Novel HALO® image analysis to determine cell phenotype in porous precision-templated scaffolds.

Image analysis platforms have gained increasing popularity in the last decade for the ability to automate and conduct high-throughput, multiplex, and quantitative analyses of a broad range of pathological tissues. However, imaging tissues with unique morphology or tissues containing implanted biomaterial scaffolds remain a challenge. Using HALO®, an image analysis platform specialized in quantitative tissue analysis, we have developed a novel method to determine multiple cell phenotypes in porous precision-templated scaffolds (PTS).

Challenges in Predicting Recurrence After Resection of Node-Negative Non-Small Cell Lung Cancer.

Background: One in 5 patients with completely resected early-stage non-small cell lung cancer will recur within 2 years. Risk stratification may facilitate a personalized approach to the use of adjuvant therapy and surveillance imaging. We developed a prediction model for recurrence based on five clinical variables (tumor size and grade, visceral pleural and lymphovascular invasion, and sublobar resection), and tested the hypothesis that the addition of several new molecular markers of poor long-term outcome (vascular endothelial growth factor C; microRNA precursors 486 and 30d) would enhance prediction.

Nonhematopoietic Peroxisome Proliferator-Activated Receptor-α Protects Against Cardiac Injury and Enhances Survival in Experimental Polymicrobial Sepsis.

Objectives: Peroxisome proliferator-activated receptor-α is significantly down-regulated in circulating leukocytes from children with sepsis. Peroxisome proliferator-activated receptor-α null (Ppara) mice have greater mortality than wild-type mice when subjected to sepsis by cecal ligation and puncture. We sought to characterize the role of peroxisome proliferator-activated receptor-α in sepsis and to identify the mechanism whereby peroxisome proliferator-activated receptor-α confers a survival advantage.