Background: The potential for PPAR agonists to positively affect risk of cardiovascular disease in patients with type 2 diabetes (T2DM) is of persistent attention. The PRESS XII study primarily aimed to evaluate the efficacy and safety of saroglitazar (2 mg and 4 mg) as compared to pioglitazone 30 mg on glycemic control in patients with type 2 diabetes mellitus. Methods: In this randomized double-blind study, patients with T2DM [glycosylated hemoglobin (HbA1c) ≥ 7.5%] were enrolled from 39 sites in India. Patients received once-daily doses of either saroglitazar or pioglitazone (1:1:1 allocation ratio) for a total of 24 weeks. Patients were continued in a double blind extension period for an additional 32 weeks. Efficacy evaluations of glycemic parameters [HbA1c (Primary endpoint at week 24), FPG and PPG] and other lipid parameters (TG, LDL-C, VLDL-C, HDL-C, TC, Non HDL-C, Apo A1 and Apo B) were conducted at week 12, 24 and 56 and compared to the baseline levels. The efficacy analyses were performed by using paired t-test and ANCOVA model. Results: A total of 1155 patients were enrolled in this study. The baseline characteristics were similar between the three treatment groups. The within group mean (± SD) change in HbA1c (%) from baseline of the saroglitazar (2 mg and 4 mg) and pioglitazone treatment groups at week 24 were: − 1.38 ± 1.99 for saroglitazar 2 mg; − 1.47 ± 1.92 for saroglitazar 4 mg and − 1.41 ± 1.86 for pioglitazone, respectively. Statistically significant reduction from baseline in HbA1c was observed in each treatment group at week 24 with p-value < 0.016. There was a significant reduction in TG, LDL-C, VLDL-C, TC and Non HDL-C with a significant increase in HDL-C from baseline levels (< 0.016). Most of the AE's were 'mild' to 'moderate' in severity and were resolved by the completion of the study.

Introduction Hand hygiene practice, as correctly said, is the backbone of infection control and it has been proven to limit infections in hospital settings. Currently most healthcare facilities monitor hand hygiene compliance by direct observation technique. We decided to use video surveillance as a tool to monitor hand hygiene compliance and its impact. Materials and Methods This study was conducted over a period of 6 months from March 2018 to August 2018 at Apex Hospital, Jaipur, India. We compared direct observation of ICU, High Dependency Units, and Emergency with video surveillance in these areas. Results and Observations In this study, direct observation and video audit were compared from March 2018 to August 2018. During March to August, average compliance rates of direct observation and video surveillance were compared. In month of march, they were 67% and 20%, respectively and in the month of august, they were 81% and 47%, respectively. Conclusion In our study, We can conclude in our study that video monitoring combined with direct observation can produce a significant and sustained improvement in hand hygiene compliance and can improve quality of patient care. How to cite this article Sharma S, Khandelwal V, Mishra G. Video Surveillance of Hand Hygiene: A Better Tool for Monitoring and Ensuring Hand Hygiene Adherence. Indian J Crit Care Med 2019;23(5):224–226.

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This study highlights the importance of the information contained extreme value ratios (or returns) in the volatility estimation of financial assets. Most popular extreme value estimators like Parkinson (1980), Garman Klass (1980), Rogers Satchell (1991) and Yang Zhang (2000) use a subset of all available extreme value ratios but not the full set. We examine if there are other extreme value ratios which contain more information than the most widely used ratios. This study shows empirically how much information is contained in various extreme value ratios of financial assets, using both real and simulated data. Using information theory, we find out their variability in relation to a uniform distribution in each quarter. We then rank them using the Kullback-Leibler metric (in accordance with a scoring methodology we developed in this study) to ascertain which set of ratios are more variable than others and thus may provide better estimation in computing volatility. We also calculate the rank of the matrix to identify the set of linearly independent ratios, for ascertaining the number of ratios that would be enough to generate a class of volatility estimators. The empirical results demonstrate that the need for incorporating other ratios in volatility estimation. We also observe that each dataset has other more informative ratios which are uniquely attributed to that dataset.