Objective To establish a predictive model for digestive and respiratory infectious disease patients'selection of first-entry healthcare institutions and analyze the influencing factors,providing a reference for regional healthcare institution development.Methods The cluster sampling was used to select 1524 target patients.The data was randomly split 8∶2 into training and testing sets.The binary logistic regression method was employed to establish a predictive model for the selection of infectious disease patients'first choosing institution and analyze the influencing factors.The model's predictive performance was evaluated using the AUC of the testing set.Results It analyzed 1 524 infectious disease cases;417 chose community clinics first.Influences on their initial choice included education,insurance type,occupation,relative distance,and having a family doctor.The AUC of the test set was 0.851.Conclusion The predictive model of the selection of infectious disease patients'first choosing institution established in this study can be used to predict patient flow and help allocate medical resources reasonably.Increasing the rate of primary care for patients with infectious diseases in the region.

Objective To establish a predictive model for digestive and respiratory infectious disease patients'selection of first-entry healthcare institutions and analyze the influencing factors,providing a reference for regional healthcare institution development.Methods The cluster sampling was used to select 1524 target patients.The data was randomly split 8∶2 into training and testing sets.The binary logistic regression method was employed to establish a predictive model for the selection of infectious disease patients'first choosing institution and analyze the influencing factors.The model's predictive performance was evaluated using the AUC of the testing set.Results It analyzed 1 524 infectious disease cases;417 chose community clinics first.Influences on their initial choice included education,insurance type,occupation,relative distance,and having a family doctor.The AUC of the test set was 0.851.Conclusion The predictive model of the selection of infectious disease patients'first choosing institution established in this study can be used to predict patient flow and help allocate medical resources reasonably.Increasing the rate of primary care for patients with infectious diseases in the region.

Talents are the main force for the development of traditional Chinese medicine（TCM）， and the construction of TCM talents and the reformation of talent evaluation system are essential to promote the inheritance and innovation of TCM. At present， we are still exploring and developing in the fields of the formulation， implementation and evaluation indicators of TCM talent evaluation system. However， there are shortcomings and difficulties. For instance， insufficient stratification in the evaluation， excessive emphasis on the quantity of achievements， neglecting the quality of the achievements and the actual contribution， imperfect assessment indicators， and the weak characteristics of TCM. Therefore， national ministries and commissions have jointly issued a document requesting to break the four only and set a new standard， in order to promote the construction of a scientific and technological talent evaluation system oriented by innovation value， ability and contribution. For the evaluation of TCM clinical talents， China Association for Science and Technology commissioned China Association of Chinese Medicine to build the China Clinical Cases Library of TCM（CCCL-TCM）， which aims at collecting the most authoritative and representative TCM clinical cases and exploring the advantages of applying clinical cases as masterpiece of achievement in TCM clinical talents evaluation. CCCL-TCM can promote the construction of a talent evaluation system that is more in line with the development characteristics of TCM industry， and to carry out relevant pilot in TCM colleges and institutions across the country in order to promote the reformation of TCM talent evaluation system.

Objective:To explore the application of COVID-19-specific IgG antibody in identifying epidemic Omicron BA.5 strain infection.Method:Omicron BF.7/BA.5 naturally infected population, healthy population vaccinated with the COVID-19 vaccine, and Omicron BF.7/BA.5 breakthrough cases were enrolled into this study. The serum WT-S-IgG and BA.5-S-IgG were detected by indirect ELISA, and the serum-specific IgG antibody levels of different populations were compared. The application value of the two antibody titers and the ratio of the two antibodies in identifying Omicron BA.5 epidemic strain infection were explored by the ROC curve, aiming to provide technical support for pathogen diagnosis.Results:The antibody titers of WT-S-IgG and BA.5-S-IgG in the breakthrough cases were higher than those in the naturally infected population and the healthy population ( P<0.05). The area under the curve (AUC) of WT-S-IgG and BA.5-S-IgG in identifying epidemic Omicron BA.5 strain infection was 0.947 and 0.961, respectively. The AUC of BA.5-S-IgG and WT-S-IgG antibody titer ratio was 0.873. When the antibody titer ratio was 0.855, the sensitivity and specificity were 80.00% and 90.00%, respectively. According to the interval since the last infection, the AUC of the ratio of BA.5-S-IgG to WT-S-IgG antibody titer to identify the infection of epidemic strains less than 30 days and more than 30 days was 0.887 and 0.863, respectively, and the sensitivity and specificity were both above 80%. Conclusion:Both BA.5-S-IgG and WT-S-IgG, as well as the combination of these two antibodies, are of high value in the identification of epidemic strains.

Objective:To discuss the effect of exosome(Exo)microRNA-761(miR-761)on the epithelial-mesenchymal transition(EMT)process of the osteosarcoma(OS)cells by regulating tumor-associated macrophage(TAM)polarization,and to clarify its related mechanism.Methods:The miR-761 plasmid and negative control(miR-NC)plasmid were transfected into the HEK293 cells,and the non-transfected cells were regarded as control group.The transfection efficiency was detected using real-time fluorescence quantitative PCR(RT-qPCR)method.The Exo containing miR-761 was isolated,and the morphology of Exo was observed by transmission electron microscope.The concentration and size distribution of Exo samples were detected by nanoparticle analyzer,and the expression of Exo surface marker protein was detected by Western blotting method.The human monocyte leukemia THP-1 cells were stimulated with phorbol 12-myristate 13-acetate(PMA)to become the M0 macrophages,which were then treated with Exo containing miR-761 and co-cultured with the OS MG63 cells to establish the co-culture system.The experiment was divided into M0 group,TAM group,miR-761 NC group,and miR-761 Exo group.The M0 macrophages were collected from various groups,and the positive rates of M1 macrophage marker CD86 and M2 macrophage marker CD206 in various groups were detected by flow cytometry;the protein expression levels of M1 macrophage secreted factors interleukin-1β(IL-1β)and tumor necrosis factor-α(TNF-α)and M2 macrophage secreted factors interleukin-10(IL-10)and transforming growth factor-β1(TGF-β1)in various groups were detected by Western blotting method.The M0 macrophages were treated with Exo containing miR-761 and co-cultured with MG63 cells to establish the co-culture system.The experiment was divided into control group,TAM group,miR-NC Exo+TAM group,and miR-761 Exo+TAM group.The MG63 cells in various groups were collected,and the fluorescence intensities of E-cadherin and Vimentin in the MG63 cells in various groups were observed by immunofluorescence staining;the expression levels of E-cadherin,Vimentin,and EMT regulation-related transcription factors Twist1,Snail,and Slug proteins in the cells in various groups were detected by Western blotting method;the numbers of invasion and migration cells in various groups were detected by Transwell chamber assay.Results:The HEK293 cells containing miR-761 were successfully obtained by transfection experiments,and the Exo was isolated.Compared with M0 group,the positive rate of CD86 of the macrophages in TAM group was decreased(P＜0.05),while the positive rate of CD206 was increased(P＜0.05),the expression levels of IL-1β and TNF-α proteins were decreased(P＜0.05),while the expression levels of IL-10 and TGF-β1 proteins were increased(P＜0.05).Compared with TAM group,the positive rate of CD86 of the macrophages in miR-761 Exo group was increased(P＜0.05),while the positive rate of CD206 was decreased(P＜0.05),the expression levels of IL-1β and TNF-α proteins were increased(P＜0.05),while the expression levels of IL-10 and TGF-β1 proteins were decreased(P＜0.05).Compared with control group,the fluorescence intensity of E-cadherin in the MG63 cells in TAM group was decreased,while the fluorescence intensity of Vimentin was increased,the expression level of E-cadherin protein was decreased(P＜0.05),while the expression levels of Vimentin,Twist1,Snail,and Slug proteins were increased(P＜0.05),and the numbers of invasion and migration cells were increased(P＜0.05).Compared with TAM group,the fluorescence intensity of E-cadherin in the MG63 cells in miR-761 Exo+TAM group was increased,while the fluorescence intensity of Vimentin was decreased,the expression level of E-cadherin protein was increased(P＜0.05),while the expression levels of Vimentin,Twist1,Snail,and Slug proteins were decreased(P＜0.05),and the numbers of invasion and migration cells were decreased(P＜0.05).Conclusion:The exo-delivered miR-761 can inhibit the EMT process of the OS cells,thereby inhibiting the cell migration and cell invasion;its mechanism may be related to regulating TAM polarization.

Objective To establish a predictive model for digestive and respiratory infectious disease patients'selection of first-entry healthcare institutions and analyze the influencing factors,providing a reference for regional healthcare institution development.Methods The cluster sampling was used to select 1524 target patients.The data was randomly split 8∶2 into training and testing sets.The binary logistic regression method was employed to establish a predictive model for the selection of infectious disease patients'first choosing institution and analyze the influencing factors.The model's predictive performance was evaluated using the AUC of the testing set.Results It analyzed 1 524 infectious disease cases;417 chose community clinics first.Influences on their initial choice included education,insurance type,occupation,relative distance,and having a family doctor.The AUC of the test set was 0.851.Conclusion The predictive model of the selection of infectious disease patients'first choosing institution established in this study can be used to predict patient flow and help allocate medical resources reasonably.Increasing the rate of primary care for patients with infectious diseases in the region.

Objective To establish a predictive model for digestive and respiratory infectious disease patients'selection of first-entry healthcare institutions and analyze the influencing factors,providing a reference for regional healthcare institution development.Methods The cluster sampling was used to select 1524 target patients.The data was randomly split 8∶2 into training and testing sets.The binary logistic regression method was employed to establish a predictive model for the selection of infectious disease patients'first choosing institution and analyze the influencing factors.The model's predictive performance was evaluated using the AUC of the testing set.Results It analyzed 1 524 infectious disease cases;417 chose community clinics first.Influences on their initial choice included education,insurance type,occupation,relative distance,and having a family doctor.The AUC of the test set was 0.851.Conclusion The predictive model of the selection of infectious disease patients'first choosing institution established in this study can be used to predict patient flow and help allocate medical resources reasonably.Increasing the rate of primary care for patients with infectious diseases in the region.

Objective To establish a predictive model for digestive and respiratory infectious disease patients'selection of first-entry healthcare institutions and analyze the influencing factors,providing a reference for regional healthcare institution development.Methods The cluster sampling was used to select 1524 target patients.The data was randomly split 8∶2 into training and testing sets.The binary logistic regression method was employed to establish a predictive model for the selection of infectious disease patients'first choosing institution and analyze the influencing factors.The model's predictive performance was evaluated using the AUC of the testing set.Results It analyzed 1 524 infectious disease cases;417 chose community clinics first.Influences on their initial choice included education,insurance type,occupation,relative distance,and having a family doctor.The AUC of the test set was 0.851.Conclusion The predictive model of the selection of infectious disease patients'first choosing institution established in this study can be used to predict patient flow and help allocate medical resources reasonably.Increasing the rate of primary care for patients with infectious diseases in the region.

Objective To establish a predictive model for digestive and respiratory infectious disease patients'selection of first-entry healthcare institutions and analyze the influencing factors,providing a reference for regional healthcare institution development.Methods The cluster sampling was used to select 1524 target patients.The data was randomly split 8∶2 into training and testing sets.The binary logistic regression method was employed to establish a predictive model for the selection of infectious disease patients'first choosing institution and analyze the influencing factors.The model's predictive performance was evaluated using the AUC of the testing set.Results It analyzed 1 524 infectious disease cases;417 chose community clinics first.Influences on their initial choice included education,insurance type,occupation,relative distance,and having a family doctor.The AUC of the test set was 0.851.Conclusion The predictive model of the selection of infectious disease patients'first choosing institution established in this study can be used to predict patient flow and help allocate medical resources reasonably.Increasing the rate of primary care for patients with infectious diseases in the region.

Objective To establish a predictive model for digestive and respiratory infectious disease patients'selection of first-entry healthcare institutions and analyze the influencing factors,providing a reference for regional healthcare institution development.Methods The cluster sampling was used to select 1524 target patients.The data was randomly split 8∶2 into training and testing sets.The binary logistic regression method was employed to establish a predictive model for the selection of infectious disease patients'first choosing institution and analyze the influencing factors.The model's predictive performance was evaluated using the AUC of the testing set.Results It analyzed 1 524 infectious disease cases;417 chose community clinics first.Influences on their initial choice included education,insurance type,occupation,relative distance,and having a family doctor.The AUC of the test set was 0.851.Conclusion The predictive model of the selection of infectious disease patients'first choosing institution established in this study can be used to predict patient flow and help allocate medical resources reasonably.Increasing the rate of primary care for patients with infectious diseases in the region.