OBJECTIVE：To esta blish a UPLC fingerprint of Pyrrosia petiolosa from southwest China ，and to determine the contents of 4 kinds of phenolic acids （neochlorogenic acid ，caffeic acid ，chlorogenic acid and cryptochlorogenic acid ）. METHODS：The determination was performed on Waters Cortecs T 3 C18 column（100 mm×2.1 mm，1.6 μm）with mobile phase consisted of methanol- 0.1％ phosphoric acid （gradient elution ）at the flow rate of 0.35 mL/min. The detection wavelength was set at 326 nm. The column temperature was 30 ℃，and injection volume was 1 μL. UPLC method was used to establish the UPLC fingerprint of P. petiolosa in combination with the Similarity Evaluation System of TCM Chromatographic Fingerprints （2012 edition）. Cluster analysis and principle component analysis （PCA）were performed by using SPSS 20.0 software. The contents of 4 kinds of phenolic acids in 20 batches of P. petiolosa were determined by external standard method. RESULTS ：There were 9 common peaks for the UPLC fingerprint of P. petiolosa . Peaks 1，3，4，5 and 9 were identified as neochlorogenic acid ，caffeic acid，chlorogenic acid ，cryptochlorogenic acid and isochlorogenic acid C ，respectively. RSDs of the relative retention time of each peak in different batches of P. petiolosa were 0-0.68％，and the RSDs of the relative peak area were 0-62.35％. The similarities between the fingerprint of 20 batches of medicinal materials and the control chromatogram were not less than 0.990. The result of cluster analysis showed that P. petiolosa from different regions could be sorted into three species. Results of PCA showed the differences among P. petiolosa from different regions. The linear range of neochlorogenic acid ，caffeic acid ，chlorogenic acid and cryptochlorogenic acid were 0.61-61.41，0.18-17.60，2.00-200.11，0.62-61.51 μ g/mL （R2＞0.999 9）. RSDs of precision ， reproducibility and stability tests were all lower than 2.00％. The recoveries were 96.23％-98.17％（RSD＝0.96％-2.28％， n＝6）. Among 20 batches of samples ，the contents of above 4 kinds of phenolic acids were 0.385 3-1.891 9，0.018 0-0.129 5，2.569 5-10.676 0，0.563.5-1.860 5 mg/g. CONCLUSIONS ： The established UPL C fingerprint could reflect the main chemical constituents of P. pedunculata . Phenolic acids could be used as the main evaluation indexes for the quality of P. petiolosa . The quality order of P. petiolosa from southwest China was Chongqing product＞Sichuan product ＞Guizhou product.

OBJECTIVE：To provide reference for the identification of Chebulae Fructus and Chebulae Fructus Immaturus . METHODS：UPLC method was adopted. The determination was performed on Waters Cortecs T 3 C18 column with mobile phase consisted of acetonitrile- 0.2％ phosphoric acid solution （gradient elution ）at the flow rate of 0.35 mL/min. The column temperature was 30 ℃，and the detection wavelength was set at 270 nm. The sample size was 1 μL. Using gallic acid as reference，UPLC fingerprints of 17 batches of Chebulae Fructus and 14 batches of Chebulae Fructus Immaturus were established and their similarity was evaluated by TCM Chromatographic Fingerprint Similarity Evaluation System （2012 edition）. By comparing substance control ， UV absorption spectrum and related literaturs ，common peaks were identified. PCA and PLS-DA were performed by using SPSS 20.0 and SIMCA 14.1 software. The contents of main difference components in Chebulae Fructus and Chebulae Fructus Immaturus were determined by above UPLC method and compared. RESULTS ：There were 8 common peaks in UPLC fingerprint of Chebulae Fructus and Chebulae Fructus Immaturus ，i.e. chebulic acid （peak 1），gallic acid （peak 2），punicalagin A （peak 3），punicalagin B （peak 4），corilagin（peak 6），chebulagic acid （peak 7）and chebulinic acid （peak 8）. The similarities of 17 batches of Chebulae Fructus were from 0.92 to 0.99，while 14 batches of Chebulae Fructus Immaturus were all above 0.99. The similarity of control fingerprint between Chebulae Fructus and Chebulae Fructus Immaturus was 0.909. PCA demonstrated the differences between Chebulae Fructus and Chebulae Fructus Immaturus . The results of PLS-DA were consistent with those of PCA ，and the variable importance in projection （VIP）values of peak 5，4，7，3 and 2 were above 1 in the PLS-DA model. In 31 batches of samples ，the contents of gallic acid （peak 2），punicalagin A（peak 3），punicalagin B （peak 4）and chebulagic acid （peak 7）were 2.63-10.31， 5.37-44.63，8.02-60.77，44.07-162.98 mg/g；RSDs were 40.14％， 47.91％ ，53.97％ ，36.22％（n＝31）. There was statistical significance in the differences of the mentioned 4 components between Chebulae Fructus and Chebulae Fructus Immaturus 719412818@qq.com （P＜0.05）. CONCLUSIONS ：There are significant differences between Chebulae Fructus and Chebulae Fructus Immaturus gallic acid ，punicalagin A ，punicalagin B and chebulagic acid are the main difference components for identification.

Objective:To compare the prognostic values of different classification by using transpulmonary pressure gradient (TPG), diastolic pressure gradient (DPG) and pulmonary vascular resistance (PVR) in patients with pulmonary hypertension due to left heart disease (PH-LHD), and investigated hemodynamic and clinical factors associated with mortality in patients with PH-LHD.Methods:This was a single-center prospective cohort study. In-hospital patients diagnosed with PH-LHD via right heart catheterization at the Department of Cardiology, the First Affiliated Hospital of Nanjing Medical University, from September 2013 to December 2019 were enrolled. Patients were divided according to TPG (cutoff value 12 mmHg; 1 mmHg=0.133 kPa), DPG (cutoff value 7 mmHg), PVR (cutoff value 3 Wood Units), and the combination of TPG and PVR. Baseline characteristic was recorded. All patients were followed up until the occurrence of endpoint event, defined as all-cause death that occurred during the follow-up period, or until April 18, 2022. Receiver operating characteristic curves were used to compare the predictive value of 3 classification methods for all-cause death in PH-LHD patients. The optimal cutoff values were calculated using Jorden index. Survival analysis was performed using Kaplan-Meier analysis, and log-rank test was used to compare the predictive efficacy of classification methods based on optimal cutoff values or guidance-recommended thresholds for the survival of PH-LHD patients. Variables showing statistical significance in the univariate analysis were incorporated into multivariate Cox regression model to analyze the independent risk factors for all-cause mortality.Results:A total of 243 patients were enrolled, aged (54.9±12.7) years old, including 169 (69.5%) males. During a median follow-up of 57 months, there were 101 (41.6%) deaths occurred. Grouping results were as follows: (1) TPG: TPG≤12 mmHg group 115 patients, TPG>12 mmHg group 128 patients; (2) DPG: DPG<7 mmHg group 193 patients, DPG≥7 mmHg group 50 patients; (3) PVR: PVR≤3 Wood Units group 108 patients, PVR>3 Wood Units group 135 patients; (4) TPG and PVR: TPG≤12 mmHg and PVR≤3 Wood Units group 89 patients, TPG>12 mmHg and PVR>3 Wood Units group 109 patients. PVR ( AUC=0. 698,95% CI:0.631-0.766) had better predictive value for all-cause mortality than TPG ( AUC=0.596, 95% CI: 0.523-0.669) and DPG ( AUC=0.526, 95% CI: 0.452-0.601) (all P<0.05). The optimal cutoff values for TPG, DPG, and PVR were13.9 mmHg, 2.8 mmHg, and 3.8 Wood Units, respectively. Kaplan-Meier analysis based on the optimal cutoff values or guidance-recommended thresholds showed that PVR and TPG were the predictors of survival ( P<0.05), while DPG did not showed significance ( P>0.05). Multivariate Cox regression analysis showed that age, PVR and log 2N-terminal pro-B-type natriuretic peptide were independent risk factors for all-cause mortality in PH-LHD patients (all P<0.05). Conclusion:Classification according to PVR was most valuable in predicting all-cause death in PH-LHD patients, while TPG showed moderate predictive ability and DPG had no predictive value.