Objective To investigate the association of sleep quality with prevalence of irritable bowel syndrome (IBS) in communities of Shanghai.Methods A survey on prevalence of IBS in communities of Jiangqiao County,Jiading District of Shanghai was conducted using a stratified,randomized cluster-sampling method.A total of 11 569 questionnaires was collected.The IBS was diagnosed according to Rome Ⅲ criteria,and Athens Insomnia Scale (ALS) was used for selfassessment.The relationship between sleep quality and IBS was analyzed.Results The prevalence of insomnia was 21.00% in community groups,33.02% in IBS patients and 18.74% in non-IBS patients.The incidence of insomnia was significant higher in IBS group than non-IBS group (P =0.004,OR = 2.14,95 % CI:1.92- 2.39).Among IBS patients,the incidence of insomnia was higher in females than in males (37.24% vs 28.41%,P = 0.000).A logistic analysis for psychological contributing factors in IBS revealed that insomnia might be one of the risk factors for IBS (OR= 2.11,95% CI:1.89-2.36).Conclusion IBS patients have poor sleep quality,especially in females.

Objective:Eph is the largest member of the family of receptor tyrosine kinases (RTK). Hypermethylation of CpG island is an important mechanism leading to down-regulation of EphA7 in gastric cancer. The objective of this study is to investigate the expression level of EphA7 in gastric carcinoma and its roles in the development,progression and prognosis of gastric carcinoma. Methods:The expression level of EphA7 transcript in gastric carcinoma specimens and normal mucosa tissues was detected using real-time RT-PCR. Results:The expression level of EphA7 in gastric carcinoma was divided into three groups according to the expression rate in gastric cancer tissue and matched normal mucosa. Statistics analysis showed that the expression of EphA7 is significantly related to age and stage. Overexpression of EphA7 has a tendency to relate to metastasis. Conclusion:EphA7 is differentially expressed in gastric carcinoma specimens. Overexpression of EphA7 may play a role in the development and progress of gastric carcinoma.

Objective:To develop and validate a predictive model for adverse outcomes in women with hypertensive disorders of pregnancy (HDP).Methods:We retrospectively analyzed the clinical data of patients diagnosed with HDP and delivered at the First Affiliated Hospital of Soochow University or Sichuan Provincial Maternity and Child Health Care Hospital between May 1, 2011, and April 30, 2019. These patients were categorized as the adverse outcome group or the control group with adverse outcomes within 48 h after admission. Univariate analysis, least absolute shrinkage, selection operator (LASSO), and multivariable logistic regression were employed to analyze factors influencing the adverse outcomes and develop a predictive model. The receiver operating characteristic (ROC) curve and calibration plot was used to assess the predictive performance. Bootstrapping was used for the internal validation and the retrospective dataset of patients with HDP from the First Affiliated Hospital of Soochow University from May 1, 2019, to April 30, 2020, for the external validation. A graphic nomogram was created through R software based on the model.Results:(1) Of the 2 978 HDP patients who were included in the development set, 356 were in the adverse outcome group, accounting for 12.0%; of the 233 patients who were included in the external validation set, 40 presented with adverse outcomes within 48 h after admission, accounting for 17.2%. (2) Nine optimal predictors were identified based on the LASSO regression analysis and multivariable logistic regression, consisting of gestational age on admission, routine prenatal care, number of symptoms, mean arterial pressure, platelet count, fibrinogen, albumin, serum urea, and serum creatinine, based on which the logistic predictive model was established. (3) The ROC curve for this predictive model achieved an area under the curve (AUC) of 0.878 (95% CI: 0.858-0.897), and the ideal cut-off value for predicted probability was 0.136, with a sensitivity of 0.778 (95% CI: 0.731-0.820) and specificity of 0.848(95% CI: 0.834-0.862). The model was well-calibrated as the Hosmer-Lemeshow test showed that P>0.05. The calibration plot of the model had a slope of 1 and an intercept of 0. (4) The model showed good consistency in the internal validation and had an AUC of 0.872 (95% CI: 0.807-0.937) in the external validation. The Hosmer-Lemeshow test showed that the P value was >0.05, and the calibration slope was 1.001. (5) A nomogram was constructed for convenient clinical use. Conclusion:A relatively accurate prediction model for adverse outcomes in HDP patients was established, which could be used as a valuable quantitative tool for assessing HDP-related complications.

Objective:To establish a model for predicting cesarean delivery after failure of trial of labor among low-risk term primipara.Methods:This study retrospectively analyzed the clinical data of low-risk primiparas, with singleton cephalic full-term fetus, who delivered in the Department of Obstetrics and Gynecology of the First Affiliated Hospital of Soochow University from January 1, 2011 to August 31, 2017. Women experienced cesarean delivery(CS) following failed trial of labor were grouped as CS group, while those successfully delivered normally as vaginal delivery group(VD group). Chi-square test, t-test and multivariate logistic regression analysis were used for statistical analysis. Influencing factors of CS after a failed trial of labor were screened to establish the prediction model. The area under the receiver operating characteristic curve (AUC) and Hosmer-Lemeshow goodness-of-fit test were used to assess the performance of the model. A nomogram was established using R programming language based on the predictive model. Results:(1) This study recruited 6 551 subjects and among them, 576 (8.8%) women experienced CS after a failed trial of labor and the rest 5 975(91.2%) delivered vaginally. (2) The women in CS group were older [(27.5±3.1) vs (26.8±3.0) years, t=-4.963, P<0.01] and shorter in height [(159.5±4.2) vs (161.7±4.6) cm, t=11.548, P<0.01] , had higher pre-pregnancy body mass index (BMI) [(21.5±2.6) vs (20.8±2.5) kg/m 2, t=-6.743, P<0.01] and higher weight gain during pregnancy [(14.8±4.2) vs (14.1±4.2) kg, t=-3.446, P<0.01] and delivered later [(282±7) vs (278±7) d, t=-10.499, P<0.01] compared with those in VD group. The incidence of premature rupture of membranes (PROM) [26.4% (152/576) vs 20.7% (1 238/5 975) , χ2=10.101, P<0.01], labor induction [oxytocin: 26.4% (152/576) vs 16.3% (976/5 975), artificial rupture of membranes: 46.5% (268/576) vs 36.6% (2 189/5 975), application of cervical dilator balloon: 2.6% (15/576) vs 1.1% (65/5 975) and Propess: 4.7% (27/576) vs 2.5% (149/5 975), χ2=134.918, P<0.01], and the proportion of cases with meconium-stained amniotic fluid [ Ⅰ: 5.2% (30/576) vs 3.5% (209/5 975), Ⅱ: 5.7% (33/576) vs 2.5% (150/5 975), Ⅲ/bloody: 13.7% (79/576) vs 1.8% (105/5 975), χ2=307.664, P<0.01] were all higher in CS group than in VD group. There were more male infants [58.0% (334/576) vs 49.1% (2 934/5 975), χ2=16.576, P<0.01] and higher neonatal birth weight [(3 528±389) vs (3 344±368) g, t=-11.431, P<0.01] in the CS group as well. (3) Multivariate logistic regression analysis showed that maternal age and height, pre-pregnancy BMI, weight gain during pregnancy, gestational age at delivery, PROM, labor induction with oxytocin, artificial rupture of membrane, application of cervical dilator balloon and Propess, meconium-stained amniotic fluid, and fetal gender were all independent factors for CS. Two prediction models and nomograms were established according to fetal gender was involved or not. (4) The AUC of the prediction model not involving fetal gender was 0.774 (95% CI: 0.763-0.784) and the cut-off value was >8.7% with the sensitivity and specificity of 0.707 and 0.706, while that involving fetal gender was 0.782 (95% CI: 0.771-0.791) with the sensitivity and specificity of 0.785 and 0.645, respectively, when the cut-off value was >7.4%. The Hosmer-Lemeshow goodness-of-fit test showed that the two models fitted well (both P>0.05). Results of the internal validation using Bootstrap method indicated that the CS rates predicted by both models were consistent with the real data. Conclusions:The established models could effectively and accurately predict CS in term, singleton, cephalic, and low-risk primipara after failure of trial of labor, which might be a tool for clinicians to inform pregnant women to choose an appropriate delivery mode, thus improving maternal and infant outcomes.

Tumor microenvironment (TME) is comprised of cellular and non-cellular components that exist within and around the tumor mass. The TME is highly dynamic and its importance in different stages of cancer progression has been well recognized. A growing body of evidence suggests that TME also plays pivotal roles in cancer treatment responses. TME is significantly remodeled upon cancer therapies, and such change either enhances the responses or induces drug resistance. Given the importance of TME in tumor progression and therapy resistance, strategies that remodel TME to improve therapeutic responses are under developing. In this review, we provide an overview of the essential components in TME and the remodeling of TME in response to anti-cancer treatments. We also summarize the strategies that aim to enhance therapeutic efficacy by modulating TME.
Antineoplastic Agents ; pharmacology ; Drug Resistance ; Humans ; Neoplasm Staging ; Neoplasms ; drug therapy ; pathology ; Treatment Outcome ; Tumor Microenvironment ; drug effects ; physiology

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.