Objective To optimize an extracting technology for Fufang Baige capsule. Methods The orthogonal design was used for the inspection of water-extracting process and alcohol-precipitating process. Water volume, extracting time and extracting times were screened as inspection elements for water-extracting process. And liquid concentration, alcohol-precipitating concentration and incubating time were screened as inspection elements for alcohol-precipitating process. The transferring rate of paeoniflorin which is the indicator component in drug-Jun was screened as the inspection indicator for each fraction of extraction process. Results The optimal extraction conditions were as follows:for water- extracting process, that was extracting 2 times with 15-fold water, 2 hours for each time ; For alcohol-precipitating process, that was concentrating liquid to 1.0 g/mL Chinese herbs containing, then adding alcohol untill alcohol concentration ammouted to 70%, and at last putting it static for 18 hours. Conclusion The extracting technology was simple, economical and practical, and was able to increase production efficiency.

Objective To explore the application of diagnostic system with artificial intelligence(AI)in the evaluation of patients with coronary heart disease(CHD)at high altitude.Methods A total of 318 pa-tients underwent coronary CT angiography(CTA)at the hospital from January to December 2022 were pro-spectively collected.According to the altitude gradient,the patients were divided into the 2 000-3 000 m group and>3 000 m group.Coronary angiography(CAG)was used as the gold standard to verify the diag-nostic performance of AI diagnostic system.Coronary artery diagnosis system with AI technology and CT de-rived fractional flow reserve(CT-FFR)measurement system were used to evaluate the plaque structure char-acteristics and hemodynamic changes in the two groups of patients.Results Calcified plaques and vulnerable plaques in the>3 000 m group were more than those in the 2 000-3 000 m group(χ2=3.976,6.482,P= 0.046,0.011).The incidence of multi-vessel coronary artery disease,moderate stenosis,severe stenosis and complete occlusion in the>3 000 m group was higher than that in the 2 000-3 000 m group,and the inci-dence of single-vessel coronary artery disease and mild stenosis in the 2 000-3 000 m group was higher than that in the>3 000 m group(P<0.05).The incidence of CT-FFR≤0.80 and<0.70 in the>3 000 m group was higher than that in the 2 000-3 000 m group(χ2=4.782,28.118,P=0.029,<0.001).The comparison with the gold standard showed that this method has high sensitivity,specificity,and diagnostic consistency(P<0.001).Conclusion The coronary diagnosis system with AI technology has certain value in the system-atic evaluation of coronary artery characteristics and hemodynamic changes in CHD patients at high altitude.

Objective To utilize sophisticated CT-driven radiomics to prognosticate the mutation situation of KRAS in patients with colorectal cancer(CRC).Methods A total of 393 patients who underwent KRAS mutation testing and preoperative triphasic enhanced CT were analyzed retrospectively.All patients were divided into training group(n=276)and validation group(n=117)with a ratio of 7∶3.The characteristics tightly associated with KRAS mutation were extracted and screened to construct three models,include clinical,radiomics,and clinical-radiomics fusion models for prediction of KRAS mutation.The performance and clinical utility of these three models were assessed via receiver operating characteristic(ROC)curve and decision curve analysis(DCA).Results The study identified significant correlations between KRAS mutation and CEA,CA199,and a set of 13 radiomics features,respective-ly.Based on these clinical indicators and radiomics features,clinical,radiomics,and clinical-radiomics fusion models were constructed to prognosticate KRAS mutation.The radiomics model construc-ted in this study had good performance for the prediction of KRAS mutation status in CRC patients.Most notably,a clinical-radiomics nomogram that amalgamated both clinical risk factors and radiomics parameters emerged as the most effective predictor of KRAS mutation,with an area under the curve(AUC)of 0.782 and 0.744 in the training group and validation group,respectively.Conclusion The refined CT radiomics model serves as a robust,non-invasive,quantitative tool for the assessment of KRAS mutation status in CRC patients.