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: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: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 correlation of color Doppler flow imaging (CDFI) and shear wave elastography (SWE) with pathological classification and prognosis of breast cancer patients.Methods:A total of 87 patients (103 lesions) with breast cancer admitted to Shanxi Maternal and Child Health Care Hospital and the Second Hospital of Shanxi Medical University From May. 2021 to Mar. 2024 were retrospectively included. All patients underwent CDFI and SWE examinations before surgery. The pathological characteristics and molecular typing of each lesion were recorded, and the correlation of CDFI and SWE examination parameters with molecular typing of breast cancer was evaluated. Patients were followed up for 1 year, and the predictive value of CDFI and SWE parameters in lymph node metastasis was analyzed by receiver operating characteristic curve (ROC) .Results:There were no statistically significant differences in the pulse index (PI) , resistance index (RI) , maximum lesion elastic modulus (E max) , and the ratio between the elastic value at the hardest lesion and the elastic value of adipose tissue (E ratio) among patients with different pathological types ( F=0.64, 0.13, 0.81, 2.84, P>0.05) . There were no statistically significant differences in PI and RI values among patients with different tumor sizes ( F=2.99, 1.81, P>0.05) , and statistically significant differences in E max and E ratio among patients with different tumor sizes ( F=6.42, 34.31, P<0.05) . The differences among different molecular types PI, RI, E max, and E ratio were statistically significant ( F=406.59, 245.23, 206.30, 204.36, P<0.05) , and Luminal B type PI, RI, E max, and E ratio were the highest, followed by HER2-positive, triple-negative, and Luminal A type, with statistically significant differences ( P<0.05) . PI, RI, E max and E ratio in patients with positive lymph node metastasis were higher than those in patients with negative lymph node metastasis ( t=4.99, 3.04, 2.70, 3.13, all P<0.05) . ROC results showed that the area under the curve (AUC) of PI, RI, E max and E ratio for predicting lymph node metastasis of breast cancer were 0.654, 0.704, 0.664 and 0.696, respectively. The sensitivity to predict lymph node metastasis of breast cancer was 74.19%, 54.84%, 51.61%, 64.52, and the specificity was 54.17%, 79.17%, 79.17%, 70.83% (all P<0.05) . Conclusions:The correlation of CDFI and SWE examination parameters are correlated with the molecular classification of breast cancer, and the prediction of lymph node metastasis of breast cancer is good.

Objective:To assess the effectiveness of combining color Doppler ultrasound (CDU) with shear wave elastography (SWE) in forecasting the likelihood of breast cancer (BC) recurrence.Methods:From Jan. 2022 to Jan. 2024, we gathered data on 92 BC patients admitted to Shanxi Maternal and Child Health Hospital and Shanxi Cancer Hospital, focusing on their lesion characteristics. Each patient underwent CDU and SWE examinations pre-surgery and was monitored for a year. Based on BC recurrence, patients were categorized into recurrence and non-recurrence groups. We compared CDU imaging and hemodynamic features of BC between these groups and evaluated SWE elastic modulus values. To assess the agreement between CDU, SWE, and their combined use in predicting BC recurrence and pathological diagnosis, we employed the Kappa test. Additionally, we plotted ROC curves to analyze the predictive power of CDU, SWE, and their combination in assessing BC recurrence risk. Results:Among the 92 BC patients studied, 38 experienced recurrence, while 54 did not. CDU examination revealed that the non-recurrence group exhibited significantly larger tumor maximum diameter, higher peak systolic velocity (PSV), a higher proportion of aspect ratio ≥ 1, irregular margins, calcification, and increased vascular abundance, compared to the recurrence group ( t/ χ2=17.188, 18.491, 6.099, 15.374, 14.526, 19.318, P<0.05). Additionally, the vascular resistance index (RI) was lower in the non-recurrence group ( t=-26.429, P<0.05). SWE results indicated that the recurrence group had higher average (E mean), maximum (E max), and minimum (E min) elastic moduli compared to the non-recurrence group ( t=14.39, 12.34, 8.29, P<0.05). CDU and SWE predictions showed substantial agreement with pathological results, with Kappa values of 0.66 and 0.69, respectively ( P<0.05). The combination of CDU and SWE predictions demonstrated excellent concordance with pathological outcomes ( Kappa=0.91, P<0.05). In terms of predicting BC recurrence risk, CDU and SWE had accuracies of 83.70% and 84.21%, respectively. The ROC curve analysis showed AUC values of 0.830 for CDU, 0.847 for SWE, and 0.955 for their combination. Sensitivity was 0.870 for CDU, 0.852 for SWE, and 0.963 for the combination. Specificity was 0.789 for CDU, 0.842 for SWE, and 0.947 for the combination. Positive predictive values were 78.95% for CDU, 84.21% for SWE, and 94.74% for the combination, while negative predictive values were 87.04% for CDU, 85.19% for SWE, and 96.30% for the combination. The AUC for CDU in predicting post-operative BC recurrence risk was not significantly different from SWE ( χ2=0.04, P>0.05), but the combined prediction AUC was significantly higher than individual predictions ( χ2=8.00, 7.04, P<0.05) . Conclusion:The predictive value of CDU and SWE combined examination for the recurrence risk of BC is better than that of single examination, and it is suggested that the combined examination method should be popularized in clinic.

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 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 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.