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Ting Wen Yi Hu,1 Dan Nie,1,2 Jin Hai Gou,1,3 Zheng Yu Li1,3 1Department of Gynecology and Obstetrics, West China Second University Hospital, Sichuan University, Chengdu, People’s Republic of China; 2Key Laboratory of Obstetrics and Gynecologic and Pediatric Diseases and Birth Defects of Ministry of Education, West China Second Hospital, Sichuan University, Chengdu, People’s Republic of China; 3The State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, People’s Republic of China Background: Computed tomography (CT) has been extensively used in predicting suboptimal cytoreduction (SCR) in advanced ovarian cancer (OC). However, disagreements remain in literatures on the predictive value of CT findings for SCR. This meta-analysis was designed to determine the ability of eight preoperative CT findings to predict SCR in advanced OC. Materials and methods: A comprehensive literature search was conducted for eligible studies to identify the association between the eight preoperative CT findings and SCR in advanced OC. The predictive performances of preoperative CT findings were expressed in terms of sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR) and diagnostic odds ratio (DOR) with pooled proportion. Results: A total of 10 studies and 1,614 patients were included in this meta-analysis. Large volume ascites had the highest sensitivity (64%, CI 56–71%), with a PLR of 1.3 (CI 1.1–1.5) and an NLR of 0.73 (0.59–0.90), while lymph node involvement had the highest specificity (89%, CI 79–94%). The highest DOR of 3 (CI 2–4) was achieved in peritoneal involvement and large bowel mesentery involvement. The other CT findings had poorer predictive performance. Conclusion: Preoperative CT findings have a poor discriminative capacity to predict SCR in advanced OC. Preoperative CT predictors should be used with caution amid clinical decision-making. Keywords: computed tomography, suboptimal cytoreduction, ovarian cancer, meta-analysis, predict

The search for new compounds with а high nootropic biological activity is a promising scientific direction. A modern computational method for predicting pharmacological activity of the studied compounds is the molecular docking. The aim of this work is to study the affinity of new N-acyl derivatives of 2-oxo-1-pyrrolidine acetamide to the binding site of NMDA receptor to search of new effective nootropic drugs. As objects of study, there are used the new N-acyl derivatives of 2-oxo-1-pyrrolidine acetamide, racetams, glutamate and a virtual model of NMDA receptor of organism Rattus norvegicus with an identification code 2A5S from the RCSB PDB database. The results of the computational experiment indicate the presence of high nootropic biological activity in the compounds under study. Substance 3 has the greatest affinity of the N-acyl derivatives of 2-oxo-1-pyrrolidineethanol to the binding site of NMDA receptor Thus, we consider it is appropriate to conduct pharmacological in vivo studies of these compounds for the presence of nootropic biological activity.

The proportion of patients who reattended emergency department (ED) within 72 hours is an important indicator of quality of care. This study develops a practical framework to predict patients who will reattend ED in 72 hours from clinical perspectives. We analyze 328,733 ED patients from 1 January 2011 to 31 December 2013, with an average of 4.6% reattendances. We feature over 100 factors including demographics, diagnosis, patient acuity, chief complaints, selected laboratory tests, summarized vital signs. Using univariate analysis, a pool of risk variables is selected for subsequent factor selection. We then apply filter methods to derive a set of candidate factors. With these factors in combination with suggestions from ED clinicians, a mixed integer programming model based on discriminant analysis is proposed to determine a classification rule for 72-hour reattendance. In numerical experiments, various small subsets of risk factors are used for classification and prediction. The results show that favorable predicting performances can be achieved in both training and test sets. [ABSTRACT FROM AUTHOR]