Differentiating benign from malignant cardiac tumors with cardiac magnetic resonance imaging

Background

The purpose of this analysis is to describe the differences in cardiac magnetic resonance characteristics between benign and malignant tumors, which would be helpful for surgical planning.

Classification treesA possible method for iso-resource grouping in intensive care

Classification and grouping of clinical data into defined categories or hierarchies is difficult in intensive care practice. Diagnosis-related groups are used to categorise patients on the basis of diagnosis. However, this approach may not be applicable to intensive care where there is wide heterogeneity within diagnostic groups. Classification tree analysis uses selected independent variables to group patients according to a dependent variable in a way that reduces variation. In this study, the influence of three easily identified patient attributes on their length of intensive care unit stay was explored using classification analysis. Two thousand five hundred and forty-five critically ill patients from three hospitals were classified into groups so that the variation in length of stay within each group was minimised. In 23 out of 39 terminal groups, the interquartile range of the length of stay was ≤ 3 days.     

CD8+T-bet+ cells as a predominant biomarker for inclusion body myositis

Background

Myositis is a heterogeneous group of muscular auto-immune diseases with clinical and pathological criteria that allow the classification of patients into different sub-groups. Inclusion body myositis is the most frequent myositis above fifty years of age. Diagnosing inclusion body myositis requires expertise and is challenging. Little is known concerning the pathogenic mechanisms of this disease in which conventional suppressive-immune therapies are inefficacious.

Relationship between brain zinc and transient learning impairment of adult rats fed zinc-deficient diet

Because of the reported presence of both CART peptide and NOS activity in the same hypothalamic nuclei, their colocalization was examined. Eighteen percent of the neurons in the supraoptic nuclei, and 16% of the neurons in the paraventricular nucleus contained both CART immunoreactivity and NOS activity. Many other neurons in these regions stained for only one marker although they were often close by. Thus, CART peptides and NO may interact in these regions.     

Identification and ranking of genetic and laboratory environment factors influencing a behavioral trait,thermal nociception,via computational analysis of a large data archive

Laboratory conditions in biobehavioral experiments are commonly assumed to be ‘controlled’, having little impact on the outcome. However, recent studies have illustrated that the laboratory environment has a robust effect on behavioral traits. Given that environmental factors can interact with trait-relevant genes, some have questioned the reliability and generalizability of behavior genetic research designed to identify those genes. This problem might be alleviated by the identification of the most relevant environmental factors, but the task is hindered by the large number of factors that typically vary between and within laboratories. We used a computational approach to retrospectively identify and rank sources of variability in nociceptive responses as they occurred in a typical research laboratory over several years. A machine-learning algorithm was applied to an archival data set of 8034 independent observations of baseline thermal nociceptive sensitivity. This analysis revealed that a factor even more important than mouse genotype was the experimenter performing the test, and that nociception can be affected by many additional laboratory factors including season/humidity, cage density, time of day, sex and within-cage order of testing. The results were confirmed by linear modeling in a subset of the data, and in confirmatory experiments, in which we were able to partition the variance of this complex trait among genetic (27%), environmental (42%) and genetic×environmental (18%) sources.