Objective: To establish HPLC fingerprints of different parts of chicory stems, leaves, roots, flowers and seeds, and compare the similarities and differences of chemical components in different parts, so as to provide a scientific basis for the comprehensive utilization of chicory. Methods: To establish the HPLC fingerprint of chicory, the chromatographic column was chosen with Agilent ZORBAX Eclipse XDB-C

OBJECTIVE： To establish HPLC fingerprints of Paeonia tactilora decoction pieces， and to conduct its cluster analysis and principal component analysis. METHODS： HPLC method was adopted. The determination was performed on SunFire® C18 column with mobile phase consisted of acetonitril-0.05％ phosphoric acid solution （gradient elution） at the flow rate of 1.0 mL/min. The detection wavelength was set at 230 nm， the column temperature was 30 ℃， the collection time was 70 min，and sample size was 15 μL. Using paeoniflorin as reference， HPLC fingerprints of 26 batches P. tactilora decoction pieces from different habitats and 30 batches by different processed methods were established. The similarity of samples was evaluated by TCM Chromatographic Fingerprint Similarity Evaluation System （2012 edition） to confirm common peak. Cluster analysis and principal component analysis were performed by using SPSS 20.0 software. RESULTS： There were 9 common peaks in HPLC fingerprints of 26 batches of sample from different habitats， the similarity of which was higher than 0.880. Six peaks were identified， including gallic acid， catechin， albiflorin， paeoniflorin， 1，2，3，4，6-pentagalloylglucose and benzoylpaeoniflorin. Cluster analysis showed that 26 batches of samples were clustered into 2 categories when cosine distance was 15. S1-S21 were clustered into one category； S22-S26 were clustered into the other category. By principal component analysis， the accumulative contribution rate of two main components was 81.124％. There were 10 common peaks in HPLC fingerprints of 30 batches of sample by different processed methods， the simi- larity of which was higher than 0.970. Seven peaks were identified， including gallic acid， catechin， aplopaeonoside， albiflorin， paeoniflorin， 1，2，3，4，6-pentagalloylglucose and benzoylpaeoniflorin. Cluster analysis showed that 30 batches of samples were clustered into 2 categories when cosine distance was 25. B1-B10 were clustered into one category； C1-C10 and J1-J10 were clustered into the other category. By principal component analysis， the accumulative contribution rate of four main components was 86.887％. CONCLUSIONS： Established HPLC fingerprint， the results of cluster analysis and principal component analysis can provide reference for quality control of decoction pieces of P. tactilora.

【Objective】 To explore the risk factors for platelet transfusion refractoriness(PTR)in patients with hematological disorders, construct a prediction model and validate the model efficacy. 【Methods】 Patients with hematological disorders who received platelet transfusion therapy in the Chengdu Second People′s Hospital from December 2021 to December 2022 were retrospectively included to judge the effectiveness of platelet transfusion and screened for risk factors by univariate and multivariate logistic regression. A prediction model for PTR was constructed using receiver operating characteristic(ROC) curve, calibration curve and decision curve(DCA) to assess the differentiation, calibration and clinical value of the model, respectively. 【Results】 A total of 334 hematological patients were included, including 168 males and 176 females, with a PTR incidence of 40.4%. Univariate and multivariate logistic regression analysis showed that platelet transfusion volume, erythrocyte transfusion volume, and neutrophil ratio were risk factors for PTR(P<0.05). A prediction model for PTR in hematological patients was established based on these risk factors. The area under the model′s curve was 0.8377(95% CI: 0.723-0.772), the sensitivity was 58.52%, and the specificity was 89.95%. The calibration curve showed that the S∶P was 0.964, the maximum absolute difference Emax was 0.032, and the average absolute difference Eavg was 0.009. The DCA analysis showed that the model had clinical application value when the risk threshold ranged from 0.2 to 0.9. 【Conclusion】 The PTR prediction model based on platelet transfusion volume, erythrocyte transfusion volume and neutrophil ratio can provide a basis for effective platelet transfusion in hematological patients.