Environmental pollution is a significant public health challenge worldwide, and investigating the causal relationship between environmental exposure and population health outcomes is a key objective of environmental epidemiology research. In recent years, the complexity of environmental exposures has increasingly come to the forefront, making it challenging for observational studies that dominate environmental epidemiology to accurately estimate causal effects. Causal inference methods are particularly advantageous in controlling for confounding factors, thus holding great potential in environmental epidemiology research. Researchers can use appropriate causal inference methods to simulate the process of randomization, providing strong support for revealing the causal relationship between environmental exposure and health outcomes. However, there is a lack of reviews on the application of causal inference methods in environmental epidemiology studies in China. Therefore, this study introduced the basic principles of common causal inference statistical methods in environmental epidemiology, summarized the applicable conditions, advantages and disadvantages of various methods, and provided R software implementation codes for these methods, aiming to offer guidance for optimizing research design and practicing causal inference statistical methods.

Objective To evaluate the short-term effects of comprehensive health management interventions for stroke high-risk population screening on the prevention and treatment of ischemic stroke, and to provide reference and basis for improving and exploring health management and prevention strategies for stroke high-risk population. Methods From 2018 to 2022, 13 community health service centers in Jiading District, Shanghai were selected in the present study. Based on information push platform, stroke risk assessment and health intervention follow-up were conducted for community residents through convenience sampling. The residents were divided into a full course intervention group (intervention group) and a routine intervention group (control group) according to different health intervention measures and forms. The incidence of ischemic stroke in the two groups of survey subjects was tracked within 36 months. Results A total of 52144 subjects were included in the study. The total number of patients in the full course intervention group was 14227, with an incidence density of 577.32/100 000 (556.49/100 000-598.12/100 000), which was lower than that of the conventional intervention group (37 917), with an incidence density of 1 485.47/100 000 (1 464.99/100 000-1 505.94/100 000) (χ²=2490.212, P<0.001). The relative risk of the full course intervention group was 0.39, and the relative risk of stroke risk factors in the full course intervention group from low to high was 0.33, 0.43, 0.45, and 0.49, respectively. The incidence density of males in the full course intervention group was 660.76 (627.46/100 000 - 694.05/100 000), with a relative risk of 0.43, and the incidence density of female patients was 509.71/100 000 (483.37/100 000 - 536.05/100 000), with a relative risk of 0.35. The overall incidence density of the population under 62 years old gourp, 62-75 years old group and over 75 years old group was 197.45/100 000 (173.09/100 000 -221.80/100 000), 608.36/100 000 (580.19/100 000-636.54/100 000), and 1 025.06/100 000 (958.51/100 000-1 091.61/100 000), with relative risks of 0.51, 0.44, and 0.38, respectively. Conclusion Comprehensive health management measures can effectively reduce the short-term risk of ischemic stroke, and should be further promoted and improved to enhance the effectiveness of stroke prevention and control.

Background Lead (Pb) is a highly toxic heavy metal that accumulates in the body, potentially leading to multi-systemic impairment. Compared with traditional statistical methods, machine learning techniques offer unique advantages, opening new avenues for occupational health risk assessment and the exploratory analysis of complex associations. Objective To examine the association between occupational lead exposure and multiple health indicators and to identify key risk factors for lead toxicity. Methods A cross-sectional study was conducted, integrating occupational hygiene investigation results from 16 lead-acid battery enterprises in Jiangsu Province with occupational health examination data from 1914 lead-exposed workers. Inter-group differences were analyzed using the χ² test or Fisher's exact test. Binary logistic regression and machine learning algorithms [CatBoost, Naive Bayes model (NBM), and random forest (RF)] were employed to evaluate the association between blood lead (PbB), urine lead (PbU), and health indicators including blood pressure (BP), red blood cell count (RBC), and alanine aminotransferase (ALT). Results The prevalence of abnormal PbB and PbU were 14.52% and 9.35%, respectively. The risks of abnormal BP, RBC, and ALT were significantly increased in the population with high lead levels (P<0.05). PbB abnormalities were closely associated with gender, environmental lead concentration, wearing masks, smoking, and alcohol consumption (P<0.05). Regarding occupational hazards, workers exposed to lead dust had a 1.98-fold risk of PbU abnormality compared to those exposed to lead fumes. The plate coating and acid leaching process posed the highest risk for both PbB (OR=8.81) and PbU (OR=5.46) abnormalities compared with assembly process. Furthermore, the risks of PbB and PbU abnormalities were significantly elevated among workers with abnormal BP, RBC or ALT (P<0.05). Among the models, CatBoost performed best in predicting RBC abnormality (accuracy: 95.8%; precision: 44.9%; F1 score: 0.952; AUC: 0.981). Feature importance analysis identified PbB and PbU as the core factors affecting abnormal RBC and ALT, while RBC and ALT abnormalities as key features for predicting the risk of PbB and PbU abnormalities. Conclusion By integrating traditional statistical methods with machine learning, this study reveals a complex bidirectional association between occupational lead exposure and multiple health indicators, and identifies gender, job category, and environmental Pb concentration as the key factors influencing PbB abnormalities. These findings provide a scientific foundation for the implementation of precision occupational health management models.

Background Ozone (O₃) is a major air pollutant. The existing monitoring system has uneven distribution of sites, insufficient coverage in underdeveloped areas, and low temporal resolution, making it difficult to obtain hourly data. This limits the dynamic identification of pollution and the formulation of prevention and control strategies. Objective To construct an hourly O₃ concentration estimation model based on ensemble machine learning, aiming to improve the accuracy of pollution exposure assessment and explore O₃ health impacts. Methods This study integrated land use regression modeling with modern machine learning techniques, employing random forest and XGBoost algorithms to construct base models, and stacking integration using non-negative least squares. The ensemble model was trained and validated across China using high-resolution, multi-source geographic data (e.g., meteorologicaldata, population density, land cover types, and aerosol optical thickness). It was tested in Taiyuan City, combined with a distributed lag non-linear model to analyze the association between O₃ and emergency admissions. Results The constructed ensemble model performed well in predicting O₃ concentration, with a higher coefficient of determination (R²) and a lower root-mean-square deviation (RMSE) compared to the single models. The R² improved from 0.90 to 0.92, and the RMSE decreased from 11.41 to 10.62, enhancing both prediction accuracy and generalization ability. In the application to Taiyuan City, the model successfully imputed the hourly-level data for the entire year. The distributed lag non-linear model analysis revealed that the relative risk (RR) values for the 6th to 8th days following O₃ exposure were 1.14 (95%CI: 1.01, 1.29), 1.16 (95%CI: 1.02, 1.31), and 1.14 (95%CI: 1.01, 1.29), respectively, which were significantly higher than 1, indicating a significant lagged association (lagged 6-8 d) between O₃ and the number of emergency room visits. Conclusion A high-precision, hourly-level O₃ concentration estimation model is successfully constructed by combining the land use regression model with an ensemble machine learning approach to provide a scientific basis for environmental policy formulation and public health intervention. The application of the model verifies its generalization ability and practical application value, which can provide a new technical framework for subsequent environmental health research.

Background Lead (Pb) is a highly toxic heavy metal that accumulates in the body, potentially leading to multi-systemic impairment. Compared with traditional statistical methods, machine learning techniques offer unique advantages, opening new avenues for occupational health risk assessment and the exploratory analysis of complex associations. Objective To examine the association between occupational lead exposure and multiple health indicators and to identify key risk factors for lead toxicity. Methods A cross-sectional study was conducted, integrating occupational hygiene investigation results from 16 lead-acid battery enterprises in Jiangsu Province with occupational health examination data from 1914 lead-exposed workers. Inter-group differences were analyzed using the χ² test or Fisher's exact test. Binary logistic regression and machine learning algorithms [CatBoost, Naive Bayes model (NBM), and random forest (RF)] were employed to evaluate the association between blood lead (PbB), urine lead (PbU), and health indicators including blood pressure (BP), red blood cell count (RBC), and alanine aminotransferase (ALT). Results The prevalence of abnormal PbB and PbU were 14.52% and 9.35%, respectively. The risks of abnormal BP, RBC, and ALT were significantly increased in the population with high lead levels (P<0.05). PbB abnormalities were closely associated with gender, environmental lead concentration, wearing masks, smoking, and alcohol consumption (P<0.05). Regarding occupational hazards, workers exposed to lead dust had a 1.98-fold risk of PbU abnormality compared to those exposed to lead fumes. The plate coating and acid leaching process posed the highest risk for both PbB (OR=8.81) and PbU (OR=5.46) abnormalities compared with assembly process. Furthermore, the risks of PbB and PbU abnormalities were significantly elevated among workers with abnormal BP, RBC or ALT (P<0.05). Among the models, CatBoost performed best in predicting RBC abnormality (accuracy: 95.8%; precision: 44.9%; F1 score: 0.952; AUC: 0.981). Feature importance analysis identified PbB and PbU as the core factors affecting abnormal RBC and ALT, while RBC and ALT abnormalities as key features for predicting the risk of PbB and PbU abnormalities. Conclusion By integrating traditional statistical methods with machine learning, this study reveals a complex bidirectional association between occupational lead exposure and multiple health indicators, and identifies gender, job category, and environmental Pb concentration as the key factors influencing PbB abnormalities. These findings provide a scientific foundation for the implementation of precision occupational health management models.

AIM: To investigate the influence of relevant inflammatory markers in peripheral blood on the progression of neovascular glaucoma(NVG)secondary to diabetic retinopathy(DR)patients.METHODS: Retrospective case-control study. Patients were categorized into two groups based on the presence or absence of NVG: those with proliferative diabetic retinopathy(PDR)alone(PDR group, n=148)and those with NVG secondary to PDR(NVG secondary to PDR group, n=142). Peripheral blood inflammatory markers were evaluated, including white blood cell-related indices, neutrophil-to-lymphocyte ratio(NLR), platelet-to-lymphocyte ratio(PLR), monocyte-to-lymphocyte ratio(MLR), and systemic immune-inflammation index(SII). The distinctions in peripheral blood inflammatory markers between the two groups of patients and their relationships with NVG secondary to PDR were analyzed.RESULTS:No statistically significant differences were observed in basic characteristics between the two groups, confirming their comparability. However, significant differences were found in eosinophil percentage and MLR between the PDR group and the NVG secondary to PDR group(all P<0.05), with both values being significantly higher in the NVG secondary to PDR group. Multivariate Logistic regression analysis revealed that the eosinophil percentage and the MLR were factors influencing the development of patients with NVG secondary to PDR.CONCLUSION: Eosinophil percentage and MLR may be associated with the progression of PDR to NVG, and could serve as potential predictive markers for NVG development in PDR patients.

Cardiovascular disease remains a significant factor leading to patient mortality and influencing outcomes during the perioperative period.Dexmedetomidine is an α2-adrenergic receptor agonist with functions such as analgesia,sedation,and anti-anxiety.This review summarizes various types of findings regarding dexmedetomidine in myocardial ischemia/reperfusion injury and conducts in-depth discussions on the corresponding mechanisms of the results,aiming to further clarify the potential cardioprotective effects of dexmedetomidine in patients with heart diseases.

Soil cadmium (Cd) pollution is one of the major environmental problems globally. Ophiopogon japonicus, a multifunctional plant extensively used in traditional Chinese medicine, has demonstrated potential in environmental remediation. This study investigated the Cd accumulation pattern of O. japonicus under cadmium stress and identified the heavy metal ATPase (HMA) family members in this plant. Our results demonstrated that O. japonicus exhibited a Cd enrichment factor (EF) of 2.75, demonstrating strong potential for soil Cd pollution remediation. Nine heavy metal ATPase (HMA) members of P1B-ATPases were successfully identified from the transcriptome data of O. japonicus, with OjHMA1-OjHMA6 classified as the Zn/Co/Cd/Pb-ATPases and OjHMA7-OjHMA9 as the Cu/Ag-ATPases. The expression levels of OjHMA1, OjHMA2, OjHMA3, and OjHMA7 were significantly up-regulated under Cd stress, highlighting their crucial roles in cadmium ion absorption and transport. The topological analysis revealed that these proteins possessed characteristic transmembrane (TM) segments of the family, along with functional A, P, and N domains involved in regulating ion absorption and release. Metal ion-binding sites (M4, M5, and M6) existed on the TM segments. Based on the number of transmembrane domains and the residues at metal ion-binding sites, the plant HMA family members were categorized into three subgroups: P1B-1 ATPases, P1B-2 ATPases, and P1B-4 ATPases. Specifically, the P1B-1 ATPase subgroup included the motifs TM4(CPC), TM5(YN[X]₄P), and TM6(M[XX]SS); the P1B-2 ATPase subgroup featured the motifs TM4(CPC), TM5(K), and TM6(DKTGT); the P1B-4 ATPase subgroup contained the motifs TM4(SPC) and TM6(HE[X]GT), all of which were critical for protein functions. Molecular docking results revealed the importance of conserved sequences such as CPC/SPC, DKTGT, and HE[X]GT in metal ion coordination and stabilization. These findings provide potential molecular targets for enhancing Cd uptake and tolerance of O. japonicus by genetic engineering and lay a theoretical foundation for developing new cultivars with high Cd accumulation capacity.
Cadmium/metabolism* ; Adenosine Triphosphatases/metabolism* ; Ophiopogon/drug effects* ; Soil Pollutants/toxicity* ; Plant Proteins/metabolism* ; Stress, Physiological ; Multigene Family ; Gene Expression Regulation, Plant

Foodborne illnesses caused by Salmonella pose significant threats to worldwide public health safety.In this study,a rapid method for screening viable Salmonella in oyster sauce and milk was developed by utilizing an electronic microbial growth analyzer(EMGA).Target food samples were diluted 10-fold with RVS broth and loaded into test tubes.Test tubes were positioned in the EMGA to determine the bacterial growth curves and the time required to reach the maximum growth rate(Tmgr).Using Salmonella typhimurium(S.typhimurium)asan model species,there was linear relationship between the logarithmic value of viable bacterial concentration(lgC)and Tmgr over the range of 5×101-5×106 CFU/mL,with a detection limit of 10 CFU/mL.For oyster sauce,the regression equation was Tmgr(min)=-80.775lg[C/(CFU/mL)]+754.96(R2=0.9907),and the recovery rates of S.typhimurium ranged from 95.2%to 119.8%,with relative standard deviations(RSD)ranging from 3.5%to 16.3%.For milk,the regression equation was Tmgr(min)=-71.922 lg[C/(CFU/mL)]+618.65(R2=0.9985),with recovery rates ranging from 98.4%to 110.6%and RSD ranging from 6.4%to 12.8%.The EMGA method required only one portable instrument,and involving only three manual steps,i.e.,dilution,transfer,and insertion.When S.typhimurium contamination reached 106 CFU/mL,the total time consumption,from the unwrapping of samples to the readout of bacterial concentration,was no more than 7 h.When applied to detection of actual oyster sauce and milk samples,the new method demonstrated strong consistency with plate counting results in positive detection rates.This method was superior to the plate counting method,which was generally considered as a gold standard,in terms of accuracy,precision,simplicity and efficiency,representing a promising alternative for the on-site screening and quantification of viable Salmonella in oyster sauce and milk products.

Forensic medicine has been designated as a first-level discipline,presenting new opportunities and challenges for the development of forensic medicine.Since the 1980s,the establishment of foren-sic medicine discipline and the cultivation of high-level forensic talents have become hot topics in the development of forensic medicine in China.Since the 13th Five-Year Plan,the forensic team of Shanxi Medical University has been aiming at the forefront,proposing the development goals of"Five First-class"and the discipline development path"Six Major Achievements".It has selected benchmark disci-plines,identified gaps in disciplinary development,unified thoughts,formulated completion timelines,concentrated superior resources,assigned tasks to individuals,and created an"Organized Fill-in-the-Blank Format"forensic medicine discipline construction model with the characteristics of the new era.The construction model of forensic medicine has achieved good results in the goals,discipline frame-work,scientific research,talent cultivation,discipline team and platform construction,forming a rela-tively complete discipline construction and management system,and accumulating valuable experience for the construction of first-level discipline and high-level talent cultivation of forensic medicine.