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The article discusses the study on the Diabetes Awareness and Prevention Education, a novel school-based health intervention program to raise awareness on diabetes in New Delhi, India. The program reportedly includes orientation workshops for teacher coordinators and peer leaders, learning games, and intraschool competitions. The results reportedly revealed that the program is a crucial tool for early diabetes prevention.

Type 2 diabetes patients exhibit subclinical inflammation but the regulatory mechanisms are poorly understood. We sought to evaluate the role of miR-146a expression along with its downstream proinflammatory signals in relation to glycemic control and insulin resistance. Study subjects ( n = 20 each) comprised of clinically well characterized Type 2 diabetes patients and control non-diabetic subjects. miRNA and mRNA expression levels were probed in peripheral blood mononuclear cells (PBMC) by Real-time RT-PCR and plasma levels of TNFα and IL-6 were measured by ELISA. The miR-146a expression levels were significantly decreased in PBMCs from patients with Type 2 diabetes compared to control subjects. Among the target genes of miR-146a, TRAF-6 mRNA expression was significantly increased in patients with Type 2 diabetes while there was no significant difference in the mRNA levels of IRAK1 in the study groups. In contrast, there were significantly increased levels of NFκB expression in patients with Type 2 diabetes. There was an increased trend in the levels of TNFα and IL-6 mRNA in patients with type 2 diabetes. While SOCS-3 mRNA levels increased, plasma TNFα and IL-6 levels were also significantly higher in patients with type 2 diabetes. miR-146a expression was negatively correlated to glycated hemoglobin, insulin resistance, TRAF6, and NFκB mRNA levels and circulatory levels of TNFα and IL-6. Reduced miR-146a levels are associated with insulin resistance, poor glycemic control, and several proinflammatory cytokine genes and circulatory levels of TNFα and IL-6 in Asian Indian Type 2 diabetic patients. [ABSTRACT FROM AUTHOR]

Indian Art Music has a huge variety of rāgas. The similarity across rāgas has traditionally been approached from various musicological viewpoints. This work aims at discovering structural similarities among renditions of rāgas using a data-driven approach. Starting from melodic contours, we obtain the descriptive note-level transcription of each rendition. Repetitive note patterns of variable and fixed lengths are derived using stochastic models. We propose a latent variable approach for raga distinction based on statistics of these patterns. The posterior probability of the latent variable is shown to capture similarities across raga renditions. We show that it is possible to visualize the similarities in a low-dimensional embedded space. Experiments show that it is possible to compare and contrast relations and distances between ragas in the embedded space with the musicological knowledge of the same for both Hindustani and Carnatic music forms. The proposed approach also shows robustness to duration of rendition. [ABSTRACT FROM AUTHOR]