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.

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.

Opium is obtained by air-drying the milky latex extracted from the unripe capsules of the opium poppy(Papaver somniferum).This latex is rich in benzylisoquinoline alkaloids(BIA),with major active compounds in-cluding morphine,codeine,thebaine,papaverine,and noscapine.Compound licorice oral solution and compound licorice tablets are derivative drugs containing opium.Initially classified as over-the-counter(OTC)medications,both formulations were later reclassified as prescription drugs by the National Medical Products Administration(NMPA),restricting their purchase without proper authorization.Although the national pharmacopeia standards specify the morphine content in the opium raw materials used for compound licorice oral solution and tablets,they lack mandatory requirements for the detection and quantification of the other four major alkaloids.Given the unique nature of opium raw materials and the stringent regulatory requirements for such drugs,it is imperative to enhance and refine simultaneous detection and control methods for all alkaloid components in these products.Furthermore,the establishment of scientific and reasonable detection and control standards for preservatives in compound licorice formulations is crucial to improving overall product quality management and ensuring drug safety and efficacy.This study analyzes and discusses the quality standards,detection methods,and research progress for opium and com-pound licorice preparations,aiming to explore the potential for technological innovation and ensure the safe use of these medications.

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:To explore the relationship between trait mindfulness and nomophobia in middle school students.Methods:A total of 942 middle school students were selected to use the Mindfulness Attention Awareness Scale and the Nomophobia Scale for Chinese for two data collection intervals of 12 months(T1 and T2,respective-ly).Results:The MAAS scores were higher at T1 than at T2(P＜0.001).The simultaneous(r=-0.11,-0.21,Ps＜0.01)and sequential(r=-0.14,-0.15,Ps＜0.001)correlations between MAAS scores and NSC scores were significant.The MAAS scores at T1 negatively predicted the NSC scores at T2(β=-0.09),and the NSC scores at T1 also negatively predicted the MAAS scores at T2(β=-0.10).Conclusion:It suggests that trait mind-fulness and nomophobia could predict each other in middle school students.

Objective To explore the effectiveness of a hospital-to-home transitional health management program for caregivers of children with severe encephalitis,aiming to provide a reference for optimizing transitional care models for these patients.Methods A convenience sampling method was used to select 84 children with severe encephalitis and their caregivers admitted to the neurology department of a tertiary hospital in Zhengzhou between March 2023 and June 2024.According to the admission time,they were divided into an experimental group and a control group,with 42 cases in each group.The experimental group received a hospital-to-home transitional health management program in addition to routine care,while the control group received standard care and discharge instructions.Differences in caregivers' perceived benefits,caregiver burden,disease management ability,and post-intervention outcomes of the children were compared between the 2 groups before and after the intervention.Results All 42 participants in both groups completed the study.After the intervention,the experimental group reported higher perceived benefits of(91.29±9.76)compared to(84.81±12.86)in the control group,lower caregiver burden of(48.55±7.15)compared to(54.71±11.23)in the control group,greater disease management ability of(41.83±4.97)than(37.79±5.23)in the control group,and lower difficulty in disease management of(31.52±7.82)compared to(34.55±3.96)in the control group,with all differences being statistically significant(P＜0.05).No statistically significant difference was found in the prognosis of the children between the 2 groups(P=0.500).Conclusion The hospital-to-home transitional health management program can effectively enhance caregivers'perceived benefits and disease management capabilities,reduce their caregiving burden,and provide scientific evidence for optimizing transitional care for children with severe encephalitis.

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 develop a novel solid-phase extraction(SPE)method based on a functionalized nanofiber membrane for the efficient co-extraction of structurally diverse antibiotics with markedly different physico-chemical properties from source water,and to establish a high-throughput analysis method by coupling this technique with ultra-performance liquid chromatography-tandem mass spectrometry(UPLC-MS/MS).Methods A polydopamine and zirconium(Ⅳ)fumarate metal-organic frameworks(MOF-801)co-modified polyacrylonitrile nanofiber membrane(PDA@PAN/MOF-801 NFMs)was prepared as the SPE adsorbent through hybrid electrospinning and dopamine self-polymerization.Critical SPE and UPLC-MS/MS parameters were optimized,and the method was applied to analyze antibiotic contamination in source water samples from 14 sources of centralized drinking water supply in Suzhou,China,to evaluate the practical application potential of the method.Results The PDA@PAN/MOF-801 NFMs adsorbent demonstrated efficient adsorption of 32 antibiotics from 6 classes through multiple retention mechanisms,including synergistic electrostatic interactions,hydrogen bonding,and π-π interactions.In combination with UPLC-MS/MS,the SPE method we developed enabled high-throughput detection of multiple antibiotics in source water,with limits of detection(LOD)being 0.001-0.05 ng/L and limits of quantitation(LOQ)being 0.005-500 ng/L.Spiked recoveries were 70.14%-111.50%.Intra-day relative standard deviation(RSD)was below 14.12%and the inter-day RSD was below 15.07%.The method demonstrated excellent sensitivity,accuracy,and precision.Conclusion In this study,we successfully developed an efficient analytical method based on a novel nanofiber membrane adsorbent.This approach provides a new technical reference for the high-throughput detection of multiple antibiotics in environmental waters and shows promising potential for practical applications.