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 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 impact and cost-effectiveness of community stroke screening intervention mode on stroke risk.Methods:A total of 3 561 community people over 40 years old who participated in screening intervention in 2017,2019 and 2021 were selected as research objects,and stroke risk was divided into low risk,medium risk and high risk.A Markov model was established to explore the impact of screening intervention mode on stroke risk in community population.The cost increment during the phase I trial was calculated,and the life year increment was adjusted according to the quality estimate of previous studies.The cost-effectiveness increment ratio was calculated,and the screening intervention mode was evaluated,and univariate sensitivity analysis was performed.Results:Within a certain range,intervention screening could effectively shift the status of residents to the low-risk direction,and finally stabilize the distribution of low-risk,medium-risk and high-risk were 47.4％,31.0％and 21.6％.The incremental cost of interventional screening was 160 245 yuan,the incremental quality-adjusted life year was 151.129 yuan,and the incremental cost-effectiveness ratio(ICER)was 1 060.319 yuan/QALY,which was less than 1 times the per capita GDP.The intervention program was fully cost-effective.Conclusion:Screening intervention can promote the transformation of the commu-nity population to a low-risk state of stroke in the prevention stage,and this approach has good cost-effectiveness performance.It is recommended that the primary medical and health institutions that are not enough to fully implement the integrated process ser-vice of community prevention and treatment of stroke should first implement low-cost screening intervention.

Objective:To explore the impact and cost-effectiveness of community stroke screening intervention mode on stroke risk.Methods:A total of 3 561 community people over 40 years old who participated in screening intervention in 2017,2019 and 2021 were selected as research objects,and stroke risk was divided into low risk,medium risk and high risk.A Markov model was established to explore the impact of screening intervention mode on stroke risk in community population.The cost increment during the phase I trial was calculated,and the life year increment was adjusted according to the quality estimate of previous studies.The cost-effectiveness increment ratio was calculated,and the screening intervention mode was evaluated,and univariate sensitivity analysis was performed.Results:Within a certain range,intervention screening could effectively shift the status of residents to the low-risk direction,and finally stabilize the distribution of low-risk,medium-risk and high-risk were 47.4％,31.0％and 21.6％.The incremental cost of interventional screening was 160 245 yuan,the incremental quality-adjusted life year was 151.129 yuan,and the incremental cost-effectiveness ratio(ICER)was 1 060.319 yuan/QALY,which was less than 1 times the per capita GDP.The intervention program was fully cost-effective.Conclusion:Screening intervention can promote the transformation of the commu-nity population to a low-risk state of stroke in the prevention stage,and this approach has good cost-effectiveness performance.It is recommended that the primary medical and health institutions that are not enough to fully implement the integrated process ser-vice of community prevention and treatment of stroke should first implement low-cost screening intervention.

Objective:To explore the impact and cost-effectiveness of community stroke screening intervention mode on stroke risk.Methods:A total of 3 561 community people over 40 years old who participated in screening intervention in 2017,2019 and 2021 were selected as research objects,and stroke risk was divided into low risk,medium risk and high risk.A Markov model was established to explore the impact of screening intervention mode on stroke risk in community population.The cost increment during the phase I trial was calculated,and the life year increment was adjusted according to the quality estimate of previous studies.The cost-effectiveness increment ratio was calculated,and the screening intervention mode was evaluated,and univariate sensitivity analysis was performed.Results:Within a certain range,intervention screening could effectively shift the status of residents to the low-risk direction,and finally stabilize the distribution of low-risk,medium-risk and high-risk were 47.4％,31.0％and 21.6％.The incremental cost of interventional screening was 160 245 yuan,the incremental quality-adjusted life year was 151.129 yuan,and the incremental cost-effectiveness ratio(ICER)was 1 060.319 yuan/QALY,which was less than 1 times the per capita GDP.The intervention program was fully cost-effective.Conclusion:Screening intervention can promote the transformation of the commu-nity population to a low-risk state of stroke in the prevention stage,and this approach has good cost-effectiveness performance.It is recommended that the primary medical and health institutions that are not enough to fully implement the integrated process ser-vice of community prevention and treatment of stroke should first implement low-cost screening intervention.

Objective:To explore the impact and cost-effectiveness of community stroke screening intervention mode on stroke risk.Methods:A total of 3 561 community people over 40 years old who participated in screening intervention in 2017,2019 and 2021 were selected as research objects,and stroke risk was divided into low risk,medium risk and high risk.A Markov model was established to explore the impact of screening intervention mode on stroke risk in community population.The cost increment during the phase I trial was calculated,and the life year increment was adjusted according to the quality estimate of previous studies.The cost-effectiveness increment ratio was calculated,and the screening intervention mode was evaluated,and univariate sensitivity analysis was performed.Results:Within a certain range,intervention screening could effectively shift the status of residents to the low-risk direction,and finally stabilize the distribution of low-risk,medium-risk and high-risk were 47.4％,31.0％and 21.6％.The incremental cost of interventional screening was 160 245 yuan,the incremental quality-adjusted life year was 151.129 yuan,and the incremental cost-effectiveness ratio(ICER)was 1 060.319 yuan/QALY,which was less than 1 times the per capita GDP.The intervention program was fully cost-effective.Conclusion:Screening intervention can promote the transformation of the commu-nity population to a low-risk state of stroke in the prevention stage,and this approach has good cost-effectiveness performance.It is recommended that the primary medical and health institutions that are not enough to fully implement the integrated process ser-vice of community prevention and treatment of stroke should first implement low-cost screening intervention.

Objective:To explore the impact and cost-effectiveness of community stroke screening intervention mode on stroke risk.Methods:A total of 3 561 community people over 40 years old who participated in screening intervention in 2017,2019 and 2021 were selected as research objects,and stroke risk was divided into low risk,medium risk and high risk.A Markov model was established to explore the impact of screening intervention mode on stroke risk in community population.The cost increment during the phase I trial was calculated,and the life year increment was adjusted according to the quality estimate of previous studies.The cost-effectiveness increment ratio was calculated,and the screening intervention mode was evaluated,and univariate sensitivity analysis was performed.Results:Within a certain range,intervention screening could effectively shift the status of residents to the low-risk direction,and finally stabilize the distribution of low-risk,medium-risk and high-risk were 47.4％,31.0％and 21.6％.The incremental cost of interventional screening was 160 245 yuan,the incremental quality-adjusted life year was 151.129 yuan,and the incremental cost-effectiveness ratio(ICER)was 1 060.319 yuan/QALY,which was less than 1 times the per capita GDP.The intervention program was fully cost-effective.Conclusion:Screening intervention can promote the transformation of the commu-nity population to a low-risk state of stroke in the prevention stage,and this approach has good cost-effectiveness performance.It is recommended that the primary medical and health institutions that are not enough to fully implement the integrated process ser-vice of community prevention and treatment of stroke should first implement low-cost screening intervention.

Objective:To explore the impact and cost-effectiveness of community stroke screening intervention mode on stroke risk.Methods:A total of 3 561 community people over 40 years old who participated in screening intervention in 2017,2019 and 2021 were selected as research objects,and stroke risk was divided into low risk,medium risk and high risk.A Markov model was established to explore the impact of screening intervention mode on stroke risk in community population.The cost increment during the phase I trial was calculated,and the life year increment was adjusted according to the quality estimate of previous studies.The cost-effectiveness increment ratio was calculated,and the screening intervention mode was evaluated,and univariate sensitivity analysis was performed.Results:Within a certain range,intervention screening could effectively shift the status of residents to the low-risk direction,and finally stabilize the distribution of low-risk,medium-risk and high-risk were 47.4％,31.0％and 21.6％.The incremental cost of interventional screening was 160 245 yuan,the incremental quality-adjusted life year was 151.129 yuan,and the incremental cost-effectiveness ratio(ICER)was 1 060.319 yuan/QALY,which was less than 1 times the per capita GDP.The intervention program was fully cost-effective.Conclusion:Screening intervention can promote the transformation of the commu-nity population to a low-risk state of stroke in the prevention stage,and this approach has good cost-effectiveness performance.It is recommended that the primary medical and health institutions that are not enough to fully implement the integrated process ser-vice of community prevention and treatment of stroke should first implement low-cost screening intervention.

Objective:To explore the impact and cost-effectiveness of community stroke screening intervention mode on stroke risk.Methods:A total of 3 561 community people over 40 years old who participated in screening intervention in 2017,2019 and 2021 were selected as research objects,and stroke risk was divided into low risk,medium risk and high risk.A Markov model was established to explore the impact of screening intervention mode on stroke risk in community population.The cost increment during the phase I trial was calculated,and the life year increment was adjusted according to the quality estimate of previous studies.The cost-effectiveness increment ratio was calculated,and the screening intervention mode was evaluated,and univariate sensitivity analysis was performed.Results:Within a certain range,intervention screening could effectively shift the status of residents to the low-risk direction,and finally stabilize the distribution of low-risk,medium-risk and high-risk were 47.4％,31.0％and 21.6％.The incremental cost of interventional screening was 160 245 yuan,the incremental quality-adjusted life year was 151.129 yuan,and the incremental cost-effectiveness ratio(ICER)was 1 060.319 yuan/QALY,which was less than 1 times the per capita GDP.The intervention program was fully cost-effective.Conclusion:Screening intervention can promote the transformation of the commu-nity population to a low-risk state of stroke in the prevention stage,and this approach has good cost-effectiveness performance.It is recommended that the primary medical and health institutions that are not enough to fully implement the integrated process ser-vice of community prevention and treatment of stroke should first implement low-cost screening intervention.

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.