OBJECTIVE To investigate the effect of Banxia Xiexin decoction(BXD) on colitis associated cancer(CAC) mice and its related mechanism. METHODS Seventy-five C57BL/6 mice were randomly divided into normal group, model group, Banxia Xiexin decoction low-dose group, high-dose group and mesalazine group. Except for the normal group, the mice in the other groups were intraperitoneally injected with azoxymethane combined with oral dextran sodium sulfate to establish the CAC model. BXD and mesalazine were given respectively for intervention. The general conditions of all mice were observed and recorded, and the changes of body weight, disease activity index, colon length and tumor number were monitored. HE staining was utilized to observe the pathological changes of colon tissue. The expression levels of PCNA, NF-κB P65 and IκB-α were detected by immunohistochemistry. The mRNA levels of IL-17A, N-cadherin, E-cadherin and Bcl-2 were detected by qRT-PCR. Macrophage infiltration was measured using immunostaining analysis. Western blotting was applied to analyze the expression of NF-κB, E-cadherin and N-cadherin proteins in colon tissues of each group. RESULTS There was no significant tumor occurrence in the normal group, while the body weight of the model group mice was significantly reduced and the number of colon tumors increased. The colon length, number of tumors, and degree of inflammatory cell infiltration in the BXD group were significantly improved compared to the model group. Immunohistochemical results showed that the expression of PCNA, NF-κB P65 and IκB-α protein in colon tissue of model group was remarkably increased (P<0.01). Immunofluorescence results showed that the number of F4/80, CD80 and CD206 positive macrophages in the colon tissue of the model group increased (P<0.05 or P<0.01). The results of RT-PCR demonstrated that the levels of IL-17A, N-cadherin and Bcl-2 mRNA in the colon tissue of the model group were significantly increased (P<0.01), while the level of E-cadherin mRNA was fundamentally decreased (P<0.01). Western blotting results displayed that the expression levels of NF-κB and N-cadherin protein in colon tissue of model group were up-regulated (P<0.01), while E-cadherin was significantly down-regulated (P<0.01). Compared with the model group, the changes of the above indexes in the BXD and mesalazine groups were ameliorated, with statistical differences (P<0.05 or P<0.01), and the changes in the BXD high-dose group were more significant. CONCLUSION BXD exhibits strong anti-inflammatory and anti-tumor benefits in CAC mice, inhibiting macrophage activation in colon tissue and promoting M2 polarization, while reducing the expression of tumor associated proteins PCNA and Bcl-2, and block the progression of EMT related proteins (E-cadherin and N-cadherin). The mechanism may connect to suppressing NF-κB P65 and IκB-α activation to regulate the NF-κB signaling pathway.

OBJECTIVE To investigate the effect of Banxia Xiexin decoction(BXD) on colitis associated cancer(CAC) mice and its related mechanism. METHODS Seventy-five C57BL/6 mice were randomly divided into normal group, model group, Banxia Xiexin decoction low-dose group, high-dose group and mesalazine group. Except for the normal group, the mice in the other groups were intraperitoneally injected with azoxymethane combined with oral dextran sodium sulfate to establish the CAC model. BXD and mesalazine were given respectively for intervention. The general conditions of all mice were observed and recorded, and the changes of body weight, disease activity index, colon length and tumor number were monitored. HE staining was utilized to observe the pathological changes of colon tissue. The expression levels of PCNA, NF-κB P65 and IκB-α were detected by immunohistochemistry. The mRNA levels of IL-17A, N-cadherin, E-cadherin and Bcl-2 were detected by qRT-PCR. Macrophage infiltration was measured using immunostaining analysis. Western blotting was applied to analyze the expression of NF-κB, E-cadherin and N-cadherin proteins in colon tissues of each group. RESULTS There was no significant tumor occurrence in the normal group, while the body weight of the model group mice was significantly reduced and the number of colon tumors increased. The colon length, number of tumors, and degree of inflammatory cell infiltration in the BXD group were significantly improved compared to the model group. Immunohistochemical results showed that the expression of PCNA, NF-κB P65 and IκB-α protein in colon tissue of model group was remarkably increased (P<0.01). Immunofluorescence results showed that the number of F4/80, CD80 and CD206 positive macrophages in the colon tissue of the model group increased (P<0.05 or P<0.01). The results of RT-PCR demonstrated that the levels of IL-17A, N-cadherin and Bcl-2 mRNA in the colon tissue of the model group were significantly increased (P<0.01), while the level of E-cadherin mRNA was fundamentally decreased (P<0.01). Western blotting results displayed that the expression levels of NF-κB and N-cadherin protein in colon tissue of model group were up-regulated (P<0.01), while E-cadherin was significantly down-regulated (P<0.01). Compared with the model group, the changes of the above indexes in the BXD and mesalazine groups were ameliorated, with statistical differences (P<0.05 or P<0.01), and the changes in the BXD high-dose group were more significant. CONCLUSION BXD exhibits strong anti-inflammatory and anti-tumor benefits in CAC mice, inhibiting macrophage activation in colon tissue and promoting M2 polarization, while reducing the expression of tumor associated proteins PCNA and Bcl-2, and block the progression of EMT related proteins (E-cadherin and N-cadherin). The mechanism may connect to suppressing NF-κB P65 and IκB-α activation to regulate the NF-κB signaling pathway.

Objective:To establish a predictive model of the first cesarean delivery in multiparous women based on the situation of two consecutive pregnancies.Methods:The data of patients with two consecutive deliv-eries of single live birth and the previous delivery was vaginal delivery in the First Affiliated Hospital of Soochow U-niversity during the second delivery time range from January 1,2018 to December 31,2021 were retrospectively analyzed.According to whether the second pregnancy occurred cesarean section,the patients were divided into two groups(vaginal delivery group and cesarean section group).Univariate,stepwise,and multiple Logistic re-gression analyses were used to screen the influencing factors of multipara's first cesarean section delivery,and the prediction model was established.R language was used to build the model's nomogram and calibration curve.The bootstrap resampling method was used for internal verification.After establishing the model,clinical data of patients with two consecutive births of single live birth between January 1,2022 and April 1,2023 were retrospec-tively collected for external verification of the model.Results:①A total of 2709 patients were included in this study for modeling,of which 6.31％(171/2709)underwent cesarean section for the first time.603 cases were included for external verification.②According to univariate,stepwise and multivariate Logistic regression analysis,all the variables affecting the first delivery by cesarean section were screened out,including:abnormal labor in previous labor,age of current delivery,assisted reproductive technology,hypertension disorder complicating pregnancy,pregnancy with thrombocytopenia,oligohydramnios,excessive amniotic fluid,macrosomia,fetal growth restriction,abnormal fetal position,fetal distress,all of the above variables P＜0.05 and incorporated into the final prediction model.③The AUC of this model was 0.949(95％CI 0.928-0.969),and the calibration curve showed that the model intercept was 0 and the slope was 1.Hosmer-Lemeshow test had a P＞0.05,indicating that the model had a high accuracy.④The AUC of external validation was 0.958,the slope of the calibration curve was 0.972,and the Hosmer-Lemeshow test had a P of 0.49.Conclusions:The prediction model of the first delivery by cesarean section during the second pregnancy has been established.The prediction efficiency of the model is good,and it can provide a tool for the individualized evaluation of menstrual women in clinical work.

Objective:To establish a predictive model of the first cesarean delivery in multiparous women based on the situation of two consecutive pregnancies.Methods:The data of patients with two consecutive deliv-eries of single live birth and the previous delivery was vaginal delivery in the First Affiliated Hospital of Soochow U-niversity during the second delivery time range from January 1,2018 to December 31,2021 were retrospectively analyzed.According to whether the second pregnancy occurred cesarean section,the patients were divided into two groups(vaginal delivery group and cesarean section group).Univariate,stepwise,and multiple Logistic re-gression analyses were used to screen the influencing factors of multipara's first cesarean section delivery,and the prediction model was established.R language was used to build the model's nomogram and calibration curve.The bootstrap resampling method was used for internal verification.After establishing the model,clinical data of patients with two consecutive births of single live birth between January 1,2022 and April 1,2023 were retrospec-tively collected for external verification of the model.Results:①A total of 2709 patients were included in this study for modeling,of which 6.31％(171/2709)underwent cesarean section for the first time.603 cases were included for external verification.②According to univariate,stepwise and multivariate Logistic regression analysis,all the variables affecting the first delivery by cesarean section were screened out,including:abnormal labor in previous labor,age of current delivery,assisted reproductive technology,hypertension disorder complicating pregnancy,pregnancy with thrombocytopenia,oligohydramnios,excessive amniotic fluid,macrosomia,fetal growth restriction,abnormal fetal position,fetal distress,all of the above variables P＜0.05 and incorporated into the final prediction model.③The AUC of this model was 0.949(95％CI 0.928-0.969),and the calibration curve showed that the model intercept was 0 and the slope was 1.Hosmer-Lemeshow test had a P＞0.05,indicating that the model had a high accuracy.④The AUC of external validation was 0.958,the slope of the calibration curve was 0.972,and the Hosmer-Lemeshow test had a P of 0.49.Conclusions:The prediction model of the first delivery by cesarean section during the second pregnancy has been established.The prediction efficiency of the model is good,and it can provide a tool for the individualized evaluation of menstrual women in clinical work.

Objective:To establish a predictive model of the first cesarean delivery in multiparous women based on the situation of two consecutive pregnancies.Methods:The data of patients with two consecutive deliv-eries of single live birth and the previous delivery was vaginal delivery in the First Affiliated Hospital of Soochow U-niversity during the second delivery time range from January 1,2018 to December 31,2021 were retrospectively analyzed.According to whether the second pregnancy occurred cesarean section,the patients were divided into two groups(vaginal delivery group and cesarean section group).Univariate,stepwise,and multiple Logistic re-gression analyses were used to screen the influencing factors of multipara's first cesarean section delivery,and the prediction model was established.R language was used to build the model's nomogram and calibration curve.The bootstrap resampling method was used for internal verification.After establishing the model,clinical data of patients with two consecutive births of single live birth between January 1,2022 and April 1,2023 were retrospec-tively collected for external verification of the model.Results:①A total of 2709 patients were included in this study for modeling,of which 6.31％(171/2709)underwent cesarean section for the first time.603 cases were included for external verification.②According to univariate,stepwise and multivariate Logistic regression analysis,all the variables affecting the first delivery by cesarean section were screened out,including:abnormal labor in previous labor,age of current delivery,assisted reproductive technology,hypertension disorder complicating pregnancy,pregnancy with thrombocytopenia,oligohydramnios,excessive amniotic fluid,macrosomia,fetal growth restriction,abnormal fetal position,fetal distress,all of the above variables P＜0.05 and incorporated into the final prediction model.③The AUC of this model was 0.949(95％CI 0.928-0.969),and the calibration curve showed that the model intercept was 0 and the slope was 1.Hosmer-Lemeshow test had a P＞0.05,indicating that the model had a high accuracy.④The AUC of external validation was 0.958,the slope of the calibration curve was 0.972,and the Hosmer-Lemeshow test had a P of 0.49.Conclusions:The prediction model of the first delivery by cesarean section during the second pregnancy has been established.The prediction efficiency of the model is good,and it can provide a tool for the individualized evaluation of menstrual women in clinical work.

Objective:To establish a predictive model of the first cesarean delivery in multiparous women based on the situation of two consecutive pregnancies.Methods:The data of patients with two consecutive deliv-eries of single live birth and the previous delivery was vaginal delivery in the First Affiliated Hospital of Soochow U-niversity during the second delivery time range from January 1,2018 to December 31,2021 were retrospectively analyzed.According to whether the second pregnancy occurred cesarean section,the patients were divided into two groups(vaginal delivery group and cesarean section group).Univariate,stepwise,and multiple Logistic re-gression analyses were used to screen the influencing factors of multipara's first cesarean section delivery,and the prediction model was established.R language was used to build the model's nomogram and calibration curve.The bootstrap resampling method was used for internal verification.After establishing the model,clinical data of patients with two consecutive births of single live birth between January 1,2022 and April 1,2023 were retrospec-tively collected for external verification of the model.Results:①A total of 2709 patients were included in this study for modeling,of which 6.31％(171/2709)underwent cesarean section for the first time.603 cases were included for external verification.②According to univariate,stepwise and multivariate Logistic regression analysis,all the variables affecting the first delivery by cesarean section were screened out,including:abnormal labor in previous labor,age of current delivery,assisted reproductive technology,hypertension disorder complicating pregnancy,pregnancy with thrombocytopenia,oligohydramnios,excessive amniotic fluid,macrosomia,fetal growth restriction,abnormal fetal position,fetal distress,all of the above variables P＜0.05 and incorporated into the final prediction model.③The AUC of this model was 0.949(95％CI 0.928-0.969),and the calibration curve showed that the model intercept was 0 and the slope was 1.Hosmer-Lemeshow test had a P＞0.05,indicating that the model had a high accuracy.④The AUC of external validation was 0.958,the slope of the calibration curve was 0.972,and the Hosmer-Lemeshow test had a P of 0.49.Conclusions:The prediction model of the first delivery by cesarean section during the second pregnancy has been established.The prediction efficiency of the model is good,and it can provide a tool for the individualized evaluation of menstrual women in clinical work.

Objective:To establish a predictive model of the first cesarean delivery in multiparous women based on the situation of two consecutive pregnancies.Methods:The data of patients with two consecutive deliv-eries of single live birth and the previous delivery was vaginal delivery in the First Affiliated Hospital of Soochow U-niversity during the second delivery time range from January 1,2018 to December 31,2021 were retrospectively analyzed.According to whether the second pregnancy occurred cesarean section,the patients were divided into two groups(vaginal delivery group and cesarean section group).Univariate,stepwise,and multiple Logistic re-gression analyses were used to screen the influencing factors of multipara's first cesarean section delivery,and the prediction model was established.R language was used to build the model's nomogram and calibration curve.The bootstrap resampling method was used for internal verification.After establishing the model,clinical data of patients with two consecutive births of single live birth between January 1,2022 and April 1,2023 were retrospec-tively collected for external verification of the model.Results:①A total of 2709 patients were included in this study for modeling,of which 6.31％(171/2709)underwent cesarean section for the first time.603 cases were included for external verification.②According to univariate,stepwise and multivariate Logistic regression analysis,all the variables affecting the first delivery by cesarean section were screened out,including:abnormal labor in previous labor,age of current delivery,assisted reproductive technology,hypertension disorder complicating pregnancy,pregnancy with thrombocytopenia,oligohydramnios,excessive amniotic fluid,macrosomia,fetal growth restriction,abnormal fetal position,fetal distress,all of the above variables P＜0.05 and incorporated into the final prediction model.③The AUC of this model was 0.949(95％CI 0.928-0.969),and the calibration curve showed that the model intercept was 0 and the slope was 1.Hosmer-Lemeshow test had a P＞0.05,indicating that the model had a high accuracy.④The AUC of external validation was 0.958,the slope of the calibration curve was 0.972,and the Hosmer-Lemeshow test had a P of 0.49.Conclusions:The prediction model of the first delivery by cesarean section during the second pregnancy has been established.The prediction efficiency of the model is good,and it can provide a tool for the individualized evaluation of menstrual women in clinical work.

Objective:To establish a predictive model of the first cesarean delivery in multiparous women based on the situation of two consecutive pregnancies.Methods:The data of patients with two consecutive deliv-eries of single live birth and the previous delivery was vaginal delivery in the First Affiliated Hospital of Soochow U-niversity during the second delivery time range from January 1,2018 to December 31,2021 were retrospectively analyzed.According to whether the second pregnancy occurred cesarean section,the patients were divided into two groups(vaginal delivery group and cesarean section group).Univariate,stepwise,and multiple Logistic re-gression analyses were used to screen the influencing factors of multipara's first cesarean section delivery,and the prediction model was established.R language was used to build the model's nomogram and calibration curve.The bootstrap resampling method was used for internal verification.After establishing the model,clinical data of patients with two consecutive births of single live birth between January 1,2022 and April 1,2023 were retrospec-tively collected for external verification of the model.Results:①A total of 2709 patients were included in this study for modeling,of which 6.31％(171/2709)underwent cesarean section for the first time.603 cases were included for external verification.②According to univariate,stepwise and multivariate Logistic regression analysis,all the variables affecting the first delivery by cesarean section were screened out,including:abnormal labor in previous labor,age of current delivery,assisted reproductive technology,hypertension disorder complicating pregnancy,pregnancy with thrombocytopenia,oligohydramnios,excessive amniotic fluid,macrosomia,fetal growth restriction,abnormal fetal position,fetal distress,all of the above variables P＜0.05 and incorporated into the final prediction model.③The AUC of this model was 0.949(95％CI 0.928-0.969),and the calibration curve showed that the model intercept was 0 and the slope was 1.Hosmer-Lemeshow test had a P＞0.05,indicating that the model had a high accuracy.④The AUC of external validation was 0.958,the slope of the calibration curve was 0.972,and the Hosmer-Lemeshow test had a P of 0.49.Conclusions:The prediction model of the first delivery by cesarean section during the second pregnancy has been established.The prediction efficiency of the model is good,and it can provide a tool for the individualized evaluation of menstrual women in clinical work.

Objective:To establish a predictive model of the first cesarean delivery in multiparous women based on the situation of two consecutive pregnancies.Methods:The data of patients with two consecutive deliv-eries of single live birth and the previous delivery was vaginal delivery in the First Affiliated Hospital of Soochow U-niversity during the second delivery time range from January 1,2018 to December 31,2021 were retrospectively analyzed.According to whether the second pregnancy occurred cesarean section,the patients were divided into two groups(vaginal delivery group and cesarean section group).Univariate,stepwise,and multiple Logistic re-gression analyses were used to screen the influencing factors of multipara's first cesarean section delivery,and the prediction model was established.R language was used to build the model's nomogram and calibration curve.The bootstrap resampling method was used for internal verification.After establishing the model,clinical data of patients with two consecutive births of single live birth between January 1,2022 and April 1,2023 were retrospec-tively collected for external verification of the model.Results:①A total of 2709 patients were included in this study for modeling,of which 6.31％(171/2709)underwent cesarean section for the first time.603 cases were included for external verification.②According to univariate,stepwise and multivariate Logistic regression analysis,all the variables affecting the first delivery by cesarean section were screened out,including:abnormal labor in previous labor,age of current delivery,assisted reproductive technology,hypertension disorder complicating pregnancy,pregnancy with thrombocytopenia,oligohydramnios,excessive amniotic fluid,macrosomia,fetal growth restriction,abnormal fetal position,fetal distress,all of the above variables P＜0.05 and incorporated into the final prediction model.③The AUC of this model was 0.949(95％CI 0.928-0.969),and the calibration curve showed that the model intercept was 0 and the slope was 1.Hosmer-Lemeshow test had a P＞0.05,indicating that the model had a high accuracy.④The AUC of external validation was 0.958,the slope of the calibration curve was 0.972,and the Hosmer-Lemeshow test had a P of 0.49.Conclusions:The prediction model of the first delivery by cesarean section during the second pregnancy has been established.The prediction efficiency of the model is good,and it can provide a tool for the individualized evaluation of menstrual women in clinical work.

Objective:To establish a predictive model of the first cesarean delivery in multiparous women based on the situation of two consecutive pregnancies.Methods:The data of patients with two consecutive deliv-eries of single live birth and the previous delivery was vaginal delivery in the First Affiliated Hospital of Soochow U-niversity during the second delivery time range from January 1,2018 to December 31,2021 were retrospectively analyzed.According to whether the second pregnancy occurred cesarean section,the patients were divided into two groups(vaginal delivery group and cesarean section group).Univariate,stepwise,and multiple Logistic re-gression analyses were used to screen the influencing factors of multipara's first cesarean section delivery,and the prediction model was established.R language was used to build the model's nomogram and calibration curve.The bootstrap resampling method was used for internal verification.After establishing the model,clinical data of patients with two consecutive births of single live birth between January 1,2022 and April 1,2023 were retrospec-tively collected for external verification of the model.Results:①A total of 2709 patients were included in this study for modeling,of which 6.31％(171/2709)underwent cesarean section for the first time.603 cases were included for external verification.②According to univariate,stepwise and multivariate Logistic regression analysis,all the variables affecting the first delivery by cesarean section were screened out,including:abnormal labor in previous labor,age of current delivery,assisted reproductive technology,hypertension disorder complicating pregnancy,pregnancy with thrombocytopenia,oligohydramnios,excessive amniotic fluid,macrosomia,fetal growth restriction,abnormal fetal position,fetal distress,all of the above variables P＜0.05 and incorporated into the final prediction model.③The AUC of this model was 0.949(95％CI 0.928-0.969),and the calibration curve showed that the model intercept was 0 and the slope was 1.Hosmer-Lemeshow test had a P＞0.05,indicating that the model had a high accuracy.④The AUC of external validation was 0.958,the slope of the calibration curve was 0.972,and the Hosmer-Lemeshow test had a P of 0.49.Conclusions:The prediction model of the first delivery by cesarean section during the second pregnancy has been established.The prediction efficiency of the model is good,and it can provide a tool for the individualized evaluation of menstrual women in clinical work.