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Saroglitazar, a dual peroxisome proliferator activated receptor α/γ agonist, approved for diabetic dyslipidemia (DD), is potential therapeutic option for non-alcoholic fatty liver disease (NAFLD). This prospective, observational, real-world study aimed to determine efficacy and safety of Saroglitazar in patients with NAFLD and DD. We included patients with DD and NAFLD who received Saroglitazar 4 mg once daily for 24 weeks. Blood investigations, liver stiffness measurement (LSM) and controlled attenuation parameter (CAP) (FibroScan) were compared at baseline and 24 weeks. Of 163 patients screened, 107 were included, and 101 completed 24 weeks treatment (mean age 50.4 ± 12.3 years, 78.5% males, mean body mass index 28.8 ± 4.2). After 24 weeks, alanine transaminase (ALT) reduced significantly from 94 (47–122) to 39 (31–49) (p < 0.0001) and aspartate aminotransferase (AST) (U/L) from 89 (43–114) to 37 (30–47) (p < 0.0001) and LSM (kPa) from 8.4 (7.1–9.3) to 7.5 (6.4–8.4) (p = 0.0261). CAP, glycated hemoglobin and lipid parameters also improved significantly. On linear regression, there was significant association between percent change in ALT and AST with TG reduction after treatment (p = 0.024 and 0.037 respectively).We conclude that Saroglitazar leads to significant improvement in transaminases, LSM, and CAP in NAFLD patients with DD.

Thresholding and edge detection being one of the important aspects of image segmentation comes prior to feature extraction and image recognition system for analyzing images. It helps in extracting the basic shape of an image, overlooking the minute unnecessary details. In this paper using image segmentation (thresholding and edge detection) techniques different geo satellite images, medical images and architectural images are analyzed. To quantify the consistency of our results error measure is used.

Atorvastatin does not affect treatment outcome in CSME with a normal lipid profile over a short term follow-up.

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