Background Lung cancer, one of the most common malignant tumors worldwide, has long ranked first in cancer incidence and mortality, posing a severe challenge to public health systems. Objective To analyze the trends in incidence, mortality, and disability-adjusted life years (DALYs) of lung cancer among residents in Jing'an District, Shanghai, from 2002 to 2021, explore the impacts of population aging, population growth, and age-specific prevalence on disease burden, and provide a scientific basis for optimizing regional lung cancer prevention and control strategies. Methods Based on the cancer registration and cause-of-death surveillance data of registered residents in Jing'an District, Shanghai, from 2002 to 2021, Joinpoint regression models were used to analyze the annual change trends (APC) and average annual change trends (AAPC) of lung cancer incidence, mortality, DALY rate, and their age-standardized rates. Decomposition analysis was applied to quantify the contribution of population aging, population growth, and age-specific prevalence to changes in the number of new cases, deaths, and DALYs. Results From 2002 to 2021, the crude incidence rate of lung cancer in Jing'an District increased from 68.00 per 100000 to 144.36 per 100000 (AAPC=4.86%, P<0.001), and the age-standardized incidence rate rose from 43.09 per 100000 to 61.46 per 100000 (AAPC=2.89%, P<0.001). The growth rate of incidence was significantly higher in females (AAPC=6.73%) than in males (AAPC=3.62%). The crude mortality rate increased from 58.08 per 100000 to 66.11 per 100000 (AAPC=1.10%, P<0.001), while the age-standardized mortality rate decreased (AAPC=−1.54%, P<0.001). The crude DALY rate showed an upward trend (AAPC=0.94%, P=0.002), whereas the age-standardized DALY rate declined (AAPC=−1.63%, P<0.001). Significant differences were observed across genders and age groups: the mortality and DALY rates increased in males aged 60-64 years, while the fastest incidence growth occurred in females aged 45-64 years. The results of decomposition analysis indicated that the increase in new cases was primarily attributed to age-specific incidence (38.15%) and aging (37.31%); the growth in deaths and DALYs was driven by aging, while age-specific mortality and population growth showed offsetting effects. From 2002 to 2021, the probability of premature death from lung cancer among males (2.04%-2.52%) in Jing'an District showed no statistically significant trend (AAPC=−0.28%, P=0.440), while it presented a decreasing trend in the total population (1.37%-1.89%) and females (0.72%-1.26%) (AAPC=−0.80% and −2.23%, respectively; P=0.029 and <0.001, respectively). Conclusion From 2002 to 2021, the risk of lung cancer incidence in Jing’an District, Shanghai, continue to increase, with a disease burden exhibiting significant heterogeneity by gender and age: key features include a rapid increase in new cases and a trend toward younger onset among females, alongside rising mortality risk among males aged 60–64 years. Population aging and increasing age-specific incidence rates are primary drivers of the growing number of cases, while declining age-specific mortality rates mitigate the rise in mortality burden.

To investigate the correlation between vitamin D nutritional status and insulin resistance in pubertal adolescents.

To investigate body composition and associated factors in adolescents undergoing long-term regular sports training.

OBJECTIVE:Nowadays,machine learning algorithms are gradually being applied to predict stroke and cardiovascular disease.Compared with traditional regression models,machine learning can learn from data to achieve high prediction accuracy by exploring the flexible relationship between a large number of predictive features and outcome variables,providing a new method for the formulation of individualized treatment and rehabilitation programs.This study aims to systematically evaluate stroke functional recovery and prognosis prediction models based on machine learning,comprehensively assessing their predictive performance and clinical application potential to provide references for the development,application,and promotion of related predictive models.METHODS:This review was conducted following the PRISMA(Preferred Reporting Items for Systematic Reviews and Meta-Analyses)guidelines.Relevant literature on stroke prognosis prediction using machine learning methods was selected by searching PubMed,EMbase,Web of Science Core Collection,CNKI,WanFang,and the China Biomedical Literature Database,with the search period from January 1,2014,to July 1,2024.Two researchers independently screened the literature and extracted data based on inclusion and exclusion criteria,using the Prediction model Risk Of Bias ASsessment Tool(PROBAST)to assess model quality.RESULTS:(1)A total of 3 126 articles were obtained in the preliminary search.After screening and exclusion,18 articles were finally included.150 prediction models were constructed using 13 machine learning methods.The three most frequently used methods are Logistic Regression,Random Forest,and Extreme Gradient Boosting(XGBoost).Only one study was externally validated.Eight studies reported how the missing data were handled.(2)In terms of outcome indicators,8 studies used the combination of clinical data and imaging data to build models,9 studies only used clinical data to build models,and 1 study only used imaging data to build models.(3)Each of the 18 studies gave the most important characteristics of the study,with the most mentioned being the National Institute of Health Stroke Scale and age.All studies reported area under curve values ranging from 0.74 to 0.96,with the highest area under curve being 0.96.The overall risk of bias in all models was high.The high risk of bias in the field of model analysis was the main reason for the high risk of overall bias in all models.(4)The results of meta-analysis showed that age and National Institute of Health Stroke Scale score had significant influence on stroke prognosis,with age[MD=8.49,95％CI(6.24,10.75),P＜0.01]and National Institute of Health Stroke Scale score[MD=4.78,95％CI(2.56,7.00),P＜0.01].CONCLUSION:This study systematically evaluated the predictive model of functional recovery and prognosis of stroke based on machine learning,and all the models have good predictive potential.However,future studies should increase the sample size of the included model,adopt prospective studies,and add external validation of the model to improve the stability and prediction accuracy of the model,control the risk of bias,and contribute to the validation and promotion of the model in practical clinical applications.At the same time,the interpolation of missing values is more transparent and accurate.Although existing machine learning models show good predictive performance,it is also important to focus on the functionality and usability of the model,and the inclusion of features will reduce ease of use.We should develop easy to use model interfaces and user-friendly clinical tools to enable medical staff to better apply the model for clinical decision.

Objective:To investigate the incidence of osteoporosis (OP) in maintenance hemodialysis (MHD) patients at the blood purification center in Gaochun district, and analyze its influencing factors.Methods:A retrospective study was conducted on 3 622 patients who received regular MHD treatment at Nanjing Gaochun People′s Hospital and Nanjing Gaochun Traditional Chinese Medicine Hospital from January 2019 to December 2023. The demographic characteristics, comorbidities, and clinical data such as blood calcium and creatinine of patients were collected. The ultivariate Logistic regression model was applied to analyze the influencing factors of OP in MHD patients.Results:The survey revealed that 33.63% of MHD patients had decreased bone mass, and 37.24% of MHD patients experienced osteoporosis. According to the occurrence of OP, 3 622 patients were separated into the OP group (1 349 cases) and the non-OP group (2 273 cases). Univariate analysis showed that compared with the non-OP group, the albumin (ALB) level in the OP group was lower: (38.95 ± 5.17) g/L vs. (40.32 ± 5.84) g/L, there was statistical difference( P<0.05). Compared with the non-OP group, the levels of immunoreactive parathyroid hormone (iPTH) and alkaline phosphatase (ALP) in the OP group were higher: (262.29 ± 36.76) ng/L vs. (249.55 ± 32.73) ng/L, (114.74 ± 18.01) U/L vs. (109.63 ± 17.25) U/L, the proportion of patients aged≥60 years old, female and dialysis duration≥5 years was higher: 61.75%(833/1 349) vs. 47.87%(1 088/2 273), 66.35%(895/1 349) vs. 54.86%(1 247/2 273), 52.34%(706/1 349) vs. 34.36%(781/2 273), there were statistical differences( P<0.05). Multivariate Logistic regression revealed ALB ( OR = 0.724, 95% CI 0.568 - 0.920), iPTH ( OR = 1.374, 95% CI 1.095 - 1.725), ALP ( OR = 1.325, 95% CI 1.070 - 1.641), age ( OR = 2.753, 95% CI 1.664 - 4.556), gender ( OR = 2.993, 95% CI 1.611 - 5.560), and dialysis time ( OR = 4.216, 95% CI 2.365 - 7.516) were all influencing factors for the occurrence of OP in MHD patients ( P<0.05). Conclusions:The incidence of OP in MHD patients in Gaochun district is high, and its occurrence is closely related to ALB, iPTH, ALP, age, gender and dialysis time. Clinical attention should be focused on this.

Objective To compare the differences in the average transaction costs per-referral patients under different models of Integrated Delivery Systems(IDS).Methods Using a typical case sampling method,it selected referred patients from three IDS models:the county medical alliance in D City(Qinghai Province),the urban medical consortium in J District(Zhengzhou City,Henan Province),and the health management coalition in N County(Shandong Province).Structured questionnaires collected demographics,average transaction costs per-referral and cost perceptions.t-tests and ANOVA assessed cost differences;generalized linear regression identified influencing factors.Results Among 915 patients,the average transaction costs per-referral were 1 035.05 yuan(county alliance),195.31 yuan(urban consortium),and 700.97 yuan(health management coalition),with statistically significant differences(P＜0.05).The urban consortium exhibited lower time costs and specialized input costs.Key influencing factors included older age(county alliance),education level,employment status,and referral travel time(urban consortium),as well as urban-rural disparities(health management coalition).Patients'cost perceptions significantly differed across models(P＜0.05).Conclusion The urban medical consortium demonstrated the lowest patient the average transaction costs,highlighting its institutional advantage in minimizing financial burdens.

Objective To explore the correlation between the immune status of patients with multiple myeloma(MM)and the dynamic changes in peripheral blood red blood cell distribution width standard deviation(RDW-SD),serum free light-chain κ/λ ratio(sFLCR),and soluble B-cell maturation antigen(sBCMA),as well as their implications for prognosis.This study aims to provide a reference for evaluating disease progression and assessing patient outcomes.Methods 182 MM patients admitted to the hospital between July 2019 and July 2021 were enrolled as the study group.All selected patients were followed up for 3 years,with 6 cases lost to follow-up,resulting in a final cohort of 176 patients.These patients were further divided into two groups based on their progno-sis:the poor-prognosis group(53 cases)and the good-prognosis group(123 cases).Additionally,50 healthy volunteers who underwent health check-ups during the same period were randomly selected as the control group.The immune status of both the study group and the control group was compared.Univariate analysis was conducted to identify factors associated with poor prognosis in MM patients,and Cox regression analysis was performed to determine risk factors for poor prognosis.The good-prognosis group was designated as the negative group,while the poor-prognosis group was designated as the positive group.The predictive value of peripheral blood RDW-SD,serum sFLCR,and sBCMA-both individually and in combination-for poor prognosis in MM patients was evaluated by constructing receiver operating characteristic(ROC)curves.The area under the curve(AUC)was calculated,and the optimal cut-off value was determined using the Youden index.Finally,the predictive value of the combined test was analyzed by fitting an appropriate equation.Results Levels of peripheral blood Th17 cells and platelet-to-lymphocyte ratio(PLR)were significantly higher in the study group compared to the control group(P<0.05),while the level of peripheral blood regulatory T cells(Tregs)was significantly lower than that in the control group(P<0.05).Additionally,levels of peripheral blood RDW-SD and serum sBCMA were significantly higher in the poor prognosis group compared to the good prognosis group(P<0.05),whereas the serum level of sFLCR was significantly lower than that in the good prognosis group(P<0.05).Cox regression analysis revealed that elevated peripheral blood RDW-SD(HR=1.091,95%CI:1.027～1.159),reduced serum sFLCR(HR=1.095,95%CI:1.035～1.159),and increased serum sBCMA(HR=1.095,95%CI:1.016～1.165)were independent risk factors for poor prognosis in patients with MM(P<0.05).ROC curve analysis demonstrated that the combination of peripheral blood RDW-SD,serum sFLCR,and sBCMA assays achieved an AUC value of 0.880 for predicting poor prognosis in MM patients,which was significantly higher than those of the three individual assays(AUC values:0.805,0.786,0.780;P<0.05).The sensitivity and specificity of this combined assay were 94.34%and 68.29%,respectively.Conclusions MM patients exhibited abnormal immune status and poor prognosis,which was associ-ated with elevated levels of peripheral blood RDW-SD and serum sBCMA,as well as reduced serum sFLCR.More-over,the combination of peripheral blood RDW-SD,serum sFLCR,and sBCMA demonstrated superior predictive value for poor prognosis in MM patients.

OBJECTIVE:Nowadays,machine learning algorithms are gradually being applied to predict stroke and cardiovascular disease.Compared with traditional regression models,machine learning can learn from data to achieve high prediction accuracy by exploring the flexible relationship between a large number of predictive features and outcome variables,providing a new method for the formulation of individualized treatment and rehabilitation programs.This study aims to systematically evaluate stroke functional recovery and prognosis prediction models based on machine learning,comprehensively assessing their predictive performance and clinical application potential to provide references for the development,application,and promotion of related predictive models.METHODS:This review was conducted following the PRISMA(Preferred Reporting Items for Systematic Reviews and Meta-Analyses)guidelines.Relevant literature on stroke prognosis prediction using machine learning methods was selected by searching PubMed,EMbase,Web of Science Core Collection,CNKI,WanFang,and the China Biomedical Literature Database,with the search period from January 1,2014,to July 1,2024.Two researchers independently screened the literature and extracted data based on inclusion and exclusion criteria,using the Prediction model Risk Of Bias ASsessment Tool(PROBAST)to assess model quality.RESULTS:(1)A total of 3 126 articles were obtained in the preliminary search.After screening and exclusion,18 articles were finally included.150 prediction models were constructed using 13 machine learning methods.The three most frequently used methods are Logistic Regression,Random Forest,and Extreme Gradient Boosting(XGBoost).Only one study was externally validated.Eight studies reported how the missing data were handled.(2)In terms of outcome indicators,8 studies used the combination of clinical data and imaging data to build models,9 studies only used clinical data to build models,and 1 study only used imaging data to build models.(3)Each of the 18 studies gave the most important characteristics of the study,with the most mentioned being the National Institute of Health Stroke Scale and age.All studies reported area under curve values ranging from 0.74 to 0.96,with the highest area under curve being 0.96.The overall risk of bias in all models was high.The high risk of bias in the field of model analysis was the main reason for the high risk of overall bias in all models.(4)The results of meta-analysis showed that age and National Institute of Health Stroke Scale score had significant influence on stroke prognosis,with age[MD=8.49,95％CI(6.24,10.75),P＜0.01]and National Institute of Health Stroke Scale score[MD=4.78,95％CI(2.56,7.00),P＜0.01].CONCLUSION:This study systematically evaluated the predictive model of functional recovery and prognosis of stroke based on machine learning,and all the models have good predictive potential.However,future studies should increase the sample size of the included model,adopt prospective studies,and add external validation of the model to improve the stability and prediction accuracy of the model,control the risk of bias,and contribute to the validation and promotion of the model in practical clinical applications.At the same time,the interpolation of missing values is more transparent and accurate.Although existing machine learning models show good predictive performance,it is also important to focus on the functionality and usability of the model,and the inclusion of features will reduce ease of use.We should develop easy to use model interfaces and user-friendly clinical tools to enable medical staff to better apply the model for clinical decision.

Objective To investigate the relationship between different body position fixation tech-niques,umbilical plane volume change(ΔV),body weight change rate(ΔW%),and radiotherapy setup errors in cervical cancer patients,and to provide recommendations for determining the margin of planning target vol-ume(MPTV).Methods A retrospective analysis was performed on the clinical data of 57 cervical cancer pa-tients who underwent radiotherapy at this hospital from June 2022 to May 2023.Patients were divided into the observation group(fixed with body membrane+thermoplastic pad,n=24)and the control group(fixed with body membrane alone,n=33)based on different positioning fixation methods.They were also further strati-fied by median BMI into BMI≥23.82 kg/m2 and BMI<23.82 kg/m2 patients.Setup errors in the left-right(X),cranio-caudal(Y),and anterior-posterior(Z)directions were recorded.Meanwhile,the umbilical plane volume on the first CT positioning image and the patient's body weight before positioning were recorded,as well as umbilical plane volume of cone-beam CT(CBCT)verification images during weekly radiotherapy and body weight before scan,the ΔV and ΔW%were calculated.Setup errors were compared between two groups,and correlations between ΔV,ΔW%and setup errors were analyzed in all patients,in two groups(the obser-vation group and the control group)and in two BMI subgroups.MPTV values in X,Y,and Z directions were calculated,and receiver operating characteristic(ROC)curve determined the cut off values of ΔV and ΔW%when setup errors met the department's MPTV criteria.Results Compared with the control group,the ob-servation group showed significantly smaller setup errors in X,Y,and Z directions(P<0.05).Both ΔV and ΔW%were positively correlated with setup errors in X and Y direction in all patients,patients in the control group,and patients with BMI≥23.82 kg/m2(P<0.05).In the observation group,ΔW%was positively corre-lated with setup errors in Z direction in patients with BMI<23.82 kg/m2(P<0.05);In the control group,ΔV and ΔW%were positively correlated with setup errors in X and Y direction in patients with BMI≥23.82 kg/m2.ROC curve analysis showed that when setup errors in Y direction reached the department's MPTV criteria(8.41 mm),the cutoff values ΔV and ΔW%were 8.045 cm2 and 4.12%,respectively.Conclusion The body membrane+thermoplastic pad fixation technique reduces setup errors and mitigates the impact of ΔV and ΔW%on setup errors in X and Y directions.When ΔV or ΔW%exceeds the cutoff values,increasing CBCT verification frequency and re-fabricating the body membrane are recommended.

Objective To compare the differences in the average transaction costs per-referral patients under different models of Integrated Delivery Systems(IDS).Methods Using a typical case sampling method,it selected referred patients from three IDS models:the county medical alliance in D City(Qinghai Province),the urban medical consortium in J District(Zhengzhou City,Henan Province),and the health management coalition in N County(Shandong Province).Structured questionnaires collected demographics,average transaction costs per-referral and cost perceptions.t-tests and ANOVA assessed cost differences;generalized linear regression identified influencing factors.Results Among 915 patients,the average transaction costs per-referral were 1 035.05 yuan(county alliance),195.31 yuan(urban consortium),and 700.97 yuan(health management coalition),with statistically significant differences(P＜0.05).The urban consortium exhibited lower time costs and specialized input costs.Key influencing factors included older age(county alliance),education level,employment status,and referral travel time(urban consortium),as well as urban-rural disparities(health management coalition).Patients'cost perceptions significantly differed across models(P＜0.05).Conclusion The urban medical consortium demonstrated the lowest patient the average transaction costs,highlighting its institutional advantage in minimizing financial burdens.