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.

The phenotype of peri-implant soft tissue is crucial to the long-term treatment outcome of implant restoration, but soft tissue phenotype deficiencies are common in clinical practice and need to be improved through soft tissue augmentation. Although autologous soft tissue graft is still the gold standard for soft tissue augmentation, it has limitations such as limited donor area, increased trauma, and low patient acceptance. Plasmatrix has sufficient sources, is easy to prepare, and has a scaffold structure, growth factors, and cell components that support soft tissue growth. It can be used to improve the phenotype of peri-implant soft tissue. However, there are currently limited clinical studies on the application of plasmatrix in improving the phenotype of peri-implant soft tissue, and there is a lack of consensus conclusions, which makes the majority of clinicians feel confused when using plasmatrix. This article will try to combine existing clinical studies and the clinical experience of the author′s team to explain the classification of peri-implant soft tissue phenotypes and the application of plasmatrix in improving soft tissue phenotypes, in order to provide a reference for related clinical treatments.

Off-label drug use in psychiatry has long been prevelent, with common clinical practices lacking standardized guidance and associated risks warranting close attention. To address the practical needs of frontline clinicians, the Mental Health Branch of the China National Narcotic Drugs Association, in collaboration with multiple institutions, has launched a project to formulate the Guidelines on Off-label Use of Common Psychiatric Medications in China ("Guideline"). The Guidelines focuses on evaluating evidence and formulating recommendations regarding off-label use scenarios of common psychiatric medications, including indications, dosage, administration, and specific populations. It employs standard methodological frameworks, notably the GRADE-ADOLOPMENT approach (Grading of Recommendations Assessment, Development, and Evaluation Evidence to Decision frameworks for adoption, adaptation, and de?novo development of recommendations), to ensure both scientific rigor and practical applicability. This proposal systematically outlines the background, objectives, processes, organizational structure, and methodologies of the Guideline, aiming to provide psychiatrists with practical and evidence-based prescribing recommendations.

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.

The phenotype of peri-implant soft tissue is crucial to the long-term treatment outcome of implant restoration, but soft tissue phenotype deficiencies are common in clinical practice and need to be improved through soft tissue augmentation. Although autologous soft tissue graft is still the gold standard for soft tissue augmentation, it has limitations such as limited donor area, increased trauma, and low patient acceptance. Plasmatrix has sufficient sources, is easy to prepare, and has a scaffold structure, growth factors, and cell components that support soft tissue growth. It can be used to improve the phenotype of peri-implant soft tissue. However, there are currently limited clinical studies on the application of plasmatrix in improving the phenotype of peri-implant soft tissue, and there is a lack of consensus conclusions, which makes the majority of clinicians feel confused when using plasmatrix. This article will try to combine existing clinical studies and the clinical experience of the author′s team to explain the classification of peri-implant soft tissue phenotypes and the application of plasmatrix in improving soft tissue phenotypes, in order to provide a reference for related clinical treatments.

With the rapid development of implant dentistry,increasing attention has been paid to the long-term stability and aesthetic outcomes of dental implants,among which sufficient volume and quality of soft and hard tissues are considered crucial contributing factors for successful treatment outcomes.Among the various available tissue regeneration strategies,plasmatrix,an autologous biomaterial derived from the patient's own peripheral blood,has demonstrated unique and significant clinical value in the re-generation and augmentation of both soft and hard tissues associated with dental implant therapy in recent years.This notable potential is primarily attributed to its rich content of multiple growth factors,viable cells,and a supportive fibrin scaffold,along with its excellent biocompatibility,tunable biodegradation profile,and a relatively simple and rapid preparation process that does not require complex laboratory equipment.As a result,its clinical applications have been continuously expanding across a wide range of indications.Based on a comprehensive review of the existing literature and current research evidence,this article provides an in-depth summary of the advancements in both basic science and clinical applica-tions of plasmatrix in the context of implant dentistry.Particular attention is given to its classification from a materials science perspective,underlying molecular mechanisms,biological effects in promoting tissue regeneration,and its implementation under different clinical scenarios.Furthermore,the article discusses unresolved technical challenges and existing controversies,and outlines potential future directions for re-search and technological innovation,aiming to provide robust evidence-based guidance for clinical prac-tice as well as a theoretical and methodological reference for future scientific investigations.

Off-label drug use in psychiatry has long been prevelent, with common clinical practices lacking standardized guidance and associated risks warranting close attention. To address the practical needs of frontline clinicians, the Mental Health Branch of the China National Narcotic Drugs Association, in collaboration with multiple institutions, has launched a project to formulate the Guidelines on Off-label Use of Common Psychiatric Medications in China ("Guideline"). The Guidelines focuses on evaluating evidence and formulating recommendations regarding off-label use scenarios of common psychiatric medications, including indications, dosage, administration, and specific populations. It employs standard methodological frameworks, notably the GRADE-ADOLOPMENT approach (Grading of Recommendations Assessment, Development, and Evaluation Evidence to Decision frameworks for adoption, adaptation, and de?novo development of recommendations), to ensure both scientific rigor and practical applicability. This proposal systematically outlines the background, objectives, processes, organizational structure, and methodologies of the Guideline, aiming to provide psychiatrists with practical and evidence-based prescribing recommendations.

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.