Objective:To compare the difference of chemical compositions of single decoction and mixed decoction of Gypsum Fibrosum - Anemarrhenae Rhizoma medicinal pair with different proportion.Methods:The Ultra Performance Liquid Chromatography (UPLC) and Ion Chromatography (IC) fingerprints of each sample were collected, and the independent sample t-test on the ratio of "peak area/weight" value of common peaks which was from the single decoction and mixed decoction samples was performed through SPSS 26.0.Results:In the UPLC fingerprint, there was significant difference in the "peak area/weight" values of peak 1, peak 2, peak 3, peak 6 and peak 13 between mixed decoction and single decoction with different compatibility ratios (1:1, 3:2, 2:1, 5:2, 3:1)( P<0.05), while peak 5 and peak 11 were without significant difference ( P>0.05). When the compatibility ratio of Gypsum Fibrosum and Anemarrhenae Rhizoma was 1:1, 3:2, 5:2, 3:1, there was significant difference in the "peak area/weight" values of peak 7 (neomangiferin) ( P<0.05). In the IC fingerprint, there was significant difference in the "peak area/weight" values of peak 1 between mixed decoction and single decoction with different compatibility ratios (1:1, 3:2, 2:1, 5:2, 3:1) ( P<0.05), while there was no significant difference in peak 5 (calcium ion) ( P>0.05). Conclusion:There are differences in the chemical component content of Gypsum Fibrosum-Anemarrhenae Rhizoma medicinal pair for single decoction and mixed decoction.

Objective     To identify the preoperative risk factors for prolonged mechanical ventilation (PMV) after pulmonary thromboendarterectomy (PTE). Methods    The clinical data of patients who underwent PTE from December 2016 to August 2021 in our hospital were retrospectively analyzed. The patients were divided into two groups according to the postoperative mechanical ventilation time, including a postoperative mechanical ventilation time≤48 h group (≤48 h group) and a postoperative mechanical ventilation time>48 h (PMV) group (>48 h group). Univariable and logistic regression analysis were used to identify the preoperative risk factors for postoperative PMV. Results    Totally, 90 patients were enrolled in this study. There were 40 patients in the ≤48 h group, including 30 males and 10 females, with a mean age of 45.48±12.72 years, and there were 50 patients in the >48 h group, including 29 males and 21 females, with a mean age of 55.50±10.42 years. The results showed that in the ≤48 h group, the median postoperative ICU stay was 3.0 days, and the median postoperative hospital stay was 15.0 days; in the >48 h group, the median postoperative ICU stay was 7.0 days, and the median postoperative hospital stay was 20.0 days. The postoperative PMV was significantly correlated with tricuspid annular plane systolic excursion (TAPSE) [OR=0.839, 95%CI (0.716, 0.983), P=0.030], age [OR=1.082, 95%CI (1.034, 1.132), P=0.001] and pulmonary vascular resistance (PVR) [OR=1.001, 95%CI (1.000, 1.003), P=0.028]. Conclusion    Age and PVR are the preoperative risk factors for PMV after PTE, and TAPSE is the preoperative protective factor for PMV after PTE.

Objective     To construct a radiomics model for identifying clinical high-risk carotid plaques. Methods     A retrospective analysis was conducted on patients with carotid artery stenosis in China-Japan Friendship Hospital from December 2016 to June 2022. The patients were classified as a clinical high-risk carotid plaque group and a clinical low-risk carotid plaque group according to the occurrence of stroke, transient ischemic attack and other cerebrovascular clinical symptoms within six months. Six machine learning models including eXtreme Gradient Boosting, support vector machine, Gaussian Naive Bayesian, logical regression, K-nearest neighbors and artificial neural network were established. We also constructed a joint predictive model combined with logistic regression analysis of clinical risk factors. Results    Finally 652 patients were collected, including 427 males and 225 females, with an average age of 68.2 years. The results showed that the prediction ability of eXtreme Gradient Boosting was the best among the six machine learning models, and the area under the curve (AUC) in validation dataset was 0.751. At the same time, the AUC of eXtreme Gradient Boosting joint prediction model established by clinical data and carotid artery imaging data validation dataset was 0.823. Conclusion     Radiomics features combined with clinical feature model can effectively identify clinical high-risk carotid plaques.