A comprehensive study on relationship between body mass index and ABO blood group in patients in and around Lucknow region

Background: ABO blood group is the most important of all blood group systems. A and B antibodies are present naturally in individuals from birth who lack the corresponding antigen on their red cells. Aims and Objective: The main aim of this study was to find out that whether there is any association between ABO blood group and body mass index (BMI) or there is no association between ABO blood group and BMI. Material and Methods: This is a cross-sectional study which was conducted in the Department of Physiology Of IIMSR, Lucknow in collaboration with department of Medicine. Data collection was done over a period of 6 months and a sample size came out to be 384 Anthropometric parameters were recorded using a standardized scale and blood group sampling was done in department of physiology using slide agglutination method. Data were analyzed using SPSS version 17.0. Result: Blood group distribution of individuals with normal BMI was 6.7% with O negative, 3.6% with O positive, 5.4% with A negative, 4.1% with A positive, 6.5 % with B negative, 2.8 % with B positive, 10.4% with AB negative, and 8.3% with AB positive. Conclusion: A significant association was found between the blood groups and obesity. The individuals with blood groups A and AB are more likely to develop obesity than the individuals with blood group O.

CoMFA -3D QSAR approach in drug design.

Progress in medicinal chemistry and in drug design depends on our ability to understand the interactions of drugs with their biological targets. Classical QSAR studies describe biological activity in terms of physicochemical properties of substituents in certain positions of the drug molecules. The detailed discussion of the present state of the art should enable scientists to further develop and improve these powerful new tools. Comparative Molecular Field Analysis (CoMFA) is a mainstream and down-toearth 3D QSAR technique in the coverage of drug discovery and development. Even though CoMFA is remarkable for high predictive capacity, the intrinsic data-dependent characteristic still makes this methodology certainly be handicapped by noise. It's well known that the default settings in CoMFA can bring about predictive QSAR models, in the meanwhile optimized parameters was proven to provide more predictive results. Accordingly, so far numerous endeavors have been accomplished to ameliorate the CoMFA model’s robustness and predictive accuracy by considering various factors, including molecular conformation and alignment, field descriptors and grid spacing. In the present article we are going to discuss the basic approaches of CoMFA in drug design.