OBJECTIVE To establish a extraction process of Changyan Heji Ⅱ(CYHJ-Ⅱ)based on UPLC-Q-TOF-MS/MS technology combined with response surface analysis,and to optimize the extraction process.METHODS The chemical components in CYHJ-Ⅱ were qualitatively analyzed by UPLC-Q-TOF-MS/MS technology,and the chemical components with good linear relation-ship in mass spectrometry response were selected as process investigation indicators;the extraction process parameters(water addition amount,extraction time and soaking time)were investigated by Box-Behnken design;the comprehensive score was obtained by princi-pal component analysis(PCA),and the optimal process was determined by the comprehensive score combined with response surface a-nalysis.RESULTS Through qualitative analysis,110 components were inferred and identified from CYHJ-Ⅱ,including 2 organic acids,82 flavonoids,13 terpenoids,and 13 alkaloids.Based on the results of qualitative analysis,48 index components with good lin-ear relationships were derived by UPLC-Q-TOF-MS/MS combined with Masshunter mass spectrometry data analysis software.PCA was performed and the comprehensive score was calculated.Response surface analysis was performed with the comprehensive score as an indicator.The optimal extraction process obtained by combining the response surface prediction results and actual production was:soaking for 45 min,8 times the amount of solvent,2 extractions,each time for 120 min.CONCLUSION This study provides a new idea for the investigation of the extraction process of traditional Chinese medicine compound prescriptions and expands a new method for the development of traditional Chinese medicine compound prescriptions.

This study explored whether Astragalus polysaccharide(APS)can regulate the VEGF/VEGFR signaling pathway to affect the proliferative activity of rat intestinal mucosal microvascu-lar endothelial cells(RIMMVECs).RIMMVECs were isolated from newborn rats,then purified and treated with APS at concentrations of 0.1,1.0,10.0,100.0,1 000.0,and 10 000.0 mg/L.MTT was used to determine the effect of APS on RIMMVECs proliferation and screen for the optimal concentration of APS.Subsequently,flow cytometry was used to detect the changes in cell cycle to evaluate the stage of action of APS on the cell cycle in RIMMVECs.Then,the ELISA was used to detect the changes of VEGFA in cell supernatant to evaluate the potential of cell proliferation and angiogenesis.The changes in fluorescence intensity of Fluo-8AM was observed using fluorescence microscopy to evaluate intracellular Ca2+levels.Finally,Western blot was used to detect the ex-pression of PERK in RIMMVECs to analyze the possible mechanism of APS.The results showed that 100 mg/L APS significantly enhanced the proliferative activity of RIMMVECs,increased the content of VEGFA in the cell supernatant,the intracellular Ca2+levels,and the expression of PERK protein,indicating that APS promotes the proliferation of RIMMVECs,which may be a-chieved by promoting the expression of VEGFA and activating the ERK pathway.

Objective:To investigate the predictive value of machine learning-based CT radiomics model for radioresistance in patients with esophageal squamous cell carcinoma (ESCC).Methods:Clinical data of 185 patients with ESCC treated with radical radiotherapy in the First Affiliated Hospital of Anhui Medical University from December 2015 to July 2022 were retrospectively analyzed, and all patients were randomly divided into a training set ( n=129) and a validation set ( n=56) at a ratio of 7 : 3. The radiomics parameters of the primary lesion of esophageal cancer and the surrounding 5 cm region in the patients' CT arterial phase images were extracted, and 6 machine learning methods were used to screen the optimal radiomics model to obtain the optimal radiomics score (Radscore). Independent prognostic predictors of radioresistance in ESCC were obtained by univariate and multivariate logistic regression analyses, which was used as the basis for constructing the nomogram. The predictive performance of different models was compared by the area under the receiver operating characteristic (ROC) curve (AUC). The predictive efficacy and clinical value of the combined model were evaluated using calibration curve, decision curve analysis and clinical impact curve, net reclassification improvement (NRI) and integrated discrimination improvement (IDI). Results:The combined intratumoral and peritumoral radiomics model based on naive Bayesian classifier yielded the optimal prediction performance, with AUC of 0.859 and 0.936 in the training set and validation set, respectively. Multivariate logistic regression analysis showed that Radscore and T stage were the independent prognostic predictors of radioresistance in ESCC patients, and the AUC of the combined model constructed based on these predictors in the training and validation sets were 0.942 and 0.959, respectively. Calibration curve, decision curve analysis and clinical impact curve, net reclassification improvement (NRI) and integrated discrimination improvement (IDI) all indicated higher clinical benefit and more consistent predictive efficacy of the combined model.Conclusions:Machine learning-based CT radiomics model is useful for the prediction of radioresistance in ESCC. The nomogram of radiomics and clinical parameters can further improve the prediction accuracy and provide novel reference for individualized treatment of patients with ESCC.

Objective:To explore the predictive value of models based on clinical parameters, deep learning radiomics (DLR) from CT images, and traditional handcrafted radiomics (HCR) in assessing pathological complete response (pCR) after neoadjuvant radiotherapy combined with medical therapy in patients with esophageal cancer.Methods:A retrospective study was conducted on 130 patients with locally advanced esophageal cancer who underwent neoadjuvant radiotherapy combined with medical therapy followed by surgery at the First Affiliated Hospital of the University of Science and Technology of China from August 1, 2018, to August 31, 2024. Patients were randomly divided into a training set ( n=91) and a validation set ( n=39) at a ratio of 7:3. Logistic regression analysis was performed to identify clinical independent risk factors associated with pCR. DLR and HCR features were extracted from the tumor and the 5 mm peritumoral region on planning CT images. Features for modeling were selected using t-test, Mann-Whitney U test or Fisher exact probability method, least absolute shrinkage and selection operator (LASSO) regression to calculate the radiomics score (Rad-score). A nomogram was then constructed by integrating the clinical risk factors. The predictive performance of each model was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and decision curve analysis (DCA) to assess clinical benefits. Results:Multivariate logistic regression analysis identified body weight ( OR=1.101, 95% CI: 1.029-1.177, P=0.005) and lymph node positivity ( OR=0.100, 95% CI: 0.014-0.727, P=0.023) as independent predictors of pCR. The peritumoral DLR-HCR model showed superior predictive performance, with AUCs of 0.870 (95% CI: 0.799-0.942) in the training set and 0.866 (95% CI: 0.750-0.982) in the validation set. The combined model incorporating clinical parameters achieved the best performance, with AUCs of 0.903 (95% CI: 0.845-0.962) and 0.888 (95% CI: 0.782-0.994) in the training and validation sets, respectively. Conclusions:The combined model integrating peritumoral DLR-HCR features with clinical parameters provides excellent predictive value for pCR after neoadjuvant radiotherapy combined with medical therapy in esophageal cancer and offers valuable guidance for personalized treatment strategies.

OBJECTIVE The non-volatile and volatile chemical components in Yin-Qiao-Qing-Re Tablets were analyzed sepa-rately using ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry(UPLC-Q-TOF-MS/MS)and Gas Chromatography Mass Spectrometry(GC-MS).METHODS The non-volatile components were analyzed using a Waters ACQUITY UPLC BEH C18 column(2.1 mm×100 mm,1.7 μm),with a mobile phase consisting of 0.1％formic acid aqueous solution(A)and acetonitrile(B)for gradient elution,a flow rate of 0.35 mL·min-1,an injection volume of 5 μL,and a column temperature of 30 ℃;the volatile components were analyzed using an Agilent SH-I-5MS column(5％Phenyl Methyl Silox,30 m×250 μm,0.25 μm);the procedure was temperature-programmed,with an injection volume of 1 μL,a split ratio of 10∶1,a flow rate of 1.0 mL·min-1,and an inlet temperature of 200 ℃.RESULTS A total of 134 non-volatile chemical components and 23 volatile components were analyzed and identified from Yin-Qiao-Qing-Re Tablets,among which 49 compounds were confirmed through comparison with reference stand-ards.The non-volatile components mainly include 27 flavonoids,21 organic acids,15 lignans,14 iridoids,12 phenylethanoid glyco-sides,11 saponins,10 alkaloids,5 terpenes,4 amino acids,3 phenylpropanoids,3 nucleosides,3 xanthones,3 phenolic glycosides,2 chromones and 1 carbohydrate.The volatile components mainly include 11 monoterpenes,5 alcohols and phenols,3 alkenes,2 ke-tones,1 ester,and 1 hydrocarbon.CONCLUSION This study rapidly identifies the chemical components of Yin-Qiao-Qing-Re Tablets,laying a preliminary foundation for research on the pharmacodynamic substances of Yin-Qiao-Qing-Re Tablets and the im-provement of quality control standards.

OBJECTIVE The non-volatile and volatile chemical components in Yin-Qiao-Qing-Re Tablets were analyzed sepa-rately using ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry(UPLC-Q-TOF-MS/MS)and Gas Chromatography Mass Spectrometry(GC-MS).METHODS The non-volatile components were analyzed using a Waters ACQUITY UPLC BEH C18 column(2.1 mm×100 mm,1.7 μm),with a mobile phase consisting of 0.1％formic acid aqueous solution(A)and acetonitrile(B)for gradient elution,a flow rate of 0.35 mL·min-1,an injection volume of 5 μL,and a column temperature of 30 ℃;the volatile components were analyzed using an Agilent SH-I-5MS column(5％Phenyl Methyl Silox,30 m×250 μm,0.25 μm);the procedure was temperature-programmed,with an injection volume of 1 μL,a split ratio of 10∶1,a flow rate of 1.0 mL·min-1,and an inlet temperature of 200 ℃.RESULTS A total of 134 non-volatile chemical components and 23 volatile components were analyzed and identified from Yin-Qiao-Qing-Re Tablets,among which 49 compounds were confirmed through comparison with reference stand-ards.The non-volatile components mainly include 27 flavonoids,21 organic acids,15 lignans,14 iridoids,12 phenylethanoid glyco-sides,11 saponins,10 alkaloids,5 terpenes,4 amino acids,3 phenylpropanoids,3 nucleosides,3 xanthones,3 phenolic glycosides,2 chromones and 1 carbohydrate.The volatile components mainly include 11 monoterpenes,5 alcohols and phenols,3 alkenes,2 ke-tones,1 ester,and 1 hydrocarbon.CONCLUSION This study rapidly identifies the chemical components of Yin-Qiao-Qing-Re Tablets,laying a preliminary foundation for research on the pharmacodynamic substances of Yin-Qiao-Qing-Re Tablets and the im-provement of quality control standards.

OBJECTIVE To establish a extraction process of Changyan Heji Ⅱ(CYHJ-Ⅱ)based on UPLC-Q-TOF-MS/MS technology combined with response surface analysis,and to optimize the extraction process.METHODS The chemical components in CYHJ-Ⅱ were qualitatively analyzed by UPLC-Q-TOF-MS/MS technology,and the chemical components with good linear relation-ship in mass spectrometry response were selected as process investigation indicators;the extraction process parameters(water addition amount,extraction time and soaking time)were investigated by Box-Behnken design;the comprehensive score was obtained by princi-pal component analysis(PCA),and the optimal process was determined by the comprehensive score combined with response surface a-nalysis.RESULTS Through qualitative analysis,110 components were inferred and identified from CYHJ-Ⅱ,including 2 organic acids,82 flavonoids,13 terpenoids,and 13 alkaloids.Based on the results of qualitative analysis,48 index components with good lin-ear relationships were derived by UPLC-Q-TOF-MS/MS combined with Masshunter mass spectrometry data analysis software.PCA was performed and the comprehensive score was calculated.Response surface analysis was performed with the comprehensive score as an indicator.The optimal extraction process obtained by combining the response surface prediction results and actual production was:soaking for 45 min,8 times the amount of solvent,2 extractions,each time for 120 min.CONCLUSION This study provides a new idea for the investigation of the extraction process of traditional Chinese medicine compound prescriptions and expands a new method for the development of traditional Chinese medicine compound prescriptions.

This study explored whether Astragalus polysaccharide(APS)can regulate the VEGF/VEGFR signaling pathway to affect the proliferative activity of rat intestinal mucosal microvascu-lar endothelial cells(RIMMVECs).RIMMVECs were isolated from newborn rats,then purified and treated with APS at concentrations of 0.1,1.0,10.0,100.0,1 000.0,and 10 000.0 mg/L.MTT was used to determine the effect of APS on RIMMVECs proliferation and screen for the optimal concentration of APS.Subsequently,flow cytometry was used to detect the changes in cell cycle to evaluate the stage of action of APS on the cell cycle in RIMMVECs.Then,the ELISA was used to detect the changes of VEGFA in cell supernatant to evaluate the potential of cell proliferation and angiogenesis.The changes in fluorescence intensity of Fluo-8AM was observed using fluorescence microscopy to evaluate intracellular Ca2+levels.Finally,Western blot was used to detect the ex-pression of PERK in RIMMVECs to analyze the possible mechanism of APS.The results showed that 100 mg/L APS significantly enhanced the proliferative activity of RIMMVECs,increased the content of VEGFA in the cell supernatant,the intracellular Ca2+levels,and the expression of PERK protein,indicating that APS promotes the proliferation of RIMMVECs,which may be a-chieved by promoting the expression of VEGFA and activating the ERK pathway.

Objective:To investigate the predictive value of machine learning-based CT radiomics model for radioresistance in patients with esophageal squamous cell carcinoma (ESCC).Methods:Clinical data of 185 patients with ESCC treated with radical radiotherapy in the First Affiliated Hospital of Anhui Medical University from December 2015 to July 2022 were retrospectively analyzed, and all patients were randomly divided into a training set ( n=129) and a validation set ( n=56) at a ratio of 7 : 3. The radiomics parameters of the primary lesion of esophageal cancer and the surrounding 5 cm region in the patients' CT arterial phase images were extracted, and 6 machine learning methods were used to screen the optimal radiomics model to obtain the optimal radiomics score (Radscore). Independent prognostic predictors of radioresistance in ESCC were obtained by univariate and multivariate logistic regression analyses, which was used as the basis for constructing the nomogram. The predictive performance of different models was compared by the area under the receiver operating characteristic (ROC) curve (AUC). The predictive efficacy and clinical value of the combined model were evaluated using calibration curve, decision curve analysis and clinical impact curve, net reclassification improvement (NRI) and integrated discrimination improvement (IDI). Results:The combined intratumoral and peritumoral radiomics model based on naive Bayesian classifier yielded the optimal prediction performance, with AUC of 0.859 and 0.936 in the training set and validation set, respectively. Multivariate logistic regression analysis showed that Radscore and T stage were the independent prognostic predictors of radioresistance in ESCC patients, and the AUC of the combined model constructed based on these predictors in the training and validation sets were 0.942 and 0.959, respectively. Calibration curve, decision curve analysis and clinical impact curve, net reclassification improvement (NRI) and integrated discrimination improvement (IDI) all indicated higher clinical benefit and more consistent predictive efficacy of the combined model.Conclusions:Machine learning-based CT radiomics model is useful for the prediction of radioresistance in ESCC. The nomogram of radiomics and clinical parameters can further improve the prediction accuracy and provide novel reference for individualized treatment of patients with ESCC.

Objective:To explore the predictive value of models based on clinical parameters, deep learning radiomics (DLR) from CT images, and traditional handcrafted radiomics (HCR) in assessing pathological complete response (pCR) after neoadjuvant radiotherapy combined with medical therapy in patients with esophageal cancer.Methods:A retrospective study was conducted on 130 patients with locally advanced esophageal cancer who underwent neoadjuvant radiotherapy combined with medical therapy followed by surgery at the First Affiliated Hospital of the University of Science and Technology of China from August 1, 2018, to August 31, 2024. Patients were randomly divided into a training set ( n=91) and a validation set ( n=39) at a ratio of 7:3. Logistic regression analysis was performed to identify clinical independent risk factors associated with pCR. DLR and HCR features were extracted from the tumor and the 5 mm peritumoral region on planning CT images. Features for modeling were selected using t-test, Mann-Whitney U test or Fisher exact probability method, least absolute shrinkage and selection operator (LASSO) regression to calculate the radiomics score (Rad-score). A nomogram was then constructed by integrating the clinical risk factors. The predictive performance of each model was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and decision curve analysis (DCA) to assess clinical benefits. Results:Multivariate logistic regression analysis identified body weight ( OR=1.101, 95% CI: 1.029-1.177, P=0.005) and lymph node positivity ( OR=0.100, 95% CI: 0.014-0.727, P=0.023) as independent predictors of pCR. The peritumoral DLR-HCR model showed superior predictive performance, with AUCs of 0.870 (95% CI: 0.799-0.942) in the training set and 0.866 (95% CI: 0.750-0.982) in the validation set. The combined model incorporating clinical parameters achieved the best performance, with AUCs of 0.903 (95% CI: 0.845-0.962) and 0.888 (95% CI: 0.782-0.994) in the training and validation sets, respectively. Conclusions:The combined model integrating peritumoral DLR-HCR features with clinical parameters provides excellent predictive value for pCR after neoadjuvant radiotherapy combined with medical therapy in esophageal cancer and offers valuable guidance for personalized treatment strategies.