Rabbit Anti-Human MCAT Polyclonal Antibody

The acceptance of students to a medical school places a considerable emphasis on performance in standardized tests and undergraduate grade point average (uGPA). Traditionally, applicants may be judged as a homogeneous population according to simple quantitative thresholds that implicitly assume a linear relationship between scores and academic success. This 'one-size-fits-all' approach ignores the notion that individuals may show distinct patterns of achievement and follow diverse paths to success. In this study, we examined a dataset composed of 53 variables extracted from the admissions application records of 1,088 students matriculating to NYU School of Medicine between the years 2006-2014. We defined training and test groups and applied K-means clustering to search for distinct groups of applicants. Building an optimized logistic regression model, we then tested the predictive value of this clustering for estimating the success of applicants in medical school, aggregating eight performance measures during the subsequent medical school training as a success factor. We found evidence for four distinct clusters of students-we termed 'signatures'-which differ most substantially according to the absolute level of the applicant's uGPA and its trajectory over the course of undergraduate education. The 'risers' signature showed a relatively higher uGPA and also steeper trajectory; the other signatures showed each remaining combination of these two main factors: 'improvers' relatively lower uGPA, steeper trajectory; 'solids' higher uGPA, flatter trajectory; 'statics' both lower uGPA and flatter trajectory. Examining the success index across signatures, we found that the risers and the statics have significantly higher and lower likelihood of quantifiable success in medical school, respectively. We also found that each signature has a unique set of features that correlate with its success in medical school. The big data approach presented here can more sensitively uncover success potential since it takes into account the inherent heterogeneity within the student population.

Alveolar epithelial cell (AEC) apoptosis and inadequate repair resulting from "exaggerated" lung aging and mitochondrial dysfunction are critical determinants promoting lung fibrosis. alpha-Klotho, which is an antiaging molecule that is expressed predominantly in the kidney and secreted in the blood, can protect lung epithelial cells against hyperoxia-induced apoptosis. We reasoned that Klotho protects AEC exposed to oxidative stress in part by maintaining mitochondrial DNA (mtDNA) integrity and mitigating apoptosis. We find that Klotho levels are decreased in both serum and alveolar type II (AT2) cells from asbestos-exposed mice. We show that oxidative stress reduces AEC Klotho mRNA and protein expression, whereas Klotho overexpression is protective while Klotho silencing augments AEC mtDNA damage. Compared with wild-type, Klotho heterozygous hypomorphic allele (kl/+) mice have increased asbestos-induced lung fibrosis due in part to increased AT2 cell mtDNA damage. Notably, we demonstrate that serum Klotho levels are reduced in wild-type but not mitochondrial catalase overexpressing (MCAT) mice 3 wk following exposure to asbestos and that EUK-134, a MnSOD/catalase mimetic, mitigates oxidant-induced reductions in AEC Klotho expression. Using pharmacologic and genetic silencing studies, we show that Klotho attenuates oxidant-induced AEC mtDNA damage and apoptosis via mechanisms dependent on AKT activation arising from upstream fibroblast growth factor receptor 1 activation. Our findings suggest that Klotho preserves AEC mtDNA integrity in the setting of oxidative stress necessary for preventing apoptosis and asbestos-induced lung fibrosis. We reason that strategies aimed at augmenting AEC Klotho levels may be an innovative approach for mitigating age-related lung diseases.