Objective:To analyze the epidemiological characteristics of severe fever with thrombocytopenia syndrome (SFTS) in Beijing in 2024, to investigate the infection status of reservoir hosts, vector organisms, and baseline human populations, and provide a scientific basis for formulating prevention and control strategies.Methods:Epidemiological surveys were conducted on all confirmed cases. Serum samples from healthy populations and reservoir hosts were collected for SFTSV antibody detection. Questing ticks were monitored using the flagging method. Real-time fluorescent quantitative PCR was employed to detect SFTSV in cases, reservoir hosts, and ticks. Positive samples underwent whole-genome sequencing and genetic evolution analysis.Results:In 2024, Beijing reported 15 locally infected cases with 4 deaths. The age of onset ranged from 50 to 80 years (median: 66 years). Cases showed a certain degree of geographical clustering, with June being the peak month of onset. The affected population was predominantly farmers, with a male-to-female ratio of 3∶2. Animal contact history emerged as a significant risk factor alongside tick bites. Parthenogenetic tick populations were identified in Pinggu district, while SFTSV-carrying ticks were detected in endemic areas (Mentougou, Shijingshan, and Fengtai Districts). Viral presence was also confirmed in ticks or dogs from non-endemic areas. Sequencing and phylogenetic analysis revealed stable clustering of strains into two distinct genotypes (A and B). Antibody-positive individuals were identified in healthy populations from non-endemic areas.Conclusions:The incidence of SFTS in Beijing is increasing, with natural viral circulation already established in non-endemic regions. Enhanced surveillance and adjusted prevention strategies are urgently needed.

Objective:To investigate the occurrence of work-related musculoskeletal disorders (WMSDs) among underground coal mine workers, identify the risk factors for WMSDs, and provide a scientific evidence for the prevention and treatment of WMSDs.Methods:In March 2024, through cluster sampling, the on-the-job workers who underwent questionnaire surveys and health examinations at a certain coal mine from July to August 2018 were selected as the research subjects. Basic information of employees, ergonomics-related characteristics, and the occurrence status of WMSDs in each part were collected, and multivariate logistic regression was used for analysis.Results:The incidence rate of WMSDs in at least one site among underground coal mine workers within the past year was 62.22% (219/352). The top three sites in sequence were the lower back (44.32%, 156/352), neck (26.14%, 92/352), and knee (26.14%, 92/352). Multivariate logistic regression analysis showed that frequently exerting great force with arms or hands during work ( OR=2.223, 95% CI: 1.022-4.836), prolonged static forward bending ( OR=1.544, 95% CI: 1.305-1.972), and frequently exerting great effort to operate tools or machines ( OR=2.206, 95% CI: 1.011-4.813), absence of external support systems ( OR=1.589, 95% CI: 1.349-1.996), and repetitive full-body twisting ( OR=1.523, 95% CI: 1.298-1.916) were all risk factors for the occurrence of WMSDs in the lower back ( P<0.05). Both night shift work ( OR=1.564, 95% CI: 1.339-1.939) and frequent forward neck flexion ( OR=1.532, 95% CI: 1.312-1.907) were all risk factors for the occurrence of WMSDs in the neck ( P<0.05). Lifting heavy objects above the shoulder ( OR=1.333, 95% CI: 1.142-1.782), uncomfortable posture and inability to exert force ( OR=1.873, 95% CI: 1.104-2.712), the use of vibration tools ( OR=2.958, 95% CI: 1.255-6.972), and length of service >10 years ( OR=1.525, 95% CI: 1.105-1.967) were all risk factors for the occurrence of WMSDs in the knee ( P<0.05) . Conclusion:The incidence of WMSDs among underground coal miners is relatively high, mainly concentrated in the lower back, neck and knee, and is related to factors such as poor working postures, and work organization. Coal mining enterprises should strengthen work organization, provide appropriate working equipment, and ensure reasonable distribution of workloads.

Objective:To identify risk factors influencing the incidence of hypertension among steelworkers (Homo sapiens) and establish an effective and easily implementable hypertension prediction model.Methods:In September 2023, 2214 steelworkers (Homo sapiens) were selected as study subjects. Basic demographic information, lifestyle, and occupational exposure data were collected, along with physiological measurements such as height, weight, and blood pressure. Multivariate unconditional logistic regression analysis was employed based on relevant literature to determine influencing factors for hypertension among steelworkers (Homo sapiens). Python 3.9 software was used to construct and compare logistic regression, support vector machine (SVM), random forest, extreme gradient boosting tree (XGBoost), and LGBM models. Model performance was evaluated using metrics such as receiver operating characteristic (ROC) curves, accuracy, calibration curves, and F1 scores. The Shapley Additive Explanations (SHAP) model was introduced for feature importance analysis to enhance the interpretability of the prediction model.Results:A total of 432 cases of hypertension were detected among 2214 study subjects, with a detection rate of 19.51%. Age, smoking status, salt intake, use of cooling equipment, carbon monoxide exposure, family history of hypertension, fasting blood glucose, triglycerides, and hemoglobin were identified as independent risk factors for hypertension ( P<0.05). A comparison of the five models revealed the following performance metrics: logistic regression achieved an accuracy of 0.853, F1 score of 0.680, Brier score of 0.108, and AUC of 0.907; SVM demonstrated an accuracy of 0.863, F1 score of 0.687, Brier score of 0.081, and AUC of 0.910; random forest showed an accuracy of 0.857, F1 score of 0.603, Brier score of 0.105, and AUC of 0.861; XGBoost yielded an accuracy of 0.850, F1 score of 0.684, Brier score of 0.117, and AUC of 0.899; and the LGBM model exhibited an accuracy of 0.838, F1 score of 0.625, Brier score of 0.112, and AUC of 0.870. Conclusion:The SVM model demonstrated strong predictive performance, effectively assessing the risk of hypertension among steelworkers (Homo sapiens) and facilitating targeted health management interventions.

Objective:To investigate the occurrence of work-related musculoskeletal disorders (WMSDs) among underground coal mine workers, identify the risk factors for WMSDs, and provide a scientific evidence for the prevention and treatment of WMSDs.Methods:In March 2024, through cluster sampling, the on-the-job workers who underwent questionnaire surveys and health examinations at a certain coal mine from July to August 2018 were selected as the research subjects. Basic information of employees, ergonomics-related characteristics, and the occurrence status of WMSDs in each part were collected, and multivariate logistic regression was used for analysis.Results:The incidence rate of WMSDs in at least one site among underground coal mine workers within the past year was 62.22% (219/352). The top three sites in sequence were the lower back (44.32%, 156/352), neck (26.14%, 92/352), and knee (26.14%, 92/352). Multivariate logistic regression analysis showed that frequently exerting great force with arms or hands during work ( OR=2.223, 95% CI: 1.022-4.836), prolonged static forward bending ( OR=1.544, 95% CI: 1.305-1.972), and frequently exerting great effort to operate tools or machines ( OR=2.206, 95% CI: 1.011-4.813), absence of external support systems ( OR=1.589, 95% CI: 1.349-1.996), and repetitive full-body twisting ( OR=1.523, 95% CI: 1.298-1.916) were all risk factors for the occurrence of WMSDs in the lower back ( P<0.05). Both night shift work ( OR=1.564, 95% CI: 1.339-1.939) and frequent forward neck flexion ( OR=1.532, 95% CI: 1.312-1.907) were all risk factors for the occurrence of WMSDs in the neck ( P<0.05). Lifting heavy objects above the shoulder ( OR=1.333, 95% CI: 1.142-1.782), uncomfortable posture and inability to exert force ( OR=1.873, 95% CI: 1.104-2.712), the use of vibration tools ( OR=2.958, 95% CI: 1.255-6.972), and length of service >10 years ( OR=1.525, 95% CI: 1.105-1.967) were all risk factors for the occurrence of WMSDs in the knee ( P<0.05) . Conclusion:The incidence of WMSDs among underground coal miners is relatively high, mainly concentrated in the lower back, neck and knee, and is related to factors such as poor working postures, and work organization. Coal mining enterprises should strengthen work organization, provide appropriate working equipment, and ensure reasonable distribution of workloads.

Objective:To identify risk factors influencing the incidence of hypertension among steelworkers (Homo sapiens) and establish an effective and easily implementable hypertension prediction model.Methods:In September 2023, 2214 steelworkers (Homo sapiens) were selected as study subjects. Basic demographic information, lifestyle, and occupational exposure data were collected, along with physiological measurements such as height, weight, and blood pressure. Multivariate unconditional logistic regression analysis was employed based on relevant literature to determine influencing factors for hypertension among steelworkers (Homo sapiens). Python 3.9 software was used to construct and compare logistic regression, support vector machine (SVM), random forest, extreme gradient boosting tree (XGBoost), and LGBM models. Model performance was evaluated using metrics such as receiver operating characteristic (ROC) curves, accuracy, calibration curves, and F1 scores. The Shapley Additive Explanations (SHAP) model was introduced for feature importance analysis to enhance the interpretability of the prediction model.Results:A total of 432 cases of hypertension were detected among 2214 study subjects, with a detection rate of 19.51%. Age, smoking status, salt intake, use of cooling equipment, carbon monoxide exposure, family history of hypertension, fasting blood glucose, triglycerides, and hemoglobin were identified as independent risk factors for hypertension ( P<0.05). A comparison of the five models revealed the following performance metrics: logistic regression achieved an accuracy of 0.853, F1 score of 0.680, Brier score of 0.108, and AUC of 0.907; SVM demonstrated an accuracy of 0.863, F1 score of 0.687, Brier score of 0.081, and AUC of 0.910; random forest showed an accuracy of 0.857, F1 score of 0.603, Brier score of 0.105, and AUC of 0.861; XGBoost yielded an accuracy of 0.850, F1 score of 0.684, Brier score of 0.117, and AUC of 0.899; and the LGBM model exhibited an accuracy of 0.838, F1 score of 0.625, Brier score of 0.112, and AUC of 0.870. Conclusion:The SVM model demonstrated strong predictive performance, effectively assessing the risk of hypertension among steelworkers (Homo sapiens) and facilitating targeted health management interventions.

Objective:To analyze the factors influencing the lung function of coal miners, identify the optimal combination of indicators for evaluating lung function, develop a risk assessment model using machine learning, and offer personalized risk assessment for workers.Methods:In June 2023, through cluster sampling, male underground workers who participated in occupational health examinations at a coal mine in North China from July to August 2018 were selected as the research subjects. Their health examination results and occupational environmental data were collected. A total of 3, 320 coal miners were included. Randomly divide the research subjects into a training set (2324 people) and a validation set (996 people) in a ratio of 7∶3, and the balance of the two sets was tested. Perform LASSO regression analysis using R 4.2.2 software to select relevant important variables, and determine the model's input variables by combining them with relevant literature. Utilize Python 3.8 to construct logistic regression, random forest, support vector machine, and XG Boost models, assess the models' discriminative ability using metrics like accuracy, sensitivity, specificity, F1 score, ROC curve, and AUC, evaluate the models' calibration using Brier score, Log loss score, and calibration curve, and further analyze the clinical performance of the developed models through DCA decision curve analysis.Results:Among the 3 320 coal miners, 856 had abnormal lung function (25.78%). The XG Boost model was identified as the optimal model, achieving a training set accuracy of 87.39%, sensitivity of 86.60%, specificity of 87.67%, F1 score of 0.779, AUC of 0.945, Brier score of 0.071, Log loss of 0.267 and demonstrated good calibration curve consistency.Conclusion:The XG Boost model exhibits superior predictive performance compared to other models, and the model has high application value. The Shapley Additive Explanation (SHAP) method is employed for interpretation, making it a reliable basis for preventing abnormal lung function in coal miners.

Objective:To analyze the epidemiological characteristics of severe fever with thrombocytopenia syndrome (SFTS) in Beijing in 2024, to investigate the infection status of reservoir hosts, vector organisms, and baseline human populations, and provide a scientific basis for formulating prevention and control strategies.Methods:Epidemiological surveys were conducted on all confirmed cases. Serum samples from healthy populations and reservoir hosts were collected for SFTSV antibody detection. Questing ticks were monitored using the flagging method. Real-time fluorescent quantitative PCR was employed to detect SFTSV in cases, reservoir hosts, and ticks. Positive samples underwent whole-genome sequencing and genetic evolution analysis.Results:In 2024, Beijing reported 15 locally infected cases with 4 deaths. The age of onset ranged from 50 to 80 years (median: 66 years). Cases showed a certain degree of geographical clustering, with June being the peak month of onset. The affected population was predominantly farmers, with a male-to-female ratio of 3∶2. Animal contact history emerged as a significant risk factor alongside tick bites. Parthenogenetic tick populations were identified in Pinggu district, while SFTSV-carrying ticks were detected in endemic areas (Mentougou, Shijingshan, and Fengtai Districts). Viral presence was also confirmed in ticks or dogs from non-endemic areas. Sequencing and phylogenetic analysis revealed stable clustering of strains into two distinct genotypes (A and B). Antibody-positive individuals were identified in healthy populations from non-endemic areas.Conclusions:The incidence of SFTS in Beijing is increasing, with natural viral circulation already established in non-endemic regions. Enhanced surveillance and adjusted prevention strategies are urgently needed.

Objective:To analyze the factors influencing the lung function of coal miners, identify the optimal combination of indicators for evaluating lung function, develop a risk assessment model using machine learning, and offer personalized risk assessment for workers.Methods:In June 2023, through cluster sampling, male underground workers who participated in occupational health examinations at a coal mine in North China from July to August 2018 were selected as the research subjects. Their health examination results and occupational environmental data were collected. A total of 3, 320 coal miners were included. Randomly divide the research subjects into a training set (2324 people) and a validation set (996 people) in a ratio of 7∶3, and the balance of the two sets was tested. Perform LASSO regression analysis using R 4.2.2 software to select relevant important variables, and determine the model's input variables by combining them with relevant literature. Utilize Python 3.8 to construct logistic regression, random forest, support vector machine, and XG Boost models, assess the models' discriminative ability using metrics like accuracy, sensitivity, specificity, F1 score, ROC curve, and AUC, evaluate the models' calibration using Brier score, Log loss score, and calibration curve, and further analyze the clinical performance of the developed models through DCA decision curve analysis.Results:Among the 3 320 coal miners, 856 had abnormal lung function (25.78%). The XG Boost model was identified as the optimal model, achieving a training set accuracy of 87.39%, sensitivity of 86.60%, specificity of 87.67%, F1 score of 0.779, AUC of 0.945, Brier score of 0.071, Log loss of 0.267 and demonstrated good calibration curve consistency.Conclusion:The XG Boost model exhibits superior predictive performance compared to other models, and the model has high application value. The Shapley Additive Explanation (SHAP) method is employed for interpretation, making it a reliable basis for preventing abnormal lung function in coal miners.

[Objective]To investigate the association between screen content and the frequency of screen exposure at the age of one and a half years and hyperactive behavior in preschool,and to explore how the association is affected by the interaction between outdoor activities and screen behaviors,which could provide theoretical basis and feasible solutions for the prevention and intervention of behavioral problems in childhoood.[Methods]The survey was conducted from June 2022 to June 2023 in Huicheng District,Huizhou (China) stratified by whole cluster sampling methods. Parents and teachers of 5648 children in 61 kindergartens were sampled for questionnaire surveys. The Conners Teacher Rating Scale (TRS) was used to investigate hyperactive behavior. A self-administered questionnaire was used to investigate basic demographic information of children,screen content,frequency of screen exposure and outdoor activities at the age of one and a half years. Multivariate logistic regression was used to explore the association between video screen behavior and hyperactive behavior and its interaction with outdoor activities by controlling for covariates such as children's age,gender,and parental education.[Results]Result showed the overall prevalence of 3.2％ for hyperactive behavior,2.1％ for conduct problems,2.1％ for hyperactivity problems,1.3％ for inattention-passivity problems,and 0.9％ for hyperactivity index. After adjusting for confounding factors,multiple logistic regression analysis showed that screen exposure of "two to four times a week" at one and a half years old was associated with an increased detection rate of hyperactive behaviors in preschool children,with an estimated ORs (95％ CI) of 1.682 (1.141,2.480). Daily screen exposure was associated with increased detection rates of hyperactive behavior,conduct problems,hyperactivity issues,inattention-passivity problems,and hyperactivity index in pre-school age. The estimated ORs (95％ CI) were 2.136 (1.218,3.746),2.321 (1.185,4.546),2.300 (1.208,4.380),2.776 (1.267,6.085) and 3.640 (1.525,8.687),respectively. But the above associations were not found in children who were engaged in daily outdoor activities at the age of one and a half years (P value for interaction<0.001). No association was found between screen content and hyperactive behavior (P>0.05).[Conclusions]Frequency of screen exposure in early childhood is significantly associated with hyperactive behavior problems in preschool,and outdoor activities could weaken the correlation between high-frequency screen exposure and hyperactive behavior,suggesting that parents and schools should prioritize scientifically guiding children's video viewing behavior and outdoor activities,ensuring a well-arranged daily life,to lay a good foundation for the healthy development of children's behavior.

Objective:To explore potential predictors of refractory Mycoplasma pneumoniae pneumonia (RMPP) in early stage. Methods:The prospective multicenter study was conducted in Zhejiang, China from May 1 st, 2019 to January 31 st, 2020. A total of 1 428 patients with fever >48 hours to <120 hours were studied. Their clinical data and oral pharyngeal swab samples were collected; Mycoplasma pneumoniae DNA in pharyngeal swab specimens was detected. Patients with positive Mycoplasma pneumoniae DNA results underwent a series of tests, including chest X-ray, complete blood count, C-reactive protein, lactate dehydrogenase (LDH), and procalcitonin. According to the occurrence of RMPP, the patients were divided into two groups, RMPP group and general Mycoplasma pneumoniae pneumonia (GMPP) group. Measurement data between the 2 groups were compared using Mann-Whitney U test. Logistic regression analyses were used to examine the associations between clinical data and RMPP. Receiver operating characteristic (ROC) curves were used to analyse the power of the markers for predicting RMPP. Results:A total of 1 428 patients finished the study, with 801 boys and 627 girls, aged 4.3 (2.7, 6.3) years. Mycoplasma pneumoniae DNA was positive in 534 cases (37.4%), of whom 446 cases (83.5%) were diagnosed with Mycoplasma pneumoniae pneumonia, including 251 boys and 195 girls, aged 5.2 (3.3, 6.9) years. Macrolides-resistant variation was positive in 410 cases (91.9%). Fifty-five cases were with RMPP, 391 cases with GMPP. The peak body temperature before the first visit and LDH levels in RMPP patients were higher than that in GMPP patients (39.6 (39.1, 40.0) vs. 39.2 (38.9, 39.7) ℃, 333 (279, 392) vs. 311 (259, 359) U/L, both P<0.05). Logistic regression showed the prediction probability π=exp (-29.7+0.667×Peak body temperature (℃)+0.004×LDH (U/L))/(1+exp (-29.7+0.667×Peak body temperature (℃)+0.004 × LDH (U/L))), the cut-off value to predict RMPP was 0.12, with a consensus of probability forecast of 0.89, sensitivity of 0.89, and specificity of 0.67; and the area under ROC curve was 0.682 (95% CI 0.593-0.771, P<0.01). Conclusion:In MPP patients with fever over 48 to <120 hours, a prediction probability π of RMPP can be calculated based on the peak body temperature and LDH level before the first visit, which can facilitate early identification of RMPP.