimbabwe and its evolutionary relationship within the Bulinus genus, which provides important insights for further understanding of the phylogeny and mitochondrial genome characteristics of the Bulinus genus.

Background In recent years, the detection rate of thyroid nodules in China's occupational population has shown an upward trend. The prevalence of this disease needs to be taken seriously and targeted measures should be taken to address its influencing factors. Objective To analyze the detection and influencing factors of thyroid nodules among adult male workers in coal mining enterprises in Shanxi Province, and provide a theoretical basis for the prevention of thyroid nodules. Methods A total of 5195 adult male workers from coal mining enterprises who ordered physical examination provided by the Xishan Coal Electricity (Group) Corporation Occupational Disease Prevention and Control Institute in Taiyuan City, Shanxi Province from May 2023 to December 2023 were selected as research subjects. A questionnaire survey, physical examination, ultrasound examination, and biological sample collection and testing were conducted. Thyroid nodules were diagnosed based on ultrasound results, classified and evaluated according to the Chinese-Thyroid Imaging Reporting and Data System (C-TIRADS), and the crude detection rate and standardized detection rate of thyroid nodules in adult male coal mine workers in Shanxi Province wereestimated. Occupational history and occupational exposure information were retrieved from their annual occupational health examination reports provided by the employer. Logistic regression modeling was used to analyze the influencing factors of thyroid nodule. Results A total of 1688 cases of thyroid nodules were detected, with a crude detection rate of 32.49% and an age-standardized detection rate of 31.17%. The cases (constitution ratio) of thyroid nodules by category in the detected cases was as follows: 358 cases (21.21%) were classified as category 2, 1261 cases (74.70%) as category 3, 67 cases (3.97%) as category 4a, and 2 cases (0.12%) as category 4b. The results of logistic regression showed that the duration of exposure to occupational hazards for 11-20 years, 21-30 years, ≥31 years, average monthly working>26 days, and shift work were positively correlated with the detection of thyroid nodules (P<0.05), and the associated ORs (95%CIs) were 1.29 (1.01, 1.65), 1.55 (1.20, 1.99), 1.68 (1.26, 2.55), 1.19 (1.00, 1.40), and 1.15 (1.02, 1.29), respectively. Conclusion The duration of exposure to occupational hazards, average monthly working days, and shift work are influencing factors of thyroid nodules. Intervention measures should be developed based on these influencing factors to prevent the occurrence of thyroid nodules.

Objective:To investigate the epidemic characteristics of severe fever with thrombocytopenia syndrome (SFTS) in Tiantai County Zhejiang Province, and provide a scientific basis for prevention and control of SFTS.Methods:The case data of SFTS in Tiantai County from 2014 to 2024 were collected through the Chinese Disease Control and Prevention Information System, and descriptive epidemic method was employed to analyze its epidemic and clinical characteristics.Results:From 2014 to 2024, a total of 114 SFTS cases were reported in Tiantai County, with an average annual incidence rate of 2.21/100 000. The peak incidence occurred in 2021 (3.40/100 000, 16 cases). Nine cases died, with a mortality rate of 7.89% (9/114). The male-to-female ratio was 1.00∶0.97 (58∶56). The high-risk population was middle-aged and elderly people aged over 50 years old, accounting for 91.23% (104/114). The occupation was mainly farmers, accounting for 92.98% (106/114). The incidence of SFTS had obvious seasonality, with a peak from April to September, accounting for 83.33% (95/114). The average onset date was calculated to be June 25th, and the peak period was from April 19th to August 31st. The distribution of cases was mainly in Tantou Town (34 cases), Shiliang Town (23 cases), Yongxi Township (17 cases), and Pingqiao Town (12 cases), accounting for 75.44% (86/114). From 2014 to 2024, the affected areas had gradually expanded from 1 township/subdistrict to 13 townships/subdistricts, showed a phenomenon of migration from east to west. Among the 114 patients with SFTS, the initial symptom was fever. The proportion of neuropsychiatric symptoms, atrial fibrillation and heart failure in the death group was significantly higher than those in the survival group (χ 2 = 19.24, 16.44, 23.90, P < 0.001). Conclusion:From 2014 to 2024, the incidence of SFTS in Tiantai County fluctuates with obvious seasonal and regional migration characteristics.

Objective:To develop and validate machine learning models for predicting the cumulative live birth rate (CLBR) following in vitro fertilization (IVF) and to analyze key predictive features using SHAP values. Methods:This retrospective study included data from patients who underwent IVF-embryo transfer at the Department of Reproductive Medicine, Baoding Maternal and Child Health Hospital, between January 2017 and December 2022. Patients were categorized into two groups based on live birth outcome: the live birth group ( n=1 036) and the non-live birth group ( n=756). The dataset was randomly divided into a training set and a validation set in a ratio of 7∶3. Five algorithms were utilized for model development: logistic regression, random forest, extreme gradient boosting (XGBoost), support vector machine, and neural networks. Model performance was assessed using the area under the receiver operating characteristic (AUC) curve, F1 score, and calibration curves. Clinical decision curve analysis (DCA) was employed to evaluate the clinical utility of the models. SHAP values were used to interpret feature importance in the XGBoost model and enhance its explainability. Results:The XGBoost model demonstrated the best performance in predicting CLBR,with accuracy of 72.44%, AUC of 0.775, and F1 score of 0.654, accuracy and F1 score outperforming logistic regression (accuracy was 70.02%, F1 score was 0.585), random forest (accuracy was 71.69%, F1 score was 0.606), support vector machine (accuracy was 70.20%, F1 score was 0.607), and neural network (accuracy was 68.72%, F1 score was 0.560). The calibration curve of XGBoost closely aligned with the diagonal line, indicating that the predicted probabilities were very close to the actual outcomes, demonstrating good calibration. DCA indicated that the XGBoost model provided higher net benefits across a wide range of clinical decision thresholds. SHAP value analysis identified number of previous IVF failures, antral follicle count, anti-Müllerian hormone level, percentage of normal sperm morphology, and sperm DNA fragmentation index as key predictors of CLBR.Conclusion:The XGBoost model exhibits excellent predictive performance and calibration for CLBR, with SHAP values providing important insights into feature importance. This model has the potential to support the development of personalized treatment strategies in clinical practice. However, its generalizability needs to be validated using external datasets to ensure its applicability to diverse populations.

Objective:To develop and validate machine learning models for predicting the cumulative live birth rate (CLBR) following in vitro fertilization (IVF) and to analyze key predictive features using SHAP values. Methods:This retrospective study included data from patients who underwent IVF-embryo transfer at the Department of Reproductive Medicine, Baoding Maternal and Child Health Hospital, between January 2017 and December 2022. Patients were categorized into two groups based on live birth outcome: the live birth group ( n=1 036) and the non-live birth group ( n=756). The dataset was randomly divided into a training set and a validation set in a ratio of 7∶3. Five algorithms were utilized for model development: logistic regression, random forest, extreme gradient boosting (XGBoost), support vector machine, and neural networks. Model performance was assessed using the area under the receiver operating characteristic (AUC) curve, F1 score, and calibration curves. Clinical decision curve analysis (DCA) was employed to evaluate the clinical utility of the models. SHAP values were used to interpret feature importance in the XGBoost model and enhance its explainability. Results:The XGBoost model demonstrated the best performance in predicting CLBR,with accuracy of 72.44%, AUC of 0.775, and F1 score of 0.654, accuracy and F1 score outperforming logistic regression (accuracy was 70.02%, F1 score was 0.585), random forest (accuracy was 71.69%, F1 score was 0.606), support vector machine (accuracy was 70.20%, F1 score was 0.607), and neural network (accuracy was 68.72%, F1 score was 0.560). The calibration curve of XGBoost closely aligned with the diagonal line, indicating that the predicted probabilities were very close to the actual outcomes, demonstrating good calibration. DCA indicated that the XGBoost model provided higher net benefits across a wide range of clinical decision thresholds. SHAP value analysis identified number of previous IVF failures, antral follicle count, anti-Müllerian hormone level, percentage of normal sperm morphology, and sperm DNA fragmentation index as key predictors of CLBR.Conclusion:The XGBoost model exhibits excellent predictive performance and calibration for CLBR, with SHAP values providing important insights into feature importance. This model has the potential to support the development of personalized treatment strategies in clinical practice. However, its generalizability needs to be validated using external datasets to ensure its applicability to diverse populations.

Objective:To investigate the epidemic characteristics of severe fever with thrombocytopenia syndrome (SFTS) in Tiantai County Zhejiang Province, and provide a scientific basis for prevention and control of SFTS.Methods:The case data of SFTS in Tiantai County from 2014 to 2024 were collected through the Chinese Disease Control and Prevention Information System, and descriptive epidemic method was employed to analyze its epidemic and clinical characteristics.Results:From 2014 to 2024, a total of 114 SFTS cases were reported in Tiantai County, with an average annual incidence rate of 2.21/100 000. The peak incidence occurred in 2021 (3.40/100 000, 16 cases). Nine cases died, with a mortality rate of 7.89% (9/114). The male-to-female ratio was 1.00∶0.97 (58∶56). The high-risk population was middle-aged and elderly people aged over 50 years old, accounting for 91.23% (104/114). The occupation was mainly farmers, accounting for 92.98% (106/114). The incidence of SFTS had obvious seasonality, with a peak from April to September, accounting for 83.33% (95/114). The average onset date was calculated to be June 25th, and the peak period was from April 19th to August 31st. The distribution of cases was mainly in Tantou Town (34 cases), Shiliang Town (23 cases), Yongxi Township (17 cases), and Pingqiao Town (12 cases), accounting for 75.44% (86/114). From 2014 to 2024, the affected areas had gradually expanded from 1 township/subdistrict to 13 townships/subdistricts, showed a phenomenon of migration from east to west. Among the 114 patients with SFTS, the initial symptom was fever. The proportion of neuropsychiatric symptoms, atrial fibrillation and heart failure in the death group was significantly higher than those in the survival group (χ 2 = 19.24, 16.44, 23.90, P < 0.001). Conclusion:From 2014 to 2024, the incidence of SFTS in Tiantai County fluctuates with obvious seasonal and regional migration characteristics.