Development and validation of nomograms for predicting adverse neonatal outcomes in women with polycystic ovary syndrome: a retrospective study.

In our study, we retrospectively enrolled 606 women with newly diagnosed polycystic ovary syndrome. Participants were divided into two cohorts: development cohort (n = 424) and validation cohort (n = 182). Multivariate logistic regression analyses were used to identify predictive indicators, and nomograms were developed and validated. We found that waist hip rate (WHR), testosterone levels, and fasting blood glucose (FBG) levels (WTF) could predict the small for gestational age; BMI, WHR and modified Ferriman-Gallwey Score (BWM) correlated with low Apgar scores; and BMI, WHR, modified Ferriman-Gallwey Score, testosterone levels, and FBG levels (BWMTF) correlated with adverse neonatal outcomes. The BWMTF nomogram was established, revealing perfect discrimination with the area under the receiver operating characteristic curve (AUC) and stratified five-fold cross-validation in development cohort (AUC = 0.75, Mean AUC = 0.75) and validation cohort (AUC = 0.68, Mean AUC = 0.75). Calibration plots showed good calibration. We established and validated three models for predicting adverse perinatal effects to guide preventive treatment protocols. Impact statementWhat is already known on this subject? Many studies have identified a large number of predictors, but also lack a comprehensively quantified tool to predict adverse neonatal outcomes in women with PCOS to guide the development of clinical treatment programs.What do the results of this study add? This article screened the high risks factors of adverse neonatal outcomes in women with PCOS, and three nomograms were established and validated. Also, the area under the receiver operating characteristic curve (AUC) and stratified five-fold cross-validation in development cohort and validation cohort showed good discrimination; Calibration plots showed good calibration.What are the implications of these findings for clinical practice and/or further research? Our scoring system could help clinicians evaluate these risks and conduct proper screening, prevention, and management to ameliorate the risk of neonatal disease in these patients.

Selenium-binding protein 1 in head and neck cancer is low-expression and associates with the prognosis of nasopharyngeal carcinoma.

BACKGROUND: Selenium-binding protein 1 (SELENBP1) expression is reduced markedly in many types of cancers and low SELENBP1 expression levels are associated with poor patient prognosis. METHODS: SELENBP1 gene expression in head and neck squamous cell carcinoma (HNSCC) was analyzed with GEO dataset and characteristics of SELENBP1 expression in paraffin embedded tissue were summarized. Expression of SELENBP1 in nasopharyngeal carcinoma (NPC), laryngeal cancer, oral cancer, tonsil cancer, hypopharyngeal cancer and normal tissues were detected using immunohistochemistry, at last, 99 NPC patients were followed up more than 5 years and were analyzed the prognostic significance of SELENBP1. RESULTS: Analysis of GEO dataset concluded that SELENBP1 gene expression in HNSCC was lower than that in normal tissue (P < 0.01), but there was no significant difference of SELENBP1 gene expression in different T-stage and N-stage (P > 0.05). Analysis of pathological section concluded that SELENBP1 in the majority of HNSCC is low expression and in cancer nests is lower expression than surrounding normal tissue, even associated with the malignant degree of tumor. Further study indicated the low SELENBP1 expression group of patients with NPC accompanied by poor overall survival and has significantly different comparing with the high expression group. CONCLUSION: SELENBP1 expression was down-regulated in HNSCC, but has no associated with T-stage and N-stage of tumor. Low expression of SELENBP1 in patients with NPC has poor over survival, so SELENBP1 could be a novel biomarker for predicting prognosis.