This network meta-analysis compared treatment via laparoscopy, hysteroscopy (HP), combined laparoscopy with HP (LH), and vaginal repair (VR) for reducing intermittent abnormal uterine bleeding and cesarean scar defect (CSD) diverticulum depth in patients with CSD. Data Sources: Electronic databases (PubMed, EMBASE, The Cochrane Central Register of Controlled Trials, MEDLINE, ClinicalTrials.gov, Chinese Biomedical Literature Database, and China National Knowledge Integrated) were searched for articles published through June 13, 2018. Methods of Study Selection: The search included randomized controlled trials (RCTs) and observational studies of surgical treatment for CSD. Standardized mean difference (SMD) and 95% confidence intervals (CIs) were reported. RCTs were evaluated by the Cochrane risk-of-bias tool, observational studies by Risk of Bias in Nonrandomized Studies of Intervention, and overall evidence quality by grade. Data were analyzed by STATA (version 15.0; StataCorp, College Station, TX) and R software for windows (version 3.5.0; R Core Team, 2018). Tabulation, Integration, and Results: Ten studies (n = 858; 4 RCTs and 6 observational studies) were included. Patients who underwent uterine diverticulum resection by LH had a shorter duration of abnormal uterine bleeding than those by HP (SMD = 1.36, 95% CI, 0.37−2.36; p = .007) and VR (SMD = 1.58, 95% CI, 0.97−2.19; p <.0001). LH reduced the CSD diverticulum depth more than VR (SMD = 1.57, 95% CI, 0.54−2.61; p = .003). There was no significant difference in efficacy among the surgical procedures. Conclusion: LH reduced intermittent abnormal uterine bleeding and scar depth more than the other surgical interventions. Larger clinical trials are warranted to verify this analysis.

frozen plasma transfusion, and graft implantation time, leading to 0e25 score points with an AUC of 0.7 in the new AKI-Predict-Score (Fig. 1). Three risk classes were identified: low-risk (0e10 points), intermediate-risk (11e20 points) and high-risk with >20 points. In addition, a score of >20 points correlated with impaired long-term graft survival and more postoperative complications assessed with the Comprehensive Complication Index. Conclusion:The AKI-Predict-Score is a new and reliable instrument to identify recipients at risk for severe post-transplant AKI. This score is readily available at end of the transplant procedure. This model offers therefore a great potential to decide which renal protective strategies might be implemented directly after liver transplant.

Background Cervical squamous cancer (CESC) is an intractable gynecological malignancy because of its high mortality rate and difficulty in early diagnosis. Several biomarkers have been found to predict the prognose of CESC using bioinformatics methods, but they still lack clinical effectiveness. Most of the existing bioinformatic studies only focus on the changes of oncogenes but neglect the differences on the protein level and molecular biology validation are rarely conducted. Methods Gene set data from the NCBI-GEO database were used in this study to compare the differences of gene and protein levels between normal and cancer tissues through significant pathway selection and core gene signature analysis to screen potential clinical biomarkers of CESC. Subsequently, the molecular and protein levels of clinical samples were verified by quantitative transcription PCR, western blot and immunohistochemistry. Results Three differentially expressed genes (RFC4, MCM2, TOP2A) were found to have a significant survival (P < 0.05) and highly expressed in CESC tissues. Molecular biological verification using quantitative reverse transcribed PCR, western blotting and immunohistochemistry assays exhibited significant differences in the expression of RFC4 between CESC and para-cancerous tissues (P < 0.05). Conclusion This study identified three potential biomarkers (RFC4, MCM2, TOP2A) of CESC which may be useful to clarify the underlying mechanisms of CESC and predict the prognosis of CESC patients.

Background Dermatomyositis accompanied with malignancy is a common poor prognostic factor of dermatomyositis. Thus, the early prediction of the risk of malignancy in patients with dermatomyositis can significantly improve the prognosis of patients. However, the identification of antibodies related to malignancy in dermatomyositis patients has not been widely implemented in clinical practice. Herein, we established a predictive nomogram model for the diagnosis of dermatomyositis associated with malignancy. Methods We retrospectively analyzed 240 cases of dermatomyositis patients admitted to Sun Yat-sen Memorial Hospital, Sun Yat-sen University from January 2002 to December 2019. According to the year of admission, the first 70% of the patients were used to establish a training cohort, and the remaining 30% were assigned to the validation cohort. Univariate analysis was performed on all variables, and statistically relevant variables were further included in a multivariate logistic regression analysis to screen for independent predictors. Finally, a nomogram was constructed based on these independent predictors. Bootstrap repeated sampling calculation C-index was used to evaluate the model’s calibration, and area under the curve (AUC) was used to evaluate the model discrimination ability. Results Multivariate logistic analysis showed that patients older than 50-year-old, dysphagia, refractory itching, and elevated creatine kinase were independent risk factors for dermatomyositis associated with malignancy, while interstitial lung disease was a protective factor. Based on this, we constructed a nomogram using the above-mentioned five factors. The C-index was 0.780 (95% CI [0.690–0.870]) in the training cohort and 0.756 (95% CI [0.618–0.893]) in the validation cohort, while the AUC value was 0.756 (95% CI [0.600–0.833]). Taken together, our nomogram showed good calibration and was effective in predicting which dermatomyositis patients were at a higher risk of developing malignant tumors.