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.

This study aimed to characterize and identify the non-volatile components in aqueous and ethanolic extracts of the stems and leaves of Rhododendron tomentosum by using sensitive and efficient ultra-performance liquid chromatography-quadrupole-time of flight mass spectrometry(UHPLC-Q-TOF-MS) combined with a self-built information database. By comparing with reference compounds, analyzing fragment ion information, searching relevant literature, and using a self-built information database, 118 compounds were identified from the aqueous and ethanolic extracts of R. tomentosum, including 35 flavonoid glycosides, 15 phenolic glycosides, 12 flavonoids, 7 phenolic acids, 7 phenylethanol glycosides, 6 tannins, 6 phospholipids, 5 coumarins, 5 monoterpene glycosides, 6 triterpenes, 3 fatty acids, and 11 other types of compounds. Among them, 102 compounds were reported in R. tomentosum for the first time, and 36 compounds were identified by comparing them with reference compounds. The chemical components in the ethanolic and aqueous extracts of R. tomentosum leaves and stems showed slight differences, with 84 common chemical components accounting for 71.2% of the total 118 compounds. This study systematically characterized and identified the non-volatile chemical components in the ethanolic and aqueous extracts of R. tomentosum for the first time. The findings provide a reference for active ingredient research, quality control, and product development of R. tomentosum.
Rhododendron/chemistry* ; Chromatography, High Pressure Liquid/methods* ; Drugs, Chinese Herbal/chemistry* ; Mass Spectrometry/methods* ; Plant Leaves/chemistry*

BACKGROUND AND OBJECTIVE: Hypertension (HT) and atrial fibrillation (AF) are highly prevalent cardiovascular conditions that frequently coexist. Coronary artery disease (CAD) is a major global cause of mortality. The co-occurrence of HT, AF, and CAD presents significant management challenges. This study aims to explore the clinical characteristics and risk factors associated with CAD in patients with HT and persistent AF (HT-AF). METHODS: In this retrospective cross-sectional study, data were collected from 384 hospitalized HT-AF patients at the People's Liberation Army General Hospital between January 2010 and December 2019. CAD diagnosis was confirmed by coronary angiography or computed tomography angiography. Clinical characteristics and comorbidities were compared between patients with and without CAD. Multivariate logistic regression analyses were performed to identify independent risk factors associated with CAD development. RESULTS: The prevalence of CAD among HT-AF patients was 66.41% (255/384). Cardiovascular complications, particularly heart failure (44.7% vs 25.6%, P < 0.05), were significantly more prevalent in the CAD group than in the non-CAD group. Only age was identified as an independent risk factor for CAD (adjusted OR: 1.047; 95% CI: 1.022-1.073; P = 0.000). Of all HT-AF patients, 54.7% had a CHA₂DS₂-VASc score of ≥4, indicating high stroke risk. There was a slightly higher anticoagulant usage rate in the CAD group than those without CAD (8.6% vs 4.7%, P = 0.157), and the overall anticoagulant usage remained low. CONCLUSION: There is a high prevalence of CAD among hospitalized HT-AF patients, among whom age is the sole independent risk factor for CAD. Despite a high stroke risk, the utilization of oral anticoagulants is alarmingly low.
Humans ; Atrial Fibrillation/epidemiology* ; Coronary Artery Disease/etiology* ; Hypertension/epidemiology* ; Male ; Female ; Risk Factors ; Middle Aged ; Retrospective Studies ; Cross-Sectional Studies ; Aged ; Prevalence

Objective:To determine whether the Rb1 of ginsenoside has protective effects on PA induced oxidative stress in endothelial cells.Methods:Established a model of palmitic acid-induced oxidative stress injury in human umbilical vein endothelial cells(HUVECs).Using MTT assay,flow cytometry,fluorescent probe staining,and Western blot analysis to detect whether Rb1 of ginsenoside has effects on the cell viability,apoptosis rate,ROS and NO production,mitochondrial membrane potential,and the expression levels of related proteins.Results:MTT assay and flow cytometry revealed that ginsenoside Rb1 can reduce PA-induced apoptosis in HUVECs(P＜0.05).The mechanism may be related to the following two points:(1)reducing ROS production and increasing NO levels,thereby enhancing the antioxidant capacity of HUVECs;(2)regulating the expression of Bcl-2 family proteins,increasing the BCL-2/Bax ratio(P＜0.05),modulating mitochondrial membrane permeability,reducing cytochrome C release(P＜0.001),and decreasing Caspase protein activation(P＜0.01),thereby attenuating PA-induced apoptosis.Conclusion:After the stimulation with PA,ROS production in human umbilical vein endothelial cells increased while NO content and cell activity decreased,oxidative stress induced apoptosis in cells.By regulating the production of ROS and NOx stabilizing the mitochondrial transmembrane potential,reducing the leakage of cytochrome C,Ginsenoside Rb1 can reduce HUVECs apoptosis induced by PA.

Objective:To determine whether the Rb1 of ginsenoside has protective effects on PA induced oxidative stress in endothelial cells.Methods:Established a model of palmitic acid-induced oxidative stress injury in human umbilical vein endothelial cells(HUVECs).Using MTT assay,flow cytometry,fluorescent probe staining,and Western blot analysis to detect whether Rb1 of ginsenoside has effects on the cell viability,apoptosis rate,ROS and NO production,mitochondrial membrane potential,and the expression levels of related proteins.Results:MTT assay and flow cytometry revealed that ginsenoside Rb1 can reduce PA-induced apoptosis in HUVECs(P＜0.05).The mechanism may be related to the following two points:(1)reducing ROS production and increasing NO levels,thereby enhancing the antioxidant capacity of HUVECs;(2)regulating the expression of Bcl-2 family proteins,increasing the BCL-2/Bax ratio(P＜0.05),modulating mitochondrial membrane permeability,reducing cytochrome C release(P＜0.001),and decreasing Caspase protein activation(P＜0.01),thereby attenuating PA-induced apoptosis.Conclusion:After the stimulation with PA,ROS production in human umbilical vein endothelial cells increased while NO content and cell activity decreased,oxidative stress induced apoptosis in cells.By regulating the production of ROS and NOx stabilizing the mitochondrial transmembrane potential,reducing the leakage of cytochrome C,Ginsenoside Rb1 can reduce HUVECs apoptosis induced by PA.

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 provide a detailed report and interpretation of the method and results for determining the weights of the technical indicators from the “multi-dimensional and multi-criteria comprehensive evaluation index system （first edition）” stated in Guideline for Multi-dimensional and Multi-criteria Comprehensive Evaluation of Chinese Patent Medicine. METHODS Normalization calculations were performed on the comprehensive weight values calculated by the analytic hierarchy process and expert weighting method to obtain the objective weights of the indicators. RESULTS The weight results of the six primary dimensions in the current comprehensive evaluation indicator system of Chinese patent medicine showed effectiveness dimension＞ safety dimension＞standard dimension＞application dimension＞scientific dimension＞economic dimension， with weight values of 0.281 0， 0.268 5， 0.195 8， 0.107 3， 0.096 1 and 0.051 3 respectively， consistent with the results of most researches currently. CONCLUSIONS The process of weight determination in this indicator system is scientifically reasonable， with clear methods and clear interpretations， and is worthy of further optimization and widespread application.

BACKGROUND:The traditional fixation method for femoral neck fractures is three hollow screws inverted triangle fixation,and the optimal fixation method for femoral neck fractures that have not achieved anatomical reduction is inconclusive. OBJECTIVE:To compare the biomechanical properties of cannulated screws internal fixation for sub-capitated femoral neck fracture with different reduction qualities based on finite element analysis. METHODS:The three-dimensional digital model was reconstructed using CT data of the proximal femur from a healthy male volunteer.The femur was modeled to sub-capitated femoral neck fractures.Fracture models were divided into anatomical reduction group,coxa vara group,and coxa valgus group.All fracture model groups were transferred using the standard group,screw depression group,and screw elevation group.A vertical downward stress of 1 400 N was applied to the femoral head at the top of the acetabulum.The displacement and stress distribution of the femur and internal fixator under different fixation methods were observed,and the maximum stress and displacement of the femur and fixator were compared. RESULTS AND CONCLUSION:(1)For anatomical reduction femoral neck fracture,the peak stress of fixation in the standard group,screw depression group and screw elevation group was 41.35,31.27 and 43.32 MPa,respectively.The maximum peak stress of the femur was found on the screw elevation group(28.58 MPa),and the standard group had the maximum peak displacement.(2)During hip varus,the stresses in the three subgroups were relatively dispersed and even.The peak stress of the femur in the standard group was the smallest,but the peak displacement was the largest.The stability of fixation might be poor.The peak displacement of the femur in the screw depression group was the smallest.(3)In the hip valgus,obvious screw stress concentration appeared in the screw depression group,and the peak displacement was the largest among the three subgroups,and an in-out-in phenomenon appeared.The peak stress of the screws in the screw elevation group was the largest among the three subgroups,but the peak displacement was the smallest.(4)It is concluded that for sub-capitated femoral neck fractures that are completely anatomically reduced,it is recommended to use standard inverted triangular nails for fixation.When the hip varus and hip valgus occur within the allowable range of the reduction standard,it is recommended to use the inverted triangle screw to fix it by rotating the corresponding angle in the same direction as the hip varus or valgus.

The clinical practice guidelines of traditional Chinese medicine （TCM） have problems such as limited clinical application and unclear implementation effects， which may be related to the lack of clinical practice evidence. To provide reliable and precise evidence for clinical practice， this article proposes a model of combining TCM guidelines with real-world study， which includes 4 steps. Firstly， during the implementation process of the guidelines， a high-quality research database is established. Secondly， the recommendations in the guidelines are evaluated based on the established database in multiple dimensions， including applicability， effectiveness， safety， and cost-effectiveness， and thus their effectiveness in practical applications can be determined. Thirdly， based on the established database， core prescriptions are identified， and the targeted populations and medication plans are determined. That is， the best treatment regimen is established based on the analysis of abundant clinical data regarding the effects of different medication frequencies， dosages， and duration on efficacy. Fourthly， the guidelines are updated according to the real-world evidence. The research based on this model can provide real-world evidence for ancient and empirical prescriptions， improving their application in clinical practice. Moreover， this model can reduce research costs and improve research efficiency. When applying this model， researchers need to pay attention to the quality of real-world evidence， ensuring that it can truly reflect the situation in clinical practice. In addition， importance should be attached to the clinical application of guideline recommendations， ensuring that doctors can conduct standardized diagnosis and treatment according to the guidelines. Finally， full-process participation of multidisciplinary experts is encouraged to ensure the comprehensiveness and scientificity of the study. In conclusion， the application of this model will contribute to the development of TCM guidelines responsive to the needs of clinical practice and achieve the goal of promoting the homogenization of TCM clinical diagnosis and treatment.

The classification of traditional Chinese medicine （TCM） syndromes is one of the core technical elements in the industry standard of Specification of Diagnosis and Therapeutic Effect Evaluation of Diseases and Syndromes in TCM. In the past，when clinical standards for TCM were formulated，the determination of TCM syndrome classification relied heavily on textbooks and expert experience，lacking systematic research. This approach thus failed to reflect the advancement and scientificity of the standards，thereby affecting their implementation and application. This article reviewed the presentation forms and technical methods of TCM syndrome classification，including the two-tier syndrome classification model with primary and secondary symptoms，as well as the application of modern literature research，ancient literature research，Delphi method，in-depth expert interviews，consensus conferences，and real-world research. When syndrome classification standards are developed，it is necessary to build upon modern literature research，adopt a mixed approach combining qualitative research and quantitative analysis results，and reach expert consensus through consensus conferences. Through systematic research，the scientificity，applicability，and coordination of TCM syndrome classification standards can be enhanced，providing guidance for the standardization of TCM.