Background: To distinguish the characteristic hand pattern of each of the three different ethnicities in Malaysia and to study the hand pattern correlation between race and gender. Method: Individual lengths of the fingers were then measured and tabulated to serve as the basis for analyzing the 2D (second digit):4D (fourth digit) hand ratio. Based on this ration, the hand patterns were classified as A, B, and C types. Results: Hand pattern A (2D<4D) appears to be the most characteristic trait in Malays. The highest scoring hand pattern in Chinese is A as well with scores of 52% in their right hands and 60% in their left hands. In Indians, hand pattern C (2D>4D) shows dominance in their right hands with a score of 46% while hand pattern A dominates their left hands. Among the males, all three races show dominance in hand pattern A except in Chinese whereby the C hand pattern was dominant in their right hands (44%). Among the females, the most common trait in Malays and Chinese are the hand pattern A in both their hands. Indian females, however, showed dominance in hand pattern C in their right hands (60%), and hand pattern B (2D = 4D) dominated in their left hands (44%). Results of the statistical analysis revealed that there was a highly significant difference in the hand patterns of both the hands when compared to gender. Conclusion: The study suggests that ratio below or equal to 0.90 is suggestive of female sex for both hands, while a ratio of more than 0.91 is suggestive of male sex for both hands. The pattern A (2D<4D) is seen to be the most common trait among the three ethnicities of Malaysia with an exception seen in the right hands of Indians.

BackgroundWe assessed the predictive accuracy of an empirically-derived score (weight loss, insulin resistance, and glycemic control: “WIG”) to predict patients who will be successful in reducing diabetes mellitus (DM) medication use with weight loss.MethodsCase records of 121 overweight and obese patients with DM at two outpatient weight management centers were analyzed.ResultsMean period of follow-up was 12.5 ± 3.5 months. To derive the “WIG” scoring algorithm, one point each was assigned to “W” (loss of 5% of initial body weight within the first 3 months of attempting weight loss), “I” (triglyceride [TGL]/highdensity lipoprotein ratio >3 [marker of insulin resistance] at baseline), and “G” (glycosylated hemoglobin [A1c%] >8.5 at baseline). WIG score showed moderate accuracy in discriminating anti-DM dose reductions at baseline, and after 3 months of weight loss efforts (likelihood ratios [LR] + >1, LR− <1, and area under the curve >0.7), and demonstrated good reproducibility.ConclusionsWIG score shows promise as a tool to predict success with dose reductions of antidiabetes medications.

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The aim of this research work was to develop a simple, accurate, sensitive and validated Ultra Violet (UV) spectrophotometric assay using the multivariate regression method for the analysis of Cilnidipine. This multivariate calibration technique was based on equations constructed using linear regression analysis using the correlation between absorbance and concentration at five selected equidistant wavelengths. Cilnidipine had a maximum absorbance at 240 nm. The findings were statistically analyzed for significance. A linear plot in the concentration range of 3-9 μg/mL, with a regression coefficient of 0.999 was obtained. The % RSD for intra-day and Inter-day precision were 0.4558 and 0.6099, respectively. The assay was determined and found to be 99.1% - 101.67% % w/w. Keywords: Cilnidipine, Antihypertensive agent, UV spectrophotometry, Multivariate calibration, Assay, ICH guidelines).