Objective To explore the characteristics,potential challenges,and optimization paths of high-quality development policies for public hospitals in China,and to provide reference for policy formulation and implementation.Methods The 31 policy documents on high-quality development of public hospitals issued at the central and provincial levels between 2021 and 2024 were coded using Nvivo 11 software,and a three-dimensional analytical framework was constructed on the basis of the policy tools,stakeholders,and system dimensions,combining both quantitative and qualitative methods for content analysis.Results It found that the distribution of policy content in the three dimensions was characterized by stage imbalance:policy tools were mainly environmental,followed by supply,and demand;stakeholder attention was focused on public hospitals and healthcare administrations,and patient attention was low;The system dimension focused mainly on the macro level,with less distribution at the meso and micro levels.Although the unbalanced distribution of policy instruments is relevant at certain stages,the long-term structural imbalance may lead to insufficient systemic policies and weakened stakeholder synergies,and there is an urgent need to optimize the structure of instruments.Conclusions It is recommended to increase the proportion of demand-based policy instruments in policy design and dynamically adjust the synergistic application of the three types of instruments;to strengthen the attention to vulnerable stakeholders,such as patients;and to optimize the design of policies at the meso-levels and micro-levels in order to enhance the systemicity and sustainability of policy implementation.

This study aims to elucidate the role and mechanism of clematichinenoside AR(CAR) in protecting bone marrow mesenchymal stem cells(BMSCs) from hypoxia-induced apoptosis. BMSCs were isolated by the bone fragment method and identified by flow cytometry. Cells were cultured under normal conditions(37℃, 5% CO_2) and hypoxic conditions(37℃, 90% N_2, 5% CO_2) and treated with CAR. The BMSCs were classified into eight groups: control(normal conditions), CAR(normal conditions + CAR), hypoxia 24 h, hypoxia 24 h + CAR, hypoxia 48 h, hypoxia 48 h + CAR, hypoxia 72 h, and hypoxia 72 h + CAR. The cell counting kit-8(CCK-8) assay and terminal-deoxynucleoitidyl transferase mediated nick end labeling(TUNEL) were employed to measure cell proliferation and apoptosis, respectively. The number of mitochondria and mitochondrial membrane potential were measured by MitoTracker®Red CM-H2XRo staining and JC-1 staining, respectively. The level of reactive oxygen species(ROS) was measured with the DCFH-DA fluorescence probe. The protein levels of B-cell lymphoma-2 associated X protein(BAX), caspase-3, and optic atrophy 1(OPA1) were determined by Western blot. The results demonstrated that CAR significantly increased cell proliferation. Compared with the control group, the hypoxia groups showed increased apoptosis rates, reduced mitochondria, elevated ROS levels, decreased mitochondrial membrane potential, upregulated expression of BAX and caspase-3, and downregulated expression of OPA1. In comparison to the corresponding hypoxia groups, CAR intervention significantly decreased the apoptosis rate, increased mitochondria, reduced ROS levels, elevated mitochondrial membrane potential, downregulated the expression of BAX and caspase-3, and upregulated the expression of OPA1. Therefore, it can be concluded that CAR may exert an anti-apoptotic effect on BMSCs under hypoxic conditions by regulating OPA1 to maintain mitochondrial homeostasis.
Mesenchymal Stem Cells/metabolism* ; Apoptosis/drug effects* ; Mitochondria/metabolism* ; Animals ; Rats ; Cell Hypoxia/drug effects* ; Homeostasis/drug effects* ; Reactive Oxygen Species/metabolism* ; Rats, Sprague-Dawley ; Membrane Potential, Mitochondrial/drug effects* ; Saponins/pharmacology* ; Caspase 3/genetics* ; Male ; bcl-2-Associated X Protein/genetics* ; Bone Marrow Cells/metabolism* ; Cell Proliferation/drug effects* ; Protective Agents/pharmacology* ; Cells, Cultured

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.

The minor purchasing process and mode of some hospital were introduced,and the implementation of the hospital's minor purchasing projects in the past year was analyzed.The causes for high failure rate of purchasing were pointed out including long interval between project creation and procurement,unreasonable demand presentation,insufficient demand demonstration and lack of active participation of suppliers.Some suggestions were put forward such as timely adjustment of demands,strengthening of demand demonstration,improvement of supplier motivation and enhancement of procurement process management,which were of great significance for increasing the success rate of minor purchasing of the hospital.[Chinese Medical Equipment Journal,2025,46(8):91-95]

The minor purchasing process and mode of some hospital were introduced,and the implementation of the hospital's minor purchasing projects in the past year was analyzed.The causes for high failure rate of purchasing were pointed out including long interval between project creation and procurement,unreasonable demand presentation,insufficient demand demonstration and lack of active participation of suppliers.Some suggestions were put forward such as timely adjustment of demands,strengthening of demand demonstration,improvement of supplier motivation and enhancement of procurement process management,which were of great significance for increasing the success rate of minor purchasing of the hospital.[Chinese Medical Equipment Journal,2025,46(8):91-95]

Objective:To establish a quality control method for Aiye standard decoction.Methods:The ultra performance liquid chromatogrphy (UPLC) column Waters ACQUITY HSS T3 C18 (2.1 mm×150 mm,1.8 μm) was used to gradient elution by acetonitrile and 0.1% formic acid in water. 16 batches of Aiye standard decoction fingerprints were established by UPLC and the common peaks were determined in the fingerprints. The contents of 12 components were determined. The 16 batches of Aiye standard decoction were analyzed by similarity calculation, hierarchical cluster analysis (HCA), principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) for analysis of differential components of Artemisiae Argyi Folium from different origins.Results:A total of 13 common peaks were marked in the fingerprints of 16 batches of Aiye standard decoction, 12 of which were identified by comparison with reference substance, including chlorogenic acid, sochlorogenic acid A, neochlorogenic acid, cryptochlorogenic acid, caffeic acid,1,3-O-Dicaffeoylquinic acid, schaftoside, isochlorogenic acid B,1,5-O-Dicaffeoylquinic acid, isochlorogenic acid C, jaceosidin and eupatilin. Similarity evaluation, PCA and HCA all classified the 16 batches of Aiye standard decoction into 2 categories. Orthogonal partial least squares discriminant analysis screened 5 differential biomarkers from 13 common peaks. The content determination results showed that the phenolic compounds and flavonoids in samples from Hubei were significantly higher than that in samples from other areas.Conclusion:This method can effectively analyze the differences in the quality of Aiye standard decoction from different origins, and provide reference for the formulation of quality standards for Aiye standard decoction and related preparations.

Objective To develop a predictive model for postoperative mortality risk in patients with acute aortic dissection(AAD)using the Extreme Gradient Boosting(XGBoost)algorithm combined with Shapley Additive Explanation(SHAP),and to establish a prediction website to serve as a diagnostic and therapeutic support platform for clinicians and patients.Methods A retrospective cohort study design was adopted.Data from 782 AAD patients who underwent surgical treatment at the Fourth Hospital of Hebei Medical University from January 2013 to December 2023 were collected,including basic information and initial serum biomarker test results.Patients were randomly divided into training and test sets at a 7:3 ratio.An external validation set consisting of 313 AAD patients admitted to the Second Hospital of Hebei Medical University from January 2020 to December 2023 was also established for further model validation.Variables were screened using LASSO regression,and an XGBoost machine learning model was constructed and interpreted using SHAP.The predictive performance of the model was evaluated using receiver operating characteristic(ROC)curve analysis.Using the Shiny package,the XGBoost model was deployed to shinyapps.io to create a prediction website for postoperative mortality risk in AAD patients.One patient was selected by simple random sampling from the test set and the external validation set respectively for the prediction example on the Shiny webpage.Results The XGBoost model demonstrated high predictive performance for postoperative mortality in AAD patients,with area under the ROC curve(AUC)values of 0.928(95%CI 0.901-0.956)in the training set,0.919(95%CI 0.891-0.949)in the test set,and 0.941(95%CI 0.915-0.967)in the external validation set.SHAP values indicated the following order of variable importance in the model(from highest to lowest):"lactate dehydrogenase""blood chlorine""multiple organ injury""carbon dioxide combining power""prothrombin time""α-hydroxybutyric acid""creatine kinase isoenzyme""Stanford classification""combined use of bedside blood purification""gender""acute kidney injury""gastrointestinal bleeding""brain injury"and"shock".A risk prediction website for adverse postoperative outcomes in AAD patients was developed using XGBoost-SHAP method(https://dun-dunxiaolu.shinyapps.io/document/)and validated with examples.One randomly selected patient from each of the test and external validation sets was applied:the predicted mortality risk value for patient 1(who died postoperatively)was 0.9539,and that for patient 2(who survived postoperatively)was 0.0206.Conclusions The XGBoost-SHAP model demonstrates high accuracy in predicting postoperative mortality risk for AAD patients.The online prediction tool established based on this model enhances the identification efficiency of high-risk postoperative mortality patients.

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.

Objective To explore the characteristics,potential challenges,and optimization paths of high-quality development policies for public hospitals in China,and to provide reference for policy formulation and implementation.Methods The 31 policy documents on high-quality development of public hospitals issued at the central and provincial levels between 2021 and 2024 were coded using Nvivo 11 software,and a three-dimensional analytical framework was constructed on the basis of the policy tools,stakeholders,and system dimensions,combining both quantitative and qualitative methods for content analysis.Results It found that the distribution of policy content in the three dimensions was characterized by stage imbalance:policy tools were mainly environmental,followed by supply,and demand;stakeholder attention was focused on public hospitals and healthcare administrations,and patient attention was low;The system dimension focused mainly on the macro level,with less distribution at the meso and micro levels.Although the unbalanced distribution of policy instruments is relevant at certain stages,the long-term structural imbalance may lead to insufficient systemic policies and weakened stakeholder synergies,and there is an urgent need to optimize the structure of instruments.Conclusions It is recommended to increase the proportion of demand-based policy instruments in policy design and dynamically adjust the synergistic application of the three types of instruments;to strengthen the attention to vulnerable stakeholders,such as patients;and to optimize the design of policies at the meso-levels and micro-levels in order to enhance the systemicity and sustainability of policy implementation.

AIM To evaluate the quality of Yanyangke Mixture.METHODS The HPLC fingerprints were established,after which cluster analysis,principal component analysis and partial least squares discriminant analysis were performed.The contents of liquiritin,rosmarinic acid,sheganoside,irisgenin,honokiol,monoammonium glycyrrhizinate,irisflorentin,isoliquiritin and magnolol were determined,the analysis was performed on a 35 ℃ thermostatic Agilent ZORBAX SB-C18 column(5 μm,250 mmx4.6 mm),with the mobile phase comprising of 0.1％phosphoric acid-acetonitrile flowing at 1 mL/min in a gradient elution manner,and multi-wavelength detection was adopted.RESULTS There were ten common peaks in the fingerprints for twelve batches of samples with the similarities of more than 0.9.Various batches of samples were clustered into three types,three principal components displayed the acumulative variance contribution rate of 87.448％,peaks 5、14(honokiol),3(liquiritin),11(monoammonium glycyrrhizinate)and 15(asarinin)were quality markers.Nine constituents showed good linear relationships within their own ranges(r＞0.999 0),whose average recoveries were 98.5％-103.6％with the RSDs of 0.92％-1.7％.CONCLUSION This stable and reliable method can provide a basis for the quality control of Yanyangke Mixture.