OBJECTIVE To establish the fingerprint of Pinghuo tea standard decoction and a method for determination of multi-component to clarify the transfer relationship of quantities and quality from pieces and standard decoction. METHODS Fifteen batches of Pinghuo tea standard decoction were prepared and the extract rate was determined； the fingerprint of the preparation was established by using high-performance liquid chromatography（HPLC）； the similarity evaluation and the determination of common peaks were performed， and chemometric analysis was performed； the same method was used to determine the content of indicator components and the transfer rate was calculated. The chromatographic column was Venusil C18 column with mobile phase consisted of acetonitrile-0.1% phosphoric acid solution （gradient elution）； the column temperature was 30 ℃， and the detection wavelengths were 238 nm （0-37 min， 85-102 min） and 330 nm （37-85 min） at a flow rate of 1.0 mL/min with an injection volume of 10 μL. RESULTS The similarity of HPLC fingerprints for 15 batches of Pinghuo tea standard decoction was not lower than 0.968. A total of 24 common peaks were calibrated and 9 peaks were recognized， which were as follows neochlorogenic acid （peak 3）， chlorogenic acid （peak 6）， geniposide （peak 9）， glycyrrhizin （peak 10）， galuteolin （peak 11）， isochlorogenic acid A （peak 14）， luteolin （peak 21）， kaempferol （peak 23） and glycyrrhizic acid （peak 24）. Cluster analysis， principal component analysis and orthogonal partial least squares discriminant analysis showed consistent results， all of which could classify the 15 batches of samples into three categories. The linear range of indicator components in 15 batches of Pinghuo tea standard decoction， such as geniposide， luteolin， isochlorogenic acid A， glycyrrhizin， and glycyrrhizic acid， were 0.020 580-0.411 600， 0.001 617-0.080 850， 0.006 076-0.607 600， 0.005 125-0.071 740， and 0.017 288-0.432 200 mg/mL， respectively； RSDs of precision， repeatability， stability and recovery rate tests were all not higher than 4% （n＝6）. The mass fractions ranged 3.227 9-10.002 2， 0.297 4-0.554 6， 3.350 1-6.159 6， 0.720 6-1.073 3， 2.003 1-3.030 1 mg/g； transfer rates from the pieces and standard decoction were 19.762 8%-35.840 5%， 12.123 3%-21.254 0%， 46.097 2%-82.869 4%， 58.708 8%-91.629 6%， 39.114 3%-63.710 6%. The transfer rates of the extract from 15 batches of Pinghuo tea standard decoction ranged from 61.15%-84.68%. CONCLUSIONS Established HPLC fingerprint and content determination methods in this study are simple and accurate， which can provide reference for the quantitative value transfer study， quality control， clinical application and the development of subsequent formulations of Pinghuo tea standard decoction.

ObjectiveTaking Achyranthis Bidentatae Radix（ABR） from different origins as samples， to quantitatively analyze the chemical composition and chromaticity of ABR with different processing degrees， and clarify the correlation and change law between color and composition in the processing process of ABR， so as to provide reference for the quality evaluation of processed products of ABR. MethodThe colorimeter is used to measure the chromaticity values of three kinds of processing degrees of ABR in different origins to show the color value change trend during the processing process， and the color parameters of wine-processed and salt-processed products of ABR with different processing degrees were analyzed by principal component analysis（PCA）， orthogonal partial least squares-discriminant analysis（OPLS-DA） and other analysis methods. The contents of eight representative components of ABR were measured by high performance liquid chromatography（HPLC）， the correlation between chromaticity and each representative component was analyzed by Pearson correlation analysis， and the applicability of the selected eight representative components was further verified by Fisher linear discriminant analysis， and the wine-processed and salt-processed products of ABR with different processing degrees were grouped according to the degree of processing， and 48 samples of wine-processed and salt-processed products with different processing degrees were used as training samples. Taking the contents of 5-hydroxymethylfurfural， polypodine B， β-ecdysterone， 25R-inokosterone， 25S-inokosterone， ginsenoside Ro， chikusetsusaponin Ⅳa and polysaccharides as variables， the discriminant function was established respectively， and 12 samples of wine-processed and salt-processed products of ABR with different processing degrees were back-tested to verify the discriminant function and test the reliability of the function. ResultPCA and OPLS-DA results showed that ABR samples with different processing degrees were classified into clusters， and the results could significantly distinguish different processed products. During the process of wine and salt processing， the contents of 5-hydroxymethylfurfural， ginsenoside Ro_， and chikusetsusaponin Ⅳa gradually increased with the deepening of the processing degree， while the contents of polypodine B， β-ecdysterone， 25R-inokosterone， 25S-inokosterone and polysaccharides showed a gradual decreasing trend， indicating these 8 components increased and decreased to different degrees in the process of wine and salt processing. The results of Pearson correlation analysis showed that the 5-hydroxymethylfurfural content of the samples with different processing degrees of wine-processed and salt-processed products were negatively correlated with the brightness value（L^*） and the total color difference value（E^*ab）（P<0.01）， and positively correlated with the red-green value（a^*） and the yellow-blue value（b^*）（P<0.01）， and that the content of polypodine B and polysaccharides were positively correlated with L^* and E^*ab（P<0.01）. The discriminant functions of wine-processed and salt-processed products of ABR were established by Fisher linear discriminant analysis， and their accuracy rates in the training samples were 93.75% and 95.83%， respectively. Twelve test samples of wine-processed and salt-processed products with different processing degree were back substitution， and the correct rate was 100%. ConclusionThe trend of composition and color changes of ABR with different processing degrees in different production areas is relatively consistent， and the color value can better distinguish ABR with different processing degrees， and the color of ABR is related to some representative components in the processing process， indicating that the color can provide reference for the identification of the processing degree of ABR and the prediction of component content.

Objective:To analyze the influencing factors of platinum resistant recurrence in advanced epithelial ovarian cancer(AEOC),establish a nomogram model to predict platinum-resistant recurrence of AEOC,and inter-nally validate it.Methods:The clinicopathological data of 577 AEOC patients who achieved complete remission af-ter initial treatment in the Department of Gynecology,Yunnan Cancer Hospital from June 1,2013 to December 31,2021 were collected.According to whether the platinum free interval(PFI)was less than 6 months,the patients were divided into platinum-resistant recurrence group(130 cases)and non-platinum-resistant group(447 cases,including patients with platinum-sensitive recurrence and no recurrence after 6 months of follow-up).Multivariate Logistic regression analysis was used to screen for the independent risk factors affecting the recurrence of plati-num-resistant patients.Based on the independent risk factors,a nomogram prediction model was established,and Bootstrap method was used for internal verification.The area under the ROC curve(AUC),calibration curve and decision curve(DCA)were used to evaluate the performance of the model.Results:①There were statistically sig-nificant differences in age,bilateral ovarian invasion,FIGO staging,menopause,neoadjuvant chemotherapy(NACT),chemotherapy interval(TTC),platelet count(PLT),platelet count/lymphocyte count ratio(PLR),fibrino-gen/lymphocyte count ratio(FLR),prognostic nutritional index(PNI),albumin(ALB),CA125 level,ascites volume,residual lesions,perioperative chemotherapy frequency,and CA125 half-life between the two groups(P＜0.05).②Multivariate Logistic regression analysis showed that bilateral ovarian invasion,FIGO stage Ⅳ,TTC＞16 days,ini-tial ascites volume＞1000ml,perioperative chemotherapy frequency＞9 times,surgery with R2 resection,CA125 half-life＞52 days were independent risk factors for recurrence of platinum-resistant AEOC patients(OR＞1,P＜0.05).③The AUC of the nomogram model constructed based on the above 7 indicators was 0.791(95％Cl 0.747-0.835),and the calibration curve and ideal curve fitted well.DCA showed that the net benefit interval of the model was 0.037-0.800.Conclusions:The nomogram prediction model based on independent risk factors for the recurrence of platinum-resistance of AEOC patients has good discrimination,calibration and clinical appli-cability,which can better predict the recurrence risk of platinum-resistance in AEOC patients after the initial treat-ment.

Objective:To analyze the influencing factors of platinum resistant recurrence in advanced epithelial ovarian cancer(AEOC),establish a nomogram model to predict platinum-resistant recurrence of AEOC,and inter-nally validate it.Methods:The clinicopathological data of 577 AEOC patients who achieved complete remission af-ter initial treatment in the Department of Gynecology,Yunnan Cancer Hospital from June 1,2013 to December 31,2021 were collected.According to whether the platinum free interval(PFI)was less than 6 months,the patients were divided into platinum-resistant recurrence group(130 cases)and non-platinum-resistant group(447 cases,including patients with platinum-sensitive recurrence and no recurrence after 6 months of follow-up).Multivariate Logistic regression analysis was used to screen for the independent risk factors affecting the recurrence of plati-num-resistant patients.Based on the independent risk factors,a nomogram prediction model was established,and Bootstrap method was used for internal verification.The area under the ROC curve(AUC),calibration curve and decision curve(DCA)were used to evaluate the performance of the model.Results:①There were statistically sig-nificant differences in age,bilateral ovarian invasion,FIGO staging,menopause,neoadjuvant chemotherapy(NACT),chemotherapy interval(TTC),platelet count(PLT),platelet count/lymphocyte count ratio(PLR),fibrino-gen/lymphocyte count ratio(FLR),prognostic nutritional index(PNI),albumin(ALB),CA125 level,ascites volume,residual lesions,perioperative chemotherapy frequency,and CA125 half-life between the two groups(P＜0.05).②Multivariate Logistic regression analysis showed that bilateral ovarian invasion,FIGO stage Ⅳ,TTC＞16 days,ini-tial ascites volume＞1000ml,perioperative chemotherapy frequency＞9 times,surgery with R2 resection,CA125 half-life＞52 days were independent risk factors for recurrence of platinum-resistant AEOC patients(OR＞1,P＜0.05).③The AUC of the nomogram model constructed based on the above 7 indicators was 0.791(95％Cl 0.747-0.835),and the calibration curve and ideal curve fitted well.DCA showed that the net benefit interval of the model was 0.037-0.800.Conclusions:The nomogram prediction model based on independent risk factors for the recurrence of platinum-resistance of AEOC patients has good discrimination,calibration and clinical appli-cability,which can better predict the recurrence risk of platinum-resistance in AEOC patients after the initial treat-ment.

Objective:To analyze the influencing factors of platinum resistant recurrence in advanced epithelial ovarian cancer(AEOC),establish a nomogram model to predict platinum-resistant recurrence of AEOC,and inter-nally validate it.Methods:The clinicopathological data of 577 AEOC patients who achieved complete remission af-ter initial treatment in the Department of Gynecology,Yunnan Cancer Hospital from June 1,2013 to December 31,2021 were collected.According to whether the platinum free interval(PFI)was less than 6 months,the patients were divided into platinum-resistant recurrence group(130 cases)and non-platinum-resistant group(447 cases,including patients with platinum-sensitive recurrence and no recurrence after 6 months of follow-up).Multivariate Logistic regression analysis was used to screen for the independent risk factors affecting the recurrence of plati-num-resistant patients.Based on the independent risk factors,a nomogram prediction model was established,and Bootstrap method was used for internal verification.The area under the ROC curve(AUC),calibration curve and decision curve(DCA)were used to evaluate the performance of the model.Results:①There were statistically sig-nificant differences in age,bilateral ovarian invasion,FIGO staging,menopause,neoadjuvant chemotherapy(NACT),chemotherapy interval(TTC),platelet count(PLT),platelet count/lymphocyte count ratio(PLR),fibrino-gen/lymphocyte count ratio(FLR),prognostic nutritional index(PNI),albumin(ALB),CA125 level,ascites volume,residual lesions,perioperative chemotherapy frequency,and CA125 half-life between the two groups(P＜0.05).②Multivariate Logistic regression analysis showed that bilateral ovarian invasion,FIGO stage Ⅳ,TTC＞16 days,ini-tial ascites volume＞1000ml,perioperative chemotherapy frequency＞9 times,surgery with R2 resection,CA125 half-life＞52 days were independent risk factors for recurrence of platinum-resistant AEOC patients(OR＞1,P＜0.05).③The AUC of the nomogram model constructed based on the above 7 indicators was 0.791(95％Cl 0.747-0.835),and the calibration curve and ideal curve fitted well.DCA showed that the net benefit interval of the model was 0.037-0.800.Conclusions:The nomogram prediction model based on independent risk factors for the recurrence of platinum-resistance of AEOC patients has good discrimination,calibration and clinical appli-cability,which can better predict the recurrence risk of platinum-resistance in AEOC patients after the initial treat-ment.

Objective:To analyze the influencing factors of platinum resistant recurrence in advanced epithelial ovarian cancer(AEOC),establish a nomogram model to predict platinum-resistant recurrence of AEOC,and inter-nally validate it.Methods:The clinicopathological data of 577 AEOC patients who achieved complete remission af-ter initial treatment in the Department of Gynecology,Yunnan Cancer Hospital from June 1,2013 to December 31,2021 were collected.According to whether the platinum free interval(PFI)was less than 6 months,the patients were divided into platinum-resistant recurrence group(130 cases)and non-platinum-resistant group(447 cases,including patients with platinum-sensitive recurrence and no recurrence after 6 months of follow-up).Multivariate Logistic regression analysis was used to screen for the independent risk factors affecting the recurrence of plati-num-resistant patients.Based on the independent risk factors,a nomogram prediction model was established,and Bootstrap method was used for internal verification.The area under the ROC curve(AUC),calibration curve and decision curve(DCA)were used to evaluate the performance of the model.Results:①There were statistically sig-nificant differences in age,bilateral ovarian invasion,FIGO staging,menopause,neoadjuvant chemotherapy(NACT),chemotherapy interval(TTC),platelet count(PLT),platelet count/lymphocyte count ratio(PLR),fibrino-gen/lymphocyte count ratio(FLR),prognostic nutritional index(PNI),albumin(ALB),CA125 level,ascites volume,residual lesions,perioperative chemotherapy frequency,and CA125 half-life between the two groups(P＜0.05).②Multivariate Logistic regression analysis showed that bilateral ovarian invasion,FIGO stage Ⅳ,TTC＞16 days,ini-tial ascites volume＞1000ml,perioperative chemotherapy frequency＞9 times,surgery with R2 resection,CA125 half-life＞52 days were independent risk factors for recurrence of platinum-resistant AEOC patients(OR＞1,P＜0.05).③The AUC of the nomogram model constructed based on the above 7 indicators was 0.791(95％Cl 0.747-0.835),and the calibration curve and ideal curve fitted well.DCA showed that the net benefit interval of the model was 0.037-0.800.Conclusions:The nomogram prediction model based on independent risk factors for the recurrence of platinum-resistance of AEOC patients has good discrimination,calibration and clinical appli-cability,which can better predict the recurrence risk of platinum-resistance in AEOC patients after the initial treat-ment.

Objective:To analyze the influencing factors of platinum resistant recurrence in advanced epithelial ovarian cancer(AEOC),establish a nomogram model to predict platinum-resistant recurrence of AEOC,and inter-nally validate it.Methods:The clinicopathological data of 577 AEOC patients who achieved complete remission af-ter initial treatment in the Department of Gynecology,Yunnan Cancer Hospital from June 1,2013 to December 31,2021 were collected.According to whether the platinum free interval(PFI)was less than 6 months,the patients were divided into platinum-resistant recurrence group(130 cases)and non-platinum-resistant group(447 cases,including patients with platinum-sensitive recurrence and no recurrence after 6 months of follow-up).Multivariate Logistic regression analysis was used to screen for the independent risk factors affecting the recurrence of plati-num-resistant patients.Based on the independent risk factors,a nomogram prediction model was established,and Bootstrap method was used for internal verification.The area under the ROC curve(AUC),calibration curve and decision curve(DCA)were used to evaluate the performance of the model.Results:①There were statistically sig-nificant differences in age,bilateral ovarian invasion,FIGO staging,menopause,neoadjuvant chemotherapy(NACT),chemotherapy interval(TTC),platelet count(PLT),platelet count/lymphocyte count ratio(PLR),fibrino-gen/lymphocyte count ratio(FLR),prognostic nutritional index(PNI),albumin(ALB),CA125 level,ascites volume,residual lesions,perioperative chemotherapy frequency,and CA125 half-life between the two groups(P＜0.05).②Multivariate Logistic regression analysis showed that bilateral ovarian invasion,FIGO stage Ⅳ,TTC＞16 days,ini-tial ascites volume＞1000ml,perioperative chemotherapy frequency＞9 times,surgery with R2 resection,CA125 half-life＞52 days were independent risk factors for recurrence of platinum-resistant AEOC patients(OR＞1,P＜0.05).③The AUC of the nomogram model constructed based on the above 7 indicators was 0.791(95％Cl 0.747-0.835),and the calibration curve and ideal curve fitted well.DCA showed that the net benefit interval of the model was 0.037-0.800.Conclusions:The nomogram prediction model based on independent risk factors for the recurrence of platinum-resistance of AEOC patients has good discrimination,calibration and clinical appli-cability,which can better predict the recurrence risk of platinum-resistance in AEOC patients after the initial treat-ment.

Objective:To analyze the influencing factors of platinum resistant recurrence in advanced epithelial ovarian cancer(AEOC),establish a nomogram model to predict platinum-resistant recurrence of AEOC,and inter-nally validate it.Methods:The clinicopathological data of 577 AEOC patients who achieved complete remission af-ter initial treatment in the Department of Gynecology,Yunnan Cancer Hospital from June 1,2013 to December 31,2021 were collected.According to whether the platinum free interval(PFI)was less than 6 months,the patients were divided into platinum-resistant recurrence group(130 cases)and non-platinum-resistant group(447 cases,including patients with platinum-sensitive recurrence and no recurrence after 6 months of follow-up).Multivariate Logistic regression analysis was used to screen for the independent risk factors affecting the recurrence of plati-num-resistant patients.Based on the independent risk factors,a nomogram prediction model was established,and Bootstrap method was used for internal verification.The area under the ROC curve(AUC),calibration curve and decision curve(DCA)were used to evaluate the performance of the model.Results:①There were statistically sig-nificant differences in age,bilateral ovarian invasion,FIGO staging,menopause,neoadjuvant chemotherapy(NACT),chemotherapy interval(TTC),platelet count(PLT),platelet count/lymphocyte count ratio(PLR),fibrino-gen/lymphocyte count ratio(FLR),prognostic nutritional index(PNI),albumin(ALB),CA125 level,ascites volume,residual lesions,perioperative chemotherapy frequency,and CA125 half-life between the two groups(P＜0.05).②Multivariate Logistic regression analysis showed that bilateral ovarian invasion,FIGO stage Ⅳ,TTC＞16 days,ini-tial ascites volume＞1000ml,perioperative chemotherapy frequency＞9 times,surgery with R2 resection,CA125 half-life＞52 days were independent risk factors for recurrence of platinum-resistant AEOC patients(OR＞1,P＜0.05).③The AUC of the nomogram model constructed based on the above 7 indicators was 0.791(95％Cl 0.747-0.835),and the calibration curve and ideal curve fitted well.DCA showed that the net benefit interval of the model was 0.037-0.800.Conclusions:The nomogram prediction model based on independent risk factors for the recurrence of platinum-resistance of AEOC patients has good discrimination,calibration and clinical appli-cability,which can better predict the recurrence risk of platinum-resistance in AEOC patients after the initial treat-ment.

Objective:To analyze the influencing factors of platinum resistant recurrence in advanced epithelial ovarian cancer(AEOC),establish a nomogram model to predict platinum-resistant recurrence of AEOC,and inter-nally validate it.Methods:The clinicopathological data of 577 AEOC patients who achieved complete remission af-ter initial treatment in the Department of Gynecology,Yunnan Cancer Hospital from June 1,2013 to December 31,2021 were collected.According to whether the platinum free interval(PFI)was less than 6 months,the patients were divided into platinum-resistant recurrence group(130 cases)and non-platinum-resistant group(447 cases,including patients with platinum-sensitive recurrence and no recurrence after 6 months of follow-up).Multivariate Logistic regression analysis was used to screen for the independent risk factors affecting the recurrence of plati-num-resistant patients.Based on the independent risk factors,a nomogram prediction model was established,and Bootstrap method was used for internal verification.The area under the ROC curve(AUC),calibration curve and decision curve(DCA)were used to evaluate the performance of the model.Results:①There were statistically sig-nificant differences in age,bilateral ovarian invasion,FIGO staging,menopause,neoadjuvant chemotherapy(NACT),chemotherapy interval(TTC),platelet count(PLT),platelet count/lymphocyte count ratio(PLR),fibrino-gen/lymphocyte count ratio(FLR),prognostic nutritional index(PNI),albumin(ALB),CA125 level,ascites volume,residual lesions,perioperative chemotherapy frequency,and CA125 half-life between the two groups(P＜0.05).②Multivariate Logistic regression analysis showed that bilateral ovarian invasion,FIGO stage Ⅳ,TTC＞16 days,ini-tial ascites volume＞1000ml,perioperative chemotherapy frequency＞9 times,surgery with R2 resection,CA125 half-life＞52 days were independent risk factors for recurrence of platinum-resistant AEOC patients(OR＞1,P＜0.05).③The AUC of the nomogram model constructed based on the above 7 indicators was 0.791(95％Cl 0.747-0.835),and the calibration curve and ideal curve fitted well.DCA showed that the net benefit interval of the model was 0.037-0.800.Conclusions:The nomogram prediction model based on independent risk factors for the recurrence of platinum-resistance of AEOC patients has good discrimination,calibration and clinical appli-cability,which can better predict the recurrence risk of platinum-resistance in AEOC patients after the initial treat-ment.

Objective:To analyze the influencing factors of platinum resistant recurrence in advanced epithelial ovarian cancer(AEOC),establish a nomogram model to predict platinum-resistant recurrence of AEOC,and inter-nally validate it.Methods:The clinicopathological data of 577 AEOC patients who achieved complete remission af-ter initial treatment in the Department of Gynecology,Yunnan Cancer Hospital from June 1,2013 to December 31,2021 were collected.According to whether the platinum free interval(PFI)was less than 6 months,the patients were divided into platinum-resistant recurrence group(130 cases)and non-platinum-resistant group(447 cases,including patients with platinum-sensitive recurrence and no recurrence after 6 months of follow-up).Multivariate Logistic regression analysis was used to screen for the independent risk factors affecting the recurrence of plati-num-resistant patients.Based on the independent risk factors,a nomogram prediction model was established,and Bootstrap method was used for internal verification.The area under the ROC curve(AUC),calibration curve and decision curve(DCA)were used to evaluate the performance of the model.Results:①There were statistically sig-nificant differences in age,bilateral ovarian invasion,FIGO staging,menopause,neoadjuvant chemotherapy(NACT),chemotherapy interval(TTC),platelet count(PLT),platelet count/lymphocyte count ratio(PLR),fibrino-gen/lymphocyte count ratio(FLR),prognostic nutritional index(PNI),albumin(ALB),CA125 level,ascites volume,residual lesions,perioperative chemotherapy frequency,and CA125 half-life between the two groups(P＜0.05).②Multivariate Logistic regression analysis showed that bilateral ovarian invasion,FIGO stage Ⅳ,TTC＞16 days,ini-tial ascites volume＞1000ml,perioperative chemotherapy frequency＞9 times,surgery with R2 resection,CA125 half-life＞52 days were independent risk factors for recurrence of platinum-resistant AEOC patients(OR＞1,P＜0.05).③The AUC of the nomogram model constructed based on the above 7 indicators was 0.791(95％Cl 0.747-0.835),and the calibration curve and ideal curve fitted well.DCA showed that the net benefit interval of the model was 0.037-0.800.Conclusions:The nomogram prediction model based on independent risk factors for the recurrence of platinum-resistance of AEOC patients has good discrimination,calibration and clinical appli-cability,which can better predict the recurrence risk of platinum-resistance in AEOC patients after the initial treat-ment.