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.

Background Long working hours, as a common risk factor for occupational stress, is closely related to the occurrence of depressive symptoms. Understanding how long working hours affect occupational stress and depressive symptoms will inform occupational health interventions. Objective To quantify the impact of long working hours exposure on occupational stress and depressive symptoms among Internet industry employees, translate black-box outputs into actionable insights, and demonstrate the value of interpretable machine learning for early-warning occupational-health surveillance. Methods A dataset was derived from a cross-sectional survey involving 2866 internet industry employees in China. This survey was part of the project Risk Assessment Of Long Working Hour Exposure And Its Adverse Health Effects, conducted by the National Institute for Occupational Health and Poisoning Control, Chinese Center for Disease Control and Prevention, from 2021 to 2023. Working hours, occupational stress and depressive symptoms were quantified with a set of structured questionnaires including the Core Occupational Stress Scale and the Patient Health Questionnaire. Pairwise associations were screened by Mantel tests and variance-inflation factors. Key predictors identified through feature selection were fed into six machine-learning risk-prediction models. Visual interpretation was provided by feature importance, Shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME), while directed causal effects and intervention impacts of prolonged working hours exposure on occupational stress and depressive symptoms were dissected with causal explanation of features techniques. Results The positive rates of occupational stress and depressive symptoms among internet employees were 12.9% and 77.8% respectively. Twelve core features for occupational stress and nine for depressive symptoms were retained after selection. After these features were supplied to six predictive algorithms and evaluated on five metrics, the Light Gradient Boosting Machine (LGBM) achieved the highest accuracy—0.89 for occupational stress and 0.79 for depressive symptoms on the hold-out test set. The feature-importance rankings converged on fatigue accumulation and life satisfaction as dominant drivers for both outcomes, whereas weekly working hours and daily overtime emerged as the principal exposure-related predictors. The SHAP summary plots revealed that longer weekly hours and daily overtime systematically elevated the probability of occupational stress. The causal feature explanation further quantified that ascending one category in weekly working hours increased the probability of occupational stress by 7.04%. Conclusion Exposure to long working hours is associated with both occupational stress and depressive symptoms among internet industry employees. Interpretable machine-learning frameworks translate these associations into transparent, defensible drivers, enabling precise identification of the pivotal factors and their interplay. This evidence base equips occupational-health practitioners with actionable insights for designing targeted prevention and intervention strategies.

Accurate assessment of treatment response after local ablation is of great importance for detecting residual viable tumor， evaluating ablative margins， and guiding subsequent follow-up and monitoring in patients with hepatocellular carcinoma （HCC）. Contrast-enhanced computed tomography and magnetic resonance imaging are currently the main imaging modalities for post-ablation evaluation， but have certain limitations in real-time imaging， intraoperative application， and repeatability. Ultrasound， especially contrast-enhanced ultrasound （CEUS）， can be used throughout the entire process of ablation therapy for HCC due to its advantages of real-time visualization of microvascular perfusion， no ionizing radiation， and repeatability， thereby playing an important role in detecting residual viable tumor， assessing immediate treatment response， and monitoring patients during follow-up. This article reviews the pathological basis and major endpoints of treatment response assessment after thermal ablation for HCC， with a focus on the clinical application of ultrasound techniques and the Contrast-Enhanced Ultrasound Liver Imaging Reporting and Data System Treatment Response Algorithm. In addition， it discusses the future development directions of multimodal imaging， quantitative assessment， and standardized evaluation， in order to provide a reference for further optimizing the treatment response assessment system for HCC after ablation.

Objective To analyze the disease burden and its changing trends of tracheal, bronchus, and lung cancer (TBL) attributable to high fasting plasma glucose (HFPG) in China from 1990 to 2021, and to provide key strategic evidence for the prevention and treatment of TBL. Methods Data related to TBL attributable to HFPG in China from 1990 to 2021 were collected from the Global Burden of Disease Study 2021 database, including mortality rate, disability-adjusted life years (DALYs), age-standardized rates, and other indicators. The Joinpoint regression model was applied to analyze the temporal trends of these indicators. Furthermore, the grey prediction model GM (1, 1) was used to forecast the disease burden of TBL attributable to HFPG in China from 2022 to 2031. Results From 1990 to 2021, the overall disease burden of TBL attributable to HFPG in China showed an upward trend. The total number of deaths, DALYs, crude mortality rate, age-standardized mortality rate, crude DALYs rate, and age-standardized DALYs rate increased from 4700, 121300 person-years, 0.40/100000, 0.61/100000, 10.31/100000, and 14.10/100000 in 1990 to 17400, 386500 person-years, 1.22/100000, 0.82/100000, 27.16/100000, and 17.72/100000 in 2021, with growth rates of 270.21%, 218.63%, 205.00%, 34.43%, 163.43%, and 25.67%, respectively. The increase rates among females were higher than those among males. Analysis using the Joinpoint regression model indicated that both the age-standardized mortality rate and age-standardized DALYs rate exhibited a significant upward trend, with average annual percentage changes (AAPC) of 1.01% and 0.82%, respectively, during 1990-2021 (P<0.05). The disease burden across different age and gender groups generally increased from 1990 to 2021. Mortality and DALYs rates for both males and females rose with advancing age, with elderly individuals and males constituting the primary affected populations. The GM (1, 1) grey prediction model projected continued increases in mortality rate, age-standardized mortality rate, DALYs rate, and age-standardized DALYs rate of TBL attributable to HFPG in China from 2022 to 2031, reaching 1.64/100000, 1.06/100000, 36.45/100000, and 21.81/100000, respectively, by 2031. Conclusion The disease burden of TBL attributable to HFPG remains substantial in China from 1990 to 2021, with males and elderly populations bearing the highest burden. However, the growth rate of disease burden is faster among females compared to males. It is predicted that the disease burden will continue to rise over the next decade, necessitating enhanced focus on early diagnosis and treatment for women and older adults.

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.

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 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 investigate the distribution of carbapenem-resistant genes and their drug susceptibility in vitro on carbapen-em-resistant Enterobacter cloacae(CRECC)in Taizhou area,and provide evidence for effective anti-infective treatment in clinical prac-tice.Methods Forty-seven strains of CRECC isolated from Enze Hospital,Taizhou Enze Medical Center(Group)and Luqiao Reha-bilitation Hospital during January 2015 and November 2022 were retrospectively analyzed.The enzyme types and resistance genes of carbapenemase were detected by the NG-Test Carba 5 and Carba-R Xpert,respectively,and the susceptibility of CERCC to common drugs was tested in vitro.Results Among 47 strains of CRECC,27 were detected to produce carbapenemase,including 24 producing New Delhi metallo-β-lactamase(NDM)type,1 producing both Klebsiella pneumoniae carbapenemase(KPC)and NDM types,and 2 producing imipenemase(IMP)type.One strain belonged to NDM genotype but no NDM enzyme type was detected.The CRECC strains had the highest sensitivity to polymyxin B(95.7%),followed by tigecycline(93.6%),fosfomycin(61.7%),and ceftazidime/avibac-tam(40.4%).In addition,the CRECC strains producing carbapenemase were more sensitive to polymyxin B,fosfomycin and aztreo-nam than those without producing carbapenemase.Conclusion The CRECC strains in Taizhou area are mainly NDM type,which has high sensitivity to polycolistin B,tigecycline and fosfomycin.NG-Test Carba 5 can not cover some strains that do not produce carbapen-emase or carry mutations in carbapenemase.

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.