Objective To systematically analyze the spatiotemporal distribution characteristics and epidemiological trends of tracheal, bronchus, and lung cancer (TBL) disease burden attributed to air pollution globally and in China and the United States from 1990 to 2021, and to assess the patterns of disease burden changes from 2022 to 2031 based on predictive models, providing a scientific basis for formulating targeted TBL prevention and control strategies. Methods Based on the Global Burden of Disease (GBD) 2021 database, we analyzed the disease burden data of TBL attributed to air pollution globally and in China and the United States from 1990 to 2021. R Studio 4.3.2 software was used to analyze the corresponding trends and the Bayesian age-period-cohort (BAPC) prediction model was used to predict the status of the disease burden of TBL attributed to air pollution in the world and in China and the United States from 2022 to 2031. Results In 2021, China had the highest number of deaths and disability-adjusted life years attributed to air pollution (211 400 patients and 4.8947 million person-years), followed by the United States (6 000 patients and 124 300 person-years). The age-standardized mortality rate (ASMR) and age-standardized disability-adjusted life years rate (ASDR) of TBL due to air pollution in the world and in China and the United States showed a decreasing trend. From 1990 to 2021, the ASMR and ASDR of TBL in China due to air pollution were much higher than those in the United States and the global average. In terms of gender, from 1990 to 2021, the disease burden of male patients with TBL attributed to air pollution was much higher than that of female patients. The BAPC prediction model showed that from 2022 to 2031, the ASMR and ASDR of TBL attributed to air pollution showed an upward trend globally, while they showed a downward trend in China and the United States. Conclusion Over the past 30 years, the air pollution-related TBL disease burden in the world and in China and the United States has continued to decline, but China's disease burden is still significantly higher than the global average. The disease burden in men far exceeds that in women, with men and the population aged ≥50 years being high-risk groups. In the future, the global disease trend may reverse and rise, while China and the United States are expected to continuously decline. However, precise prevention and control for high-risk groups remains a key challenge.

Objective:To establish a prediction model for larger for gestational age(LGA)infants in pregnant women with gestational diabetes mellitus(GDM)in order to improve pregnancy outcomes.Methods:A retro-spective analysis was performed on the clinical data of 338 pregnant women with GDM who underwent routine prenatal examinations and were hospitalized for delivery in the First Affiliated Hospital of Nanjing Medical Universi-ty from January 1,2018 to December 31,2023.Pregnant women with complete HbAlc data during pregnancy were divided into a training set of 241 cases and a validation set of 97 cases.Lasso and Logistic regression analysis and variable screening combined with previous clinical experience were used to construct a nomogram model,and its degree of differentiation and calibration were evaluated.Result:①By Lasso regression analysis,age,family histo-ry of type 2 diabetes,body mass index(BMI),gestational weight gain(GWG),fasting blood glucose(FBG),postprandial 1-hour blood glucose(1h PBG),HbAlc,free triiodothyronine(FT3),free thyroxine(FT4)and insulin treatment were important predictors of LGA.②Multivariate Logistic regression analysis showed that GWG and HbAlc were independent risk factors for LGA in pregnant women with GDM(OR＞1,P＜0.05).③Combined with Lasso and Logistic regression analysis,previous literature reports and clinical experience,BMI,GWG,FBG,1h PBG,HbAlc and FT3 were selected as independent variables,and LGA as dependent variable.A nomogram pre-diction model was constructed in the training set,and the C-index of 0.71.ROC curve analysis showed that the AUC values of the training set and the validation set were 0.709 and 0.700,respectively,and the discriminative a-bility of the model was acceptable.The calibration curve of the model was close to the ideal curve,and the clinical decision curve suggested that the model showed a positive net benefit at the threshold of 10％to 50％.Conclu-sion:The predictive model has certain value in predicting the occurrence of LGA in pregnant women with GDM,and provides help for early diagnosis,treatment and clinical intervention of GDM and its complications,in order to improve perinatal and long-term adverse outcomes.

Objective To comprehensively analyze the disease burden of respiratory cancers in China from 1990 to 2021, and predict the trend of disease burden changes from 2022 to 2031, in order to improve its prevention and treatment strategies. Methods Data from the Global Burden of Disease (GBD) 2021 database were extracted and analyzed for the disease burden of nasopharyngeal cancer, laryngeal cancer, and tracheal, bronchial and lung cancers (hereinafter referred to as lung cancer) in China from 1990 to 2021. The Joinpoint 4.9.1.0 software was utilized to analyze the corresponding trends. The grey prediction model [GM (1,1)] was employed to forecast the disease burden of respiratory cancers in China from 2022 to 2031. Results The disease burden of respiratory cancers attributed to tobacco and occupational carcinogens in China raised from 1990 to 2021. Among the respiratory cancers, lung cancer led in terms of incidence, mortality, and disability-adjusted life years (DALY) and their respective age-standardized rates from 1990 to 2021, followed by nasopharyngeal cancer, with laryngeal cancer being the lowest. Analysis via the Joinpoint regression model indicated that, overall, the disease burden of nasopharyngeal and laryngeal cancers in China decreased during this time period, while that of lung cancer increased. From a gender perspective, the disease burden of male patients was significantly higher than that of female patients from 1990 to 2021. Compared to the global average, the disease burden of respiratory cancers in China from 1990 to 2021 was still relatively heavy. As of 2021, the middle-aged and elderly population above 50 years old was the primary group suffering from the disease burden of respiratory cancers in China. The prediction model showed that the age-standardized rate of nasopharyngeal cancer in China would decline from 2022 to 2031; the age-standardized incidence rate of laryngeal cancer in China would increase, while its age-standardized mortality rate and DALY rate would both decrease; the age-standardized rates of lung cancer in China would increase. Conclusion In the past 30 years, the disease burden of nasopharyngeal and laryngeal cancers in China has lightened, but the overall disease burden of lung cancer is still on the rise. Compared to the global average, the disease burden of respiratory cancers in China is still relatively heavy. The disease burden in male patients is significantly higher than that in female patients, and the population above 50 years old is the main group suffering from the disease burden. In the next 10 years, the disease burden of respiratory cancers in China will still tend to increase. Therefore, targeted prevention and treatment strategies for men and the middle-aged and elderly populations remain key challenges that urgently need to be addressed in China's response to respiratory cancers.

Background Occupational stress has emerged as a critical public health concern affecting the physical and mental well-being of workers in the manufacturing sector. However, researchers typically evaluate its core driver—cumulative fatigue—using a crude binary “present/absent” variable, thereby overlooking the high-dimensional complexity and heterogeneity inherent in fatigue characteristics. This oversimplification constrains both the precision and predictive performance of occupational stress risk assessment model. Objective Leveraging a data-driven approach, to survey data on cumulative fatigue among manufacturing employees, and then use this new classification to develop and validate an occupational stress prediction model, with an ultimate aim of enhancing the accuracy and effectiveness of occupational stress assessment. Methods A set of cross-sectional survey data on 3871 manufacturing employees in 2021 were derived from the “Long working hours exposure and its adverse health effect risk assessment” program of the National Institute for Occupational Health and Poison Control, Chinese Center for Disease Control and Prevention. Occupational stress was assessed using the Core Occupational Stress Measurement Scale, while cumulative fatigue was evaluated via the Worker’s Self-Diagnostic Questionnaire for Fatigue Accumulation. Boruta method was applied to screen core variables from 20 occupational stress influencing factors. Dimensionality reduction of cumulative fatigue features was performed using a Bayesian sparse autoencoder (BASE), followed by comparison and application of clustering methods to achieve multi-class classification of the reduced features. Six machine learning classification models were then selected including logistic regression, support vector machine, decision tree, random forest, adaptive boosting, and lightweight gradient boosting machine (LightGBM) to construct and compare two occupational stress prediction models involving cumulative fatigue combined with ten other core variables: one utilizing the original binary cumulative fatigue label as a core variable, and another employing the novel fatigue classification proposed herein as a core variable. Results The positive rate of occupational stress among the manufacturing employees was 38.9%. The Boruta method identified 11 core variables of occupational stress: depressive symptoms, cumulative fatigue, age, length of service, tenure in current position, average weekly working hours, average daily overtime hours, average monthly income, low levels of exercise, life satisfaction, and sleep quality. The BSAE reduced the original cumulative fatigue factors to 12 dimensions, while the K-means clustering grouped the reduced fatigue features into three categories: no fatigue, moderate fatigue, and sever fatigue. Six occupational stress prediction models were constructed using the three-category labels of cumulative fatigue and ten additional factors. The results indicated that the LightGBM model performed best, achieving an area under the curve (AUC) of 0.78, an accuracy of 0.77, and an F1 score of 0.72. Compared with the model incorporating the traditional binary labels for cumulative fatigue, the best prediction AUC (0.72), accuracy (0.66), and F1 score (0.65) improved by 6%, 11%, and 7%, respectively. Conclusion Cumulative fatigue is a significant predictor of occupational stress among manufacturing employees. Applying data dimensionality reduction combined with three-class cluster analysis to characterize cumulative fatigue improves the performance of occupational stress prediction models. From a data-driven perspective, machine learning methods demonstrate strengths in processing complex datasets, capturing nonlinear relationships, and generating predictions based on key variables. This study therefore confirms that refined processing of core factors substantially enhances the predictive capability of machine learning models in assessing occupational stress risk in the manufacturing workforce.

Objective To integrate and analyze the disease burden of esophageal cancer caused by alcohol consumption in China from 1990 to 2019, along with the differences between genders, and predict the trends in disease burden changes from 2020 to 2029 to improve prevention and treatment strategies. Methods The disease burden of esophageal cancer caused by alcohol consumption in China from 1990 to 2019 was extracted and integrated from the 2019 Global Burden of Disease (GBD) database, and the corresponding trend was analyzed using the Joinpoint regression model with Joinpoint 4.9.1.0 software. The gray prediction model [GM (1, 1) ] was used to forecast the disease burden of alcohol-related esophageal cancer in China from 2020 to 2029. Results In 2019, the leading causes of esophageal cancer in China were tobacco, alcohol, high body mass index, and insufficient fruit and vegetable intake, accounting for the first to fifth positions in esophageal cancer deaths. From a gender perspective, in 2019, the death number and standardized mortality rate for males were 18.97 times and 20.00 times higher than for females, respectively. The disability-adjusted life years (DALYs) and standardized DALYs rate for males were 33.08 times and 24.78 times higher than those for females, respectively, indicating a heavier disease burden of alcohol-related esophageal cancer among Chinese males. From 1990 to 2019, the average annual percentage change (AAPC) in deaths and DALYs due to alcohol-related esophageal cancer in China was 2.08% and 1.63%, respectively, showing a continuous upward trend with statistical significance (P<0.05). The AAPC values for standardized mortality rate and standardized DALYs rate from 1990 to 2019 were –0.92% and –1.23%, respectively, showing a continuous downward trend with statistical significance (P<0.05). The population aged ≥55 years was the main group bearing the disease burden among all age groups from 1990 to 2019. The gray prediction model predicted that by 2029, the overall standardized mortality rate and standardized DALYs rate would decrease to 2.94/100 000and 67.94/100 000, with a greater decline in females than in males. Conclusion Over the past 30 years, the disease burden of alcohol-related esophageal cancer in China has slightly decreased. However, the reduction in disease burden is still lower compared to the overall decline in esophageal cancer burden, and the disease burden for males is significantly higher than for females. Focusing on prevention and treatment for males and the elderly population remains a major issue in addressing alcohol-related esophageal cancer in China.

Prostate cancer is the most prevalent malignant tumor among men, ranking first in incidence and second in mortality globally. Novel hormone therapies (NHT) targeting the androgen receptor (AR) pathway have become the standard of care for metastatic prostate cancer. This review offers a comprehensive overview of NHT, including abiraterone, enzalutamide, apalutamide, darolutamide, and rezvilutamide, which have demonstrated efficacy in delaying disease progression and improving patient survival and quality of life. Nevertheless, resistance to NHT remains a critical challenge. The mechanisms underlying resistance are complex, involving AR gene amplification, mutations, splice variants, increased intratumoral androgens, and AR-independent pathways such as the glucocorticoid receptor, neuroendocrine differentiation, DNA repair defects, autophagy, immune evasion, and activation of alternative signaling pathways. This review discusses these resistance mechanisms and examines strategies to counteract them, including sequential treatment with novel AR-targeted drugs, chemotherapy, poly ADP-ribose polymerase inhibitors, radionuclide therapy, bipolar androgen therapy, and approaches targeting specific resistance pathways. Future research should prioritize elucidating the molecular basis of NHT resistance, optimizing existing therapeutic strategies, and developing more effective combination regimens. Additionally, advanced sequencing technologies and resistance research models should be leveraged to identify novel therapeutic targets and improve drug delivery efficiencies. These advancements hold the potential to overcome NHT resistance and significantly enhance the management and prognosis of patients with advanced prostate cancer.

Objective:To integrate and analyze the trend of the disease burden of tracheal,bronchus,and lung cancer(TBL)attributable to secondhand smoke in China from 1990 to 2021 and to analyze future projections,aiming to provide data support for the prevention and treatment of TBL in China.Methods:Based on the global burden of disease(GBD)2021 database,TBL with ICD-10 disease classification C33,C34-C34.92 was studied.Using secondhand smoke as a risk factor,the data on TBL mortality and disability-adjusted life year(DALY)due to secondhand smoke in China from 1990 to 2021 were further age-standardized.Using Joinpoint 4.7.1 regression analysis model to calculate annual percentage change(APC)and average annual percentage change(AAPC),Hiplot software was used to plot disease burden data for different ages and genders,and R 4.3.1 software was used to construct a grey model GM(1,1)to predict the predicted value and trend of TBL disease burden attributed to secondhand smoke in China from 2022 to 2031.Results:From 1990 to 2021,the TBL mortality rate,age-standardized mortality rate,and DALY rate attributed to secondhand smoke in China increased from 1.76/100 000,2.63/100 000,and 49.43/100 000 to 4.08/100 000,2.80/100 000,and 95.57/100 000,respectively;the growth was 131.18%,6.45%,and 93.34%;the age-standardized DALY rate decreased from 65.04/100 000 to 63.32/100 000 with the reduction of 2.65%.The results of the Joinpoint regres-sion showed that the AAPC(95%CI)of mortality,age-standardized mortality rate,and DALY rate for TBL were 2.75(2.58-2.93)%,0.16(0.11-0.21)%,and 2.15(2.11-2.18)%,respectively,with an overall increasing trend;the AAPC(95%CI)of age-standardized DALY rate was-0.14(-0.40-0.12)%,with an overall fluctuating and unchanged trend and it was higher in males than in females.In both 1990 and 2021,the TBL mortality rate attributable to secondhand smoke in China gradually increased with age,and the DALY rate first increased and then slowed down with age.The main groups of the burden of disease were the elderly and males.The grey prediction model GM(1,1)showed that the age-standardized mortality rate of TBL attributable to secondhand smoke from 2022 to 2031 showed a slow increasing trend,and the predicted value in 2031 would increase to 2.95/100 000.The age-standardized DALY showed a slow decreasing trend,and the predicted value in 2031 would decrease to 63.83/100 000.Conclusions:From 1990 to 2021,the TBL mortality,age-standardized mortality,and DALY rates attributable to secondhand smoke in China increased,and the age-standardized DALY rate decreased.Men and the elderly are the main groups affected by TBL.Appropriate measures should be formulated to reduce exposure to and contact with secondhand smoke,tak-ing into account gender and age differences.Additionally,efforts should be made to strengthen secondhand smoke prevention and public health education.

Objective:To explore the impact of air pollution on hospital visits for mental and behavioral disorders among residents in an industrial area in Henan Province from 2016 to 2021.Methods:Daily outpatient visits data for mental and behavioral disorders were collected from Angang General Hospital in Angang Industrial Area at Anyang City between January 2016 and December 2021. And air pollutants and meteorological data during the same period were also collected. A generalized additive model was used for time-series analysis to examine the relationship between daily average concentrations of nitrogen dioxide (NO 2), sulfur dioxide (SO 2), fine particulate matter (PM 2.5), inhalable particulate matter (PM 10), carbon monoxide (CO), and ozone (O 3) with a lag of 0 to 7 days on the number of visits for mental and behavioral disorders among residents. The single-day lag effect (lag0-lag7 d) and cumulative lag effect (lag01-lag07 d) were analyzed. The smooth cubic spline function was used to fit the exposure-response relationship, and subgroup analysis was performed according to different genders, seasons and ages. Results:A total of 26 268 hospital visits for mental and behavioral disorders were collected from the industrial area between 2016 and 2021. The daily average concentrations of SO 2, NO 2, PM 2.5, PM 10, and CO were (27.50±27.33), (43.11±18.33), (73.87±60.30), (134.01±83.81) μg/m 3, and (1.72±1.03) mg/m 3, respectively. The daily maximum 8-hour average concentration of O 3 was (82.18±53.70) μg/m 3. After controlling for long-term trends, temperature, relative humidity, day of the week effects, and holiday effects, the generalized additive model analysis showed that NO 2 had a statistically significant impact on the hospital visits for mental and behavioral disorders at lag0 d, lag2 d and lag01-lag05 d and CO had a statistically significant impact at lag0-lag3 d and lag01-lag06 d (all P<0.05). NO 2 at lag02-lag04 d and CO at lag0-lag2 d and lag01-lag04 d had statistically significant effects on the visits for neurasthenia (both P<0.05). The impacts of NO 2 at lag03-lag04 d, PM 2.5 at lag3 d and lag03-lag04 d, PM 10 at lag3 d and lag03 d, and CO at lag3 d and lag01-lag05 d on visits for generalized anxiety disorder were also statistically significant (all P<0.05). After false discovery rate (FDR) correction, it was shown that for every 10 μg/m 3 increase in NO 2 and every 0.1 mg/m 3 increase in CO, the percentage increase in visits for mental and behavioral disorders and its 95% confidence interval (95% CI) were 3.38% (0.95%-5.87%) and 0.78% (0.38%-1.17%), respectively. For every 0.1 mg/m 3 increase in CO, the visits for neurasthenia increased by 0.78% (0.27%-1.29%). For every 10 μg/m 3 increase in PM 2.5 and every 0.1 mg/m 3 increase in CO, the visits for generalized anxiety disorder increased by 1.07% (0.46%-1.68%) and 1.17% (0.37%-1.97%), respectively (adjusted P<0.05). There was a linear exposure-response relationship between NO 2 and CO and the hospital visits for mental and behavioral disorders, CO and the hospital visits for neurasthenia, and CO and PM 2.5 and the hospital visits for generalized anxiety disorder ( P<0.05 for the overall association test and P>0.05 for the non-linearity test). Stratified analysis showed that air pollutants had an impact on male patients with neurasthenia, female patients with generalized anxiety disorder, individuals aged <45 years with mental and behavioral disorders, and individuals aged ≥65 years with generalized anxiety disorder. The impact of air pollutants was greater during the cold season or winter. Conclusion:Exposure to air pollution can increase hospital visits for mental and behavioral disorders among residents in industrial areas, with a higher risk among those aged<45 years old and during the cold season.

Objective:To explore the impact of air pollution on hospital visits for mental and behavioral disorders among residents in an industrial area in Henan Province from 2016 to 2021.Methods:Daily outpatient visits data for mental and behavioral disorders were collected from Angang General Hospital in Angang Industrial Area at Anyang City between January 2016 and December 2021. And air pollutants and meteorological data during the same period were also collected. A generalized additive model was used for time-series analysis to examine the relationship between daily average concentrations of nitrogen dioxide (NO 2), sulfur dioxide (SO 2), fine particulate matter (PM 2.5), inhalable particulate matter (PM 10), carbon monoxide (CO), and ozone (O 3) with a lag of 0 to 7 days on the number of visits for mental and behavioral disorders among residents. The single-day lag effect (lag0-lag7 d) and cumulative lag effect (lag01-lag07 d) were analyzed. The smooth cubic spline function was used to fit the exposure-response relationship, and subgroup analysis was performed according to different genders, seasons and ages. Results:A total of 26 268 hospital visits for mental and behavioral disorders were collected from the industrial area between 2016 and 2021. The daily average concentrations of SO 2, NO 2, PM 2.5, PM 10, and CO were (27.50±27.33), (43.11±18.33), (73.87±60.30), (134.01±83.81) μg/m 3, and (1.72±1.03) mg/m 3, respectively. The daily maximum 8-hour average concentration of O 3 was (82.18±53.70) μg/m 3. After controlling for long-term trends, temperature, relative humidity, day of the week effects, and holiday effects, the generalized additive model analysis showed that NO 2 had a statistically significant impact on the hospital visits for mental and behavioral disorders at lag0 d, lag2 d and lag01-lag05 d and CO had a statistically significant impact at lag0-lag3 d and lag01-lag06 d (all P<0.05). NO 2 at lag02-lag04 d and CO at lag0-lag2 d and lag01-lag04 d had statistically significant effects on the visits for neurasthenia (both P<0.05). The impacts of NO 2 at lag03-lag04 d, PM 2.5 at lag3 d and lag03-lag04 d, PM 10 at lag3 d and lag03 d, and CO at lag3 d and lag01-lag05 d on visits for generalized anxiety disorder were also statistically significant (all P<0.05). After false discovery rate (FDR) correction, it was shown that for every 10 μg/m 3 increase in NO 2 and every 0.1 mg/m 3 increase in CO, the percentage increase in visits for mental and behavioral disorders and its 95% confidence interval (95% CI) were 3.38% (0.95%-5.87%) and 0.78% (0.38%-1.17%), respectively. For every 0.1 mg/m 3 increase in CO, the visits for neurasthenia increased by 0.78% (0.27%-1.29%). For every 10 μg/m 3 increase in PM 2.5 and every 0.1 mg/m 3 increase in CO, the visits for generalized anxiety disorder increased by 1.07% (0.46%-1.68%) and 1.17% (0.37%-1.97%), respectively (adjusted P<0.05). There was a linear exposure-response relationship between NO 2 and CO and the hospital visits for mental and behavioral disorders, CO and the hospital visits for neurasthenia, and CO and PM 2.5 and the hospital visits for generalized anxiety disorder ( P<0.05 for the overall association test and P>0.05 for the non-linearity test). Stratified analysis showed that air pollutants had an impact on male patients with neurasthenia, female patients with generalized anxiety disorder, individuals aged <45 years with mental and behavioral disorders, and individuals aged ≥65 years with generalized anxiety disorder. The impact of air pollutants was greater during the cold season or winter. Conclusion:Exposure to air pollution can increase hospital visits for mental and behavioral disorders among residents in industrial areas, with a higher risk among those aged<45 years old and during the cold season.

Objective:To establish a prediction model for larger for gestational age(LGA)infants in pregnant women with gestational diabetes mellitus(GDM)in order to improve pregnancy outcomes.Methods:A retro-spective analysis was performed on the clinical data of 338 pregnant women with GDM who underwent routine prenatal examinations and were hospitalized for delivery in the First Affiliated Hospital of Nanjing Medical Universi-ty from January 1,2018 to December 31,2023.Pregnant women with complete HbAlc data during pregnancy were divided into a training set of 241 cases and a validation set of 97 cases.Lasso and Logistic regression analysis and variable screening combined with previous clinical experience were used to construct a nomogram model,and its degree of differentiation and calibration were evaluated.Result:①By Lasso regression analysis,age,family histo-ry of type 2 diabetes,body mass index(BMI),gestational weight gain(GWG),fasting blood glucose(FBG),postprandial 1-hour blood glucose(1h PBG),HbAlc,free triiodothyronine(FT3),free thyroxine(FT4)and insulin treatment were important predictors of LGA.②Multivariate Logistic regression analysis showed that GWG and HbAlc were independent risk factors for LGA in pregnant women with GDM(OR＞1,P＜0.05).③Combined with Lasso and Logistic regression analysis,previous literature reports and clinical experience,BMI,GWG,FBG,1h PBG,HbAlc and FT3 were selected as independent variables,and LGA as dependent variable.A nomogram pre-diction model was constructed in the training set,and the C-index of 0.71.ROC curve analysis showed that the AUC values of the training set and the validation set were 0.709 and 0.700,respectively,and the discriminative a-bility of the model was acceptable.The calibration curve of the model was close to the ideal curve,and the clinical decision curve suggested that the model showed a positive net benefit at the threshold of 10％to 50％.Conclu-sion:The predictive model has certain value in predicting the occurrence of LGA in pregnant women with GDM,and provides help for early diagnosis,treatment and clinical intervention of GDM and its complications,in order to improve perinatal and long-term adverse outcomes.