A Nomogram Model for Predicting Recurrence of Stage I-III Endometrial Cancer Based on Inflammation-Immunity-Nutrition Score (IINS) and Traditional Classical Predictors.

Objective: The purpose of this study was to investigate the prognostic value of the inflammation-immunity-nutrition score (IINS) in patients with stage I-III endometrial cancer (EC) and establish a nomogram model to predict the recurrence of EC by combining IINS and traditional classical predictors.

Methods: Seven hundred and seventy-five patients with stage I-III EC who underwent initial surgical treatment at the First Affiliated Hospital of Chongqing Medical University were included in this study as the training cohort. In the training cohort, IINS (0-3) was constructed based on preoperative C-reactive protein (CRP), lymphocytes (LYM), and albumin (ALB).

Improvements Achieved by Multiple Imputation for Single-Cell RNA-Seq Data in Clustering Analysis and Differential Expression Analysis.

In a single-cell RNA-seq (scRNA-seq) data set, a high proportion of missing values (or an excessive number of zeroes) are frequently observed. For the related follow-up tasks, such as clustering analysis and differential expression analysis, a data set without missing values is generally required. Many imputation approaches have been proposed for this purpose.