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 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.

Objective To analyze the current situation of the knowledge-attitude-practice among noise-exposed workers at an airport apron. Methods A total of 494 noise-exposed workers from an airport apron were selected as the study subjects using the judgmental sampling method. A self-designed "Occupational Protection Knowledge, Attitudes, and Practices Questionnaire" was used to assess the current situation of knowledge-attitude-practice on occupational protection. Results Regarding the awareness of noise hazards among the study subjects, the awareness rates of noise-induced impairment on digestive function and reproductive system were the lowest (44.9% and 37.7%, respectively). The awareness rate of noise-induced negative emotions increased with length of service (P<0.01). Regarding the occupational protection knowledge for noise, the awareness rate of occupational noise-induced deafness was “incurable” was the lowest (39.1%). The support rate for five kinds of occupational protection attitudes for noise was generally >85.0%, while only 58.3% of the study subjects consistently or frequently wearing earplugs during work. The most common source of noise hazard and protection knowledge was pre-employment training (76.9%), followed by occupational disease prevention and control campaigns (76.1%). Conclusion Noise-exposed workers in this airport apron have incomplete awareness of non-auditory system hazards caused by noise, and the awareness of knowledge of some occupational protection is relatively low. Although their attitudes toward occupational protection are positive, many workers still fail to consistently wear personal protective equipment at work.

Glyphosate is an organophosphorus herbicide with a large global production volume and wide application. It is commonly present in the environment and has a relatively long residual period. Its long-term toxicity, carcinogenic potential and other health effects deserve attention and further research. Glyphosate exposure has toxic effects on a variety of organs, including but not limited to hepatotoxicity, reproductive toxicity, neurotoxicity, etc., and is associated with the occurrence and development of different types of cancer. This article summarizes the physicochemical properties of glyphosate, its usage and pollution status in China, sorts out and summarizes its population load situation, and focuses on discussing the hepatotoxic effects of glyphosate under various exposure scenarios, including the acute poisoning situation and the progress of long-term low-dose exposure in the general population, and long-term high dose exposure condition in the occupational population. The hepatotoxic effects of glyphosate on mammals, fish, and organs and cells in vitro experiments were deeply analyzed. The possible toxicological mechanism of glyphosate hepatotoxicity was analyzed from oxidative stress, mitochondrial dysfunction, inflammation and other aspects, aiming to explore the effects of glyphosate exposure on liver health and provide a reference for scientific guidance on the rational use of glyphosate.

Objective:To compare the effects of propofol and dexmedetomidine for sedation on the outcome in mechanically ventilated patients with severe pulmonary infection.Methods:Patients with severe pulmonary infection (Clinical Pulmonary Infection Score >7) requiring mechanical ventilation from the Medical Information Mart for Intensive Care Ⅳ database between 2008 and 2020 were selected and divided into propofol group and dexmedetomidine group based on the sedative agent used. The primary outcome was all-cause in-hospital mortality, and secondary outcomes included 90-day all-cause mortality and duration of mechanical ventilation. Inverse probability of treatment weighting (IPTW) was used to adjust for baseline confounders, and logistic regression and linear regression were applied to analyze the effects of the two sedative drugs on the outcomes of mechanically ventilated patients with pulmonary infection. Kaplan-Meier survival curves were used to analyze survival outcomes.Results:A total of 6 204 critically ill patients with pulmonary infection requiring mechanical ventilation were included, with 3 439 cases in propofol group and 2 765 cases in dexmedetomidine group. The baseline characteristics between the two groups were well balanced (standardized mean difference < 0.1) after IPTW adjustment. In the IPTW-adjusted cohort, the in-hospital all-cause mortality and 90-day all-cause mortality were significantly lower in dexmedetomidine group than in propofol group (439.2[18.7%] vs 563.6[24.1%], 618.0[26.3%] vs 733.6[31.3%], P<0.001), the results of Further Kaplan-Meier survival curve analysis showed that the 90-day all-cause mortality was significantly lower in dexmedetomidine group than in propofol group ( P<0.01). Conclusions:Compared with propofol, dexmedetomidine can decrease the mortality rate and improve the prognosis in mechanically ventilated patients with severe pulmonary infection when used for sedation.

Objective:To construct and validate a predictive model for postoperative pulmonary complications (PPCs) in patients undergoing robot-assisted laparoscopic urological surgery.Methods:This retrospective study included the medical records of 932 patients who underwent robot-assisted laparoscopic urological surgery at the First Affiliated Hospital of Zhejiang University School of Medicine from January 2020 to February 2022. The patients were divided into a training group ( n=559) and a validation group ( n=373) at a 6∶4 ratio. Logistic regression analysis was used to determine the independent risk factors for PPCs, and a nomogram prediction model was constructed based on these factors. The performance of the model was evaluated using the receiver operating characteristic curve and calibration curve, and the clinical benefit was assessed using the clinical decision curve analysis. Results:The independent risk factors for PPCs included advanced age (>60 yr), smoking history, respiratory tract infection within 1 month, preoperative low SpO 2 (<96%), and prolonged length of postoperative hospital stay ( P<0.05), and the body mass index (18.5-<28.0 kg/m 2) was a protective factor. The nomogram prediction model developed based on the aforementioned 6 influencing factors had an area under the receiver operating characteristic curve of 0.81 (95% confidence interval 0.76-0.86) in training group and 0.80 (95% confidence interval 0.75-0.86) in validation group. The calibration curve indicated a good consistency between the predicted and actual occurrence curves, and the clinical decision curve analysis showed good accuracy and net benefit of the prediction model. Conclusions:The predictive model for PPCs is successfully constructed based on age, low body mass index, smoking history, history of respiratory tract infection within 1 month, preoperative low SpO 2 and prolonged length of postoperative hospital stay and has good predictive performance in patients undergoing robot-assisted laparoscopic urological surgery.

Objective To explore the influence of function-oriented training combined with proprio-ceptive neuromuscular facilitation(PNF)technique on stroke patients during convalescence.Methods A total of 106 patients with cerebral infarction during convalescence admitted to the First Hospital of Hebei Medical University and Hebei provincial of Tradifional Chinese Medicine Hospital from January 2021 to December 2023 were recruited and then randomly divided into combined group(53 cases)and control group(53 cases).The control group only received function-oriented training,while the combined group received function-oriented training combined with PNF technique.The neurological function,balance function,walking ability and motor function were compared before and after intervention and between groups.Results After intervention,the NIHSS score and central nervous system specific protein(S100β)and neuron specific enolase(NSE)levels were significantly lower in the two groups than those before intervention(P＜0.05),and the score and levels after intervention were significantly lower in combined group than the control group(13.28±0.86 vs 15.76±0.94,P＜0.01;1.56±0.24 μg/L vs 1.79±0.23 μg/L,P＜0.0 1;20.46±3.12 μg/L vs 24.75±3.69 μg/L,P＜0.01).The Berg balance scale(BBS)score,step speed and step length in 10-meter walking test(10 MWT)and Fugl-Meyer assessment score were significantly higher in both groups after intervention than those before intervention(P＜0.05),and the above indicators in the combined group were obviously higher than those in refer-ence group after intervention(P＜0.05,P＜0.01).Conclusion For the patients during cerebral infarction convalescence,function-oriented training combined with PNF technique can not only promote the recovery of neurological function,but also enhance the balance ability and walking a-bility,and improve the limb motor function.

Glyphosate is an organophosphorus herbicide with a large global production volume and wide application. It is commonly present in the environment and has a relatively long residual period. Its long-term toxicity, carcinogenic potential and other health effects deserve attention and further research. Glyphosate exposure has toxic effects on a variety of organs, including but not limited to hepatotoxicity, reproductive toxicity, neurotoxicity, etc., and is associated with the occurrence and development of different types of cancer. This article summarizes the physicochemical properties of glyphosate, its usage and pollution status in China, sorts out and summarizes its population load situation, and focuses on discussing the hepatotoxic effects of glyphosate under various exposure scenarios, including the acute poisoning situation and the progress of long-term low-dose exposure in the general population, and long-term high dose exposure condition in the occupational population. The hepatotoxic effects of glyphosate on mammals, fish, and organs and cells in vitro experiments were deeply analyzed. The possible toxicological mechanism of glyphosate hepatotoxicity was analyzed from oxidative stress, mitochondrial dysfunction, inflammation and other aspects, aiming to explore the effects of glyphosate exposure on liver health and provide a reference for scientific guidance on the rational use of glyphosate.

Objective:To compare the effects of propofol and dexmedetomidine for sedation on the outcome in mechanically ventilated patients with severe pulmonary infection.Methods:Patients with severe pulmonary infection (Clinical Pulmonary Infection Score >7) requiring mechanical ventilation from the Medical Information Mart for Intensive Care Ⅳ database between 2008 and 2020 were selected and divided into propofol group and dexmedetomidine group based on the sedative agent used. The primary outcome was all-cause in-hospital mortality, and secondary outcomes included 90-day all-cause mortality and duration of mechanical ventilation. Inverse probability of treatment weighting (IPTW) was used to adjust for baseline confounders, and logistic regression and linear regression were applied to analyze the effects of the two sedative drugs on the outcomes of mechanically ventilated patients with pulmonary infection. Kaplan-Meier survival curves were used to analyze survival outcomes.Results:A total of 6 204 critically ill patients with pulmonary infection requiring mechanical ventilation were included, with 3 439 cases in propofol group and 2 765 cases in dexmedetomidine group. The baseline characteristics between the two groups were well balanced (standardized mean difference < 0.1) after IPTW adjustment. In the IPTW-adjusted cohort, the in-hospital all-cause mortality and 90-day all-cause mortality were significantly lower in dexmedetomidine group than in propofol group (439.2[18.7%] vs 563.6[24.1%], 618.0[26.3%] vs 733.6[31.3%], P<0.001), the results of Further Kaplan-Meier survival curve analysis showed that the 90-day all-cause mortality was significantly lower in dexmedetomidine group than in propofol group ( P<0.01). Conclusions:Compared with propofol, dexmedetomidine can decrease the mortality rate and improve the prognosis in mechanically ventilated patients with severe pulmonary infection when used for sedation.

Objective:To construct and validate a predictive model for postoperative pulmonary complications (PPCs) in patients undergoing robot-assisted laparoscopic urological surgery.Methods:This retrospective study included the medical records of 932 patients who underwent robot-assisted laparoscopic urological surgery at the First Affiliated Hospital of Zhejiang University School of Medicine from January 2020 to February 2022. The patients were divided into a training group ( n=559) and a validation group ( n=373) at a 6∶4 ratio. Logistic regression analysis was used to determine the independent risk factors for PPCs, and a nomogram prediction model was constructed based on these factors. The performance of the model was evaluated using the receiver operating characteristic curve and calibration curve, and the clinical benefit was assessed using the clinical decision curve analysis. Results:The independent risk factors for PPCs included advanced age (>60 yr), smoking history, respiratory tract infection within 1 month, preoperative low SpO 2 (<96%), and prolonged length of postoperative hospital stay ( P<0.05), and the body mass index (18.5-<28.0 kg/m 2) was a protective factor. The nomogram prediction model developed based on the aforementioned 6 influencing factors had an area under the receiver operating characteristic curve of 0.81 (95% confidence interval 0.76-0.86) in training group and 0.80 (95% confidence interval 0.75-0.86) in validation group. The calibration curve indicated a good consistency between the predicted and actual occurrence curves, and the clinical decision curve analysis showed good accuracy and net benefit of the prediction model. Conclusions:The predictive model for PPCs is successfully constructed based on age, low body mass index, smoking history, history of respiratory tract infection within 1 month, preoperative low SpO 2 and prolonged length of postoperative hospital stay and has good predictive performance in patients undergoing robot-assisted laparoscopic urological surgery.