With the continuous rise in the incidence of chronic diseases,the traditional medical model has gradually shown limitations in coping with long-term and complex chronic disease management.In recent years,the rapid development of"Internet+"technologies,especially the deep integration of artificial intelligence,big data and other technologies,has opened up new paths for chronic disease management.This paper explores the application status and practical challenges of the"Internet+"chronic disease management model,and deeply analyzes its important role in improving the efficiency of chronic disease manage-ment,enhancing patients' health levels,and optimizing the allocation of medical resources.At the same time,it focuses on the prominent problems in technological progress and practical application,and puts forward targeted improvement measures accord-ingly,so as to provide suggestions and references for the high-quality development of"Internet+"chronic disease management in China.

With the continuous rise in the incidence of chronic diseases,the traditional medical model has gradually shown limitations in coping with long-term and complex chronic disease management.In recent years,the rapid development of"Internet+"technologies,especially the deep integration of artificial intelligence,big data and other technologies,has opened up new paths for chronic disease management.This paper explores the application status and practical challenges of the"Internet+"chronic disease management model,and deeply analyzes its important role in improving the efficiency of chronic disease manage-ment,enhancing patients' health levels,and optimizing the allocation of medical resources.At the same time,it focuses on the prominent problems in technological progress and practical application,and puts forward targeted improvement measures accord-ingly,so as to provide suggestions and references for the high-quality development of"Internet+"chronic disease management in China.

Driven by the accelerating aging of global population,the prevalence of chronic diseases has been increasing annually.A growing proportion of patients often suffer from multiple concurrent chronic diseases-a condition termed multimorbidi-ty.In China,the utilization of healthcare services by patients with chronic disease multimorbidity is significantly influenced by factors such as age,economic status,education and other social determinants,giving rise to notable disparities in healthcare eq-uity.This paper reviews the current status of chronic-disease multimorbidity,investigate healthcare service utilization among such patients,and evaluate the extent of inequity so as to provide scientific evidence and policy references for improving the equity of health service use for this population.

Objective To develop a combined model integrating radiomics and 3D deep learning features for improving the predictive efficacy of overall survival in non-small cell lung cancer(NSCLC)patients undergoing radiotherapy,thereby providing a foundation for optimizing individualized radiotherapy strategies.Methods A retrospective analysis was conducted on 522 NSCLC patients from 3 centers.Radiomics features were extracted from the tumor region of interest on radiotherapy planning CT scans,and a 3D-SE-ResNet was constructed to extract deep learning features.Following feature extraction,features were selected via univariate Cox analysis and Lasso-Cox regression,and a combined model was established by fusing the two feature types through principal component analysis.The discriminative ability of the model was evaluated using the concordance index(C-index)and the area under the receiver operating characteristic curve(AUC),while the risk stratification efficacy was verified by Kaplan-Meier survival analysis.Results The predictive performance of deep learning features was significantly superior to that of radiomics features(C-index:0.73 vs 0.65).The combined model achieved the highest predictive performance in the training set,internal test set,and external test set(C-index:0.74,0.69,0.72 respectively),with higher AUC values for predicting 1-year,2-year,and 3-year OS than either single model.Kaplan-Meier analysis showed significant differences in survival between the high-and low-risk groups(Log-rank test,P<0.001),and calibration curves indicated good consistency between predicted and actual survival outcomes.Conclusion The combined model integrating radiomics and 3D deep learning features can accurately predict survival outcomes in NSCLC patients undergoing radiotherapy.The multi-center validation results support its potential application in prognosis stratification for individualized radiotherapy.

Driven by the accelerating aging of global population,the prevalence of chronic diseases has been increasing annually.A growing proportion of patients often suffer from multiple concurrent chronic diseases-a condition termed multimorbidi-ty.In China,the utilization of healthcare services by patients with chronic disease multimorbidity is significantly influenced by factors such as age,economic status,education and other social determinants,giving rise to notable disparities in healthcare eq-uity.This paper reviews the current status of chronic-disease multimorbidity,investigate healthcare service utilization among such patients,and evaluate the extent of inequity so as to provide scientific evidence and policy references for improving the equity of health service use for this population.

Objective To develop a combined model integrating radiomics and 3D deep learning features for improving the predictive efficacy of overall survival in non-small cell lung cancer(NSCLC)patients undergoing radiotherapy,thereby providing a foundation for optimizing individualized radiotherapy strategies.Methods A retrospective analysis was conducted on 522 NSCLC patients from 3 centers.Radiomics features were extracted from the tumor region of interest on radiotherapy planning CT scans,and a 3D-SE-ResNet was constructed to extract deep learning features.Following feature extraction,features were selected via univariate Cox analysis and Lasso-Cox regression,and a combined model was established by fusing the two feature types through principal component analysis.The discriminative ability of the model was evaluated using the concordance index(C-index)and the area under the receiver operating characteristic curve(AUC),while the risk stratification efficacy was verified by Kaplan-Meier survival analysis.Results The predictive performance of deep learning features was significantly superior to that of radiomics features(C-index:0.73 vs 0.65).The combined model achieved the highest predictive performance in the training set,internal test set,and external test set(C-index:0.74,0.69,0.72 respectively),with higher AUC values for predicting 1-year,2-year,and 3-year OS than either single model.Kaplan-Meier analysis showed significant differences in survival between the high-and low-risk groups(Log-rank test,P<0.001),and calibration curves indicated good consistency between predicted and actual survival outcomes.Conclusion The combined model integrating radiomics and 3D deep learning features can accurately predict survival outcomes in NSCLC patients undergoing radiotherapy.The multi-center validation results support its potential application in prognosis stratification for individualized radiotherapy.

OBJECTIVE To investigate the effects of 3 different primitives or the same primitives with different characters of Tibetan medicine Zuomoxing[Caragana changduenais Liou f. with red heartwood, Caragana jubata(Pall.) Poir. with brown and white heartwood] on rats with pattern of toxic heat-induced blood stasis. METHODS　Ninety SD rats were randomly divided into the blank group, model group, aspirin-positive group, Changdu low-dose group(CDD), Changdu high-dose group(CDG), whitewood of Guijian low-dose group(GJBD), whitewood of Guijian high-dose group(GJBG), brown wood of Guijian low-dose group(GJZD), Brown wood of Guijian high-dose group(GJZG). Models with heat toxicity and blood stasis pattern were established by intraabdominal injection of carrageenan combined with tail vein injection of lipopolysaccharide. The effects of each group on blood rheology, coagulation four indices and blood routine were determined, and the content of arachidonic acid(AA), IL-1β, IL-6, TNF-α and thromboxane B2(TXB2) were measured with ELISA. RESULTS ①Blood rheology: Compared with model group, CDD and CDG significantly decreased whole blood viscosity(WBV), reduction viscosity of whole blood(WBRV), erythrocyte rigidity index(HGX), erythrocyte deformability index(EDI), whole blood relative index(WBRI) (P<0.01), and increased plasma viscosity(P<0.01). GJZG and GJZD significantly decreased HGX(P<0.01 or P<0.05), and increased plasma viscosity(P<0.01). GJBG and GJBG significantly decreased WBHSV, WBHSRV, HGX, EDI, and whole blood high shear relative index(WBHSRI)(P<0.01). ②Coagulation four indices: Compared with model group, CDD significantly reduced the thrombin time(TT)(P<0.01). GJZG significantly reduced activated partial thromboplastin time(APTT) and TT(P<0.01 or P<0.05). GJBD significantly reduced prothrombin time(PT) and APTT (P<0.01 or P<0.05). ③Blood routine: Compared with model group, GJZD and GJBD significantly decreased the percentage of monocytes(P<0.01 or P<0.05). The number of large platelets in CDD significantly increased(P<0.05). CDG significantly increased the platelet number, platelet hematocrit, and large platelet number(P<0.01 or P<0.05), and tended which to be normal. ④Inflammatory factors: Compared with model group, the levels of TNF-α, IL-6, TXB2 were significantly increased in CDD and CDG(P<0.01 or P<0.05). The levels of IL-6 and TXB2 were significantly increased in GJZD and GJZG(P<0.01). GJBD was significantly increased TXB2(P<0.01), and GJBG was significantly increased IL-1β, IL-6, and TXB2(P<0.01), while decreased AA(P<0.05). CONCLUSION Zuomoxing with separate sources have different degrees of effects on rats with pattern of toxic heat-induced blood stasis, and have different degrees of effects on hemorheology, coagulation factors, blood routine and inflammatory mediators, and the degree and trend of effects are different with different doses. The effect of promoting blood circulation and removing blood stasis was generally manifested as Changdu > whitewood of Guijian > Brown wood of Guijian. The effect of promoting blood circulation and removing blood stasis may be the result of multiple pathways and mechanisms.