Cardiovascular risk reduction is an important issue in the management of patients with Type 2 diabetes mellitus. Peroxisome proliferator activated receptor (PPAR) agonists favourably influence glycaemic and lipid parameters in patients with Type 2 diabetes and a dual PPAR agonist is expected to have favourable effect on both parameters. In this study we have analyzed the effect of Saroglitazar, a novel dual PPAR alpha & gamma agonist, on glycaemic and lipid parameters in Indian patients with Type 2 diabetes. After a mean follow-up period of 14 weeks in 34 patients, treatment with Saroglitazar, in a dose of 4 mg daily, resulted in significant improvement in both glycaemic and lipid parameters. There were significant mean reductions of fasting plasma glucose (36.71 mg/dl; p = 0.0007), post-prandial plasma glucose (66.29 mg/dl; p = 0.0005), glycosylated haemoglobin (1.13%; p < 0.0001), total cholesterol (48.16 mg/dl; p < 0.0001), low- density lipoprotein cholesterol (24.04 mg/dl; p = 0.0048), triglyceride (192.78 mg/dl; p = 0.0001), non-high density lipoprotein cholesterol (48.72 mg/dl; p < 0.0001) and the ratio of triglyceride and high density lipoprotein cholesterol (5.30; p = 0.0006). There was no significant change in body weight, blood pressure, high-density lipoprotein cholesterol and serum creatinine.

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The objectives of the study were to develop a framework for automatic outer and inner breast tissue segmentation using multi-parametric MRI images of the breast tumor patients; and to perform breast density and tumor tissue analysis. MRI of the breast was performed on 30 patients at 3T-MRI. T1, T2 and PD-weighted(W) images, with and without fat saturation(WWFS), and dynamic-contrast-enhanced(DCE)-MRI data were acquired. The proposed automatic segmentation approach was performed in two steps. In step-1, outer segmentation of breast tissue from rest of body parts was performed on structural images (T2-W/T1-W/PD-W without fat saturation images) using automatic landmarks detection technique based on operations like profile screening, Otsu thresholding, morphological operations and empirical observation. In step-2, inner segmentation of breast tissue into fibro-glandular(FG), fatty and tumor tissue was performed. For validation of breast tissue segmentation, manual segmentation was carried out by two radiologists and similarity coefficients(Dice and Jaccard) were computed for outer as well as inner tissues. FG density and tumor volume were also computed and analyzed. The proposed outer and inner segmentation approach worked well for all the subjects and was validated by two radiologists. The average Dice and Jaccard coefficients value for outer segmentation using T2-W images, obtained by two radiologists, were 0.977 and 0.951 respectively. These coefficient values for FG tissue were 0.915 and 0.875 respectively whereas for tumor tissue, values were 0.968 and 0.95 respectively. The volume of segmented tumor ranged over 2.1 cm3–7.08 cm3. The proposed approach provided automatic outer and inner breast tissue segmentation, which enables automatic calculations of breast tissue density and tumor volume. This is a complete framework for outer and inner breast segmentation method for all structural images.

Tumefactive demyelinating lesions reveal different microstructural changes at different depths of the lesion and this unique feature may be useful in differentiating them from other focal lesions of brain.

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