To estimate a classifier’s error in predicting future observations, bootstrap methods have been proposed as reduced-variation alternatives to traditional cross-validation (CV) methods based on sampling without replacement. Monte Carlo (MC) simulation studies aimed at estimating the true misclassification error conditional on the training set are commonly used to compare CV methods. We conducted an MC simulation study to compare a new method of bootstrap CV (BCV) to k-fold CV for estimating clasification error.

There is increasing evidence of a pivotal role for regulated mRNA translation in control of developmental cell fate transitions. Physiological and pathological stem and progenitor cell self-renewal is maintained by the mRNA-binding protein, Musashi1 through repression of translation of key mRNAs encoding cell cycle inhibitory proteins. The mechanism by which Musashi1 function is modified to allow translation of these target mRNAs under conditions that require inhibition of cell cycle progression, is unknown. In this study, we demonstrate that differentiation of primary embryonic rat neural stem/progenitor cells (NSPCs) or human neuroblastoma SH-SY5Y cells results in the rapid phosphorylation of Musashi1 on the evolutionarily conserved site serine 337 (S337). Phosphorylation of this site has been shown to be required for cell cycle control during the maturation of Xenopus oocytes. S337 phosphorylation in mammalian NSPCs and human SH-SY5Y cells correlates with the de-repression and translation of a Musashi reporter mRNA and with accumulation of protein from the endogenous Musashi target mRNA, p21WAF1/CIP1. Inhibition of Musashi regulatory phosphorylation, through expression of a phospho-inhibitory mutant Musashi1 S337A or over-expression of the wild-type Musashi, blocked differentiation of both NSPCs and SH-SY5Y cells. Musashi1 was similarly phosphorylated in NSPCs and SH-SY5Y cells under conditions of nutrient deprivation-induced cell cycle arrest. Expression of the Musashi1 S337A mutant protein attenuated nutrient deprivation-induced NSPC and SH-SY5Y cell death. Our data suggest that in response to environmental cues that oppose cell cycle progression, regulation of Musashi function is required to promote target mRNA translation and cell fate transition. Forced modulation of Musashi1 function may present a novel therapeutic strategy to oppose pathological stem cell self-renewal.

High-dose chemotherapy followed by autologous stem cell transplantation (ASCT) remains the standard of care for multiple myeloma (MM) patients. Although outpatient ASCT has been shown to be safe and feasible, the procedure is overall rare with most patients in the US undergoing inpatient ASCT. Furthermore, hospitalization rates for patients that undergo outpatient ASCT remain high. Adequate markers that predict hospitalization during outpatient ASCT are lacking, yet would be of great clinical value to select patients that are suited to outpatient ASCT. In this study we aimed to elucidate differences between planned outpatient and inpatient ASCT and further evaluated clinical characteristics that are significantly associated with hospitalization during planned outpatient hospitalization. Factors that were significantly associated with a planned inpatient ASCT included an advanced MM disease stage, worse performance status as well as non-Caucasian race, while low albumin levels and female gender were significantly associated with hospitalization during outpatient ASCT. The results of this analysis provide crucial knowledge of factors that are associated with planned inpatient ASCT and hospitalization during outpatient ASCT and could guide the treating physician in decision-making and further facilitate outpatient transplantation.

To grow beyond certain size and reach oxygen and other essential nutrients, solid tumors trigger angiogenesis (neovascularization) by secreting various growth factors. Based on this fact, several researches proposed that density of newly formed vessels correlate with tumor malignancy. Vessel density is known as a true prognostic indicator for several types of cancer. However, automated quantification of angiogenesis is still in its primitive stage, and deserves more intelligent methods by taking advantages accruing from novel computer algorithms.