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.

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 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 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.

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 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.