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 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 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 explore the protective effect of sesquiterpene lactones of Eupatorium lindleyanum DC.(SLEL)on lipopolysac-charide(LPS)-induced acute lung injury(ALI)in rats using metabolomics.Methods:Forty-eight male SD rats were randomly divided into a blank control group(CG),a model control group(MG),low-,medium-,and high-dose SLEL groups(50,100,and 200 mg/kg),and a positive control group(dexamethasone acetate tablets,5 mg/kg).CG and MG groups were given phosphate-buffered saline.All groups received intragastric administration at a dose volume of 10 mL/kg once a day for 7 consecutive days.One hour after the last ad-ministration,LPS(5 mg/kg)was instilled into the trachea of all groups except the CG group to establish the ALI rat model.Twenty-four hours after model establishment,blood was collected from the abdominal aorta and bronchoalveolar lavage fluid(BALF)was col-lected from the left lung.The total number of inflammatory cells,neutrophils,lymphocytes,and eosinophils in BALF was counted by Wright-Giemsa staining.The levels of interleukin-18(IL-18)and interferon-γ(IFN-γ)in serum and BALF were measured by enzyme-linked immunosorbent assay.The pathological changes of lung tissue were observed using hematoxylin-eosin staining.The ex-pression levels of tight junction protein-1(ZO-1)and occludin in lung tissue were determined by Western blot.The mRNA expression levels of IL-18,IFN-γ,ZO-1,and occludin in the lung were measured by real-time fluorescence quantitative PCR.Non-targeted me-tabolomics analysis of serum and lung tissue was performed using ultra-performance liquid chromatography-electrospray ionization-tandem mass spectrometry.Results:Compared with the MG group,all SLEL groups had significantly reduced wet/dry weight ratio of lung tissue,lung coefficient,and total number of inflammatory cells,neutrophils,lymphocytes,and eosinophils in BALF.SLEL signifi-cantly decreased the levels of IL-18 and IFN-γ in serum and BALF and the mRNA expression of IL-18 and IFN-γ in lung tissue,and significantly promoted the protein and mRNA expression of ZO-1 and occludin.Under light microscopy,the degree of lung tissue edema,alveolar hemorrhage,and inflammatory cell infiltration were significantly reduced,indicating a certain protective effect on ALI rats.The results of non-targeted metabolomics studies showed that there were 91 and 33 significantly different metabolites in the serum and lung tissue of rats treated with SLEL,respectively.Among them,the main differential metabolites in the serum were sphingosine,L-lactic acid,nicotinic acid,D-nucleotide,and mevalonate-5P,while the main differential metabolites in the lung tissue were tauro-cholic acid.This suggests that SLEL may mainly affect the metabolic pathways of sphingolipids,pyruvate,nicotinic acid,nicotinamide,and tryptophan in the serum and the metabolic pathways of taurine and hypotaurine in the lung tissue to improve ALI.Conclusion:SLEL has a significant protective effect on rats against LPS-induced ALI,and its mechanism of action may be related to the inhibition of inflammatory factors,improvement of lung barrier function,and regulation of related metabolic pathways.

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 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.

Inorganic polyphosphate(polyP)is wide spread in biology and exhibits striking procoagulant ef-fects in vivo.Long-chain polyphosphate(such as those present in infectious microorganisms)is a potent,natural patho-physiologic activator of the contact pathway of blood clotting and promotes thrombosis and inflammation,which suggest that antagonizing polyP function in vivo may be an attractive approach for identifying novel antithrombotic agents,perhaps with reduced bleeding side effects compared with conventional anticoagulant drugs.Short-chain polyphosphate(secreted from activated human platelets)remarkably accelerates factor V activation,absolutely abrogates the anticoagulant function of tissue factor pathway inhibitor,increase fibrin clot structure,and accelerates factor XI activation by thrombin.So polyP may be used as hemostatic agent.In this paper,the procoagulant role of polyP and the mechanism underlying polyP modu-lates blood clotting and the strategy of screening polyP inhibitors are described in detail.

Objective To study the missed diagnosis of colorectal polyps during colonoscopy and its risk factors.Methods Data of 655 patients who underwent repeated co]onoscopy in 3 months (90 days) were analyzed in three endoscopy centers in Shenzhen.Miss rates of polyps and patients were calculated.Logistic regression analysis was used to identify the suspected risk factors associated with the miss rate including gender,age,symptoms of patient and number,shape,location of polyps.Results A total of 459 polyps(20.47％,459/2 242) in 224 patients(34.20％,204/655) were missed in overall 1 783 polyps within 655 patients.The patient miss rate increased with the polyp count increasing from 1 to 4,but with no significant differences.Polyp count of more than 5 was the independent risk factor for patient miss rate during colonoscopy(OR=4.98,P=0.00).Polyps in males were easier to be missed than those in females (OR =1.76,P =0.00).Size less than 5 mm was the independent risk factor for missed diagnosis during colonoscopy(OR=2.94,P=0.00).The flat type(Yamada Ⅰ,Ⅱ) was also the independent risk factor(OR=2.72,P=0.01;OR=3.23,P=0.00 respectively).Conclusion The miss rate of polyps is related to gender,basic polyp count,the size and shape of polyp.Male with multiple polyps and polyps with flat type and small size tend to be missed.

BACKGROUND:Deep vein thrombosis after hip replacement has a high incidence rate. Moreover, deep vein thrombosis can induce pulmonary embolism that can endanger patients’ life and dysfunction of distant deep vein. The appearance of deep vein thrombosis is a great obstacle for the gradual y increased hip replacement.
OBJECTIVE:To observe the occurrence of deep vein thrombosis of lower limb after total hip replacement in elderly patients, and to screen the risk factors for the occurrence of deep vein thrombosis of lower limbs.
METHODS:Clinical data of 128 elderly patients with hip replacement were analyzed retrospectively. Al patients were examined with color Doppler ultrasound in double lower limbs at 1 day before replacement and 7 days after replacement. Multifactor unconditional logistic analysis was conducted on clinical related factors and the formation of lower limb deep vein thrombosis.
RESULTS AND CONCLUSION:At 7 days after the operation, 16 patients affected deep vein thrombosis of lower limb. The factors for deep vein thrombosis contained female, general anesthesia, bilateral hip replacement and the application of bone cement (P<0.05). The risk for deep vein thrombosis after total hip replacement significantly increased in elderly patients aged over 70 years. Multifactor unconditional logistic analysis exhibited that the multiple risks of sex, obesity and the use of bone cement in elderly patients with deep vein thrombosis increased to 11.398, 3.109 and 8.925. The patients with a blood type O at the age of over 70 years experienced a decreased risk for deep vein thrombosis after total hip replacement. The occurrence of deep vein thrombosis decreased to 0.186 times after replacement. Blood type O could be considered as a protective factor for the occurrence of deep vein thrombosis.