iosr-JPBS   Volume-5 ~ Issue-4

Abstract: Free radical oxidative injury has been linked to myocardial infarction. This study has been taken to analyse the extent of oxidative injury and the antioxidant defensive mechanism by estimating Malonaldehyde and defensive markers such as serum Ceruloplasmin,serum Catalase antioxidant defence by S. Uric Acid and S. Ceruloplasmin. On 5th day of MI, oxidative and serum Uric acid at 6-12 hrs of onset of myocardial infarction and 5 days after thrombolytic therapy.Out of total 100 cases,50 age matched Control cases and 50 cases of known Myocardial infarction(with ECG report) are taken.Serum Malonaldehyde is estimated by thiobarbituric acid method.Serum Ceruloplasmin by O-diansidine method,serum Uric acid by uricase kit method.Serum Catalase activity by Spectrophotometric assay of complex of Molybdate and Hydrogen peroxide. Routine Blood sugar,blood urea,Serum creatinine,Haemoglobin %,HIV,HBs Ag also done .The data is statistically analysed and the mean,SD values are calculated. Student 't' test and 'p' values are also calculated.Statistically significant increase in Malonaldehyde, Uric acid,Ceruloplasmin in MI cases compared to controls with 'p' values <0.000 is seen.Significant decrease in serum Catalase in cases compared to patients with 'p' value <0.000 is seen.There is a significant decrease in serum Uric acid,Malonaldehyde,Ceruloplasmin and increase in serum Catalase after 5 days of Thrombolytic therapy.The area under table of the ROC curves of different parameter is compared.In present study there is significant increase in serum Malonaldehyde, Uric acid,Ceruloplasmin levels in MI cases compared to controls. Malonaldehyde is the best markers to indicate Oxidative Stress In MI.

Key Words: Ceruloplasmin , Malonaldehyde , MI-Myocardial infarction, Reactive oxygen species and Uric acid

Abstract:In this paper, we explore the use of statistical learning approaches to predict drug-target like proteins from their primary sequences in order to facilitate the rapid discovery of new potential therapeutic targets from the large quantity of sequences in human genome. It was found that the Support Vector Machine (SVM) algorithm with a fine-tuned Gaussian kernel was able to make reasonably accurate prediction, which showed its potential to be used in the genome scale rapid drug target discovery, as a novel in silico approach supplementary to the conventional experimental approaches.

Keyword: SVM, Therapeutic, PCA, ICA

Abstract: Hypertension is a heterogenous disorder in which patients can be stratified by pathophysiology characteristics that have a direct bearing, on the efficacy of specifically targeted antihypertensive medications, on the detection of potentially curable forms of hypertension and on the risk of cardiovascular complications.The study is done to compare the effect of antihypertensive treatment with Enalapril and Atenolol on oxidative stress and insulin resistance in patients with essential hypertension .Plasma Malonaldehyde(MDA), reduced Glutathione and Catalase were estimated to measure oxidative stress and fasting plasma Insulin to measure Insulin resistance....

Keywords – Atenolol,,Catalase,Enalapril,Insulin resistance,Malonaldehyde,oxidative stress

Abstract: This study investigates the in vitro interactions of lisinopril with metformin which is important for the possible formulation of polypills comprising both drugs. The study was conducted using the USP apparatus II paddle method. The media used were buffer pH 1.2, 4.5, and 6.8 to simulate gastrointestinal tract pH. Lisinopril tablets (10 mg) were run in the presence of 500 mg metformin tablets and the degree of interaction was established after analyzing the dissolution samples with validated High Performance Liquid Chromatographic methods. The results indicated a statistically significant difference in the effect of lisinopril on the dissolution profile of metformin at pH 1.2, 4.5, and 6.8, with a decrease in the percentage of metformin released (p = 0.0052; p = 0.0037; and p = 0.0155, respectively). The effect of metformin on the dissolution profile of lisinopril was only statistically significant at pH 1.2 and 6.8 with an increase in the percentage of lisinopril released (p = 0.0062; p =0.0036, respectively), and no statistically significant difference at pH 4.5 (p = 0.7174). Thus, the co-administration of metformin with lisinopril could alter the bioavailability of both drugs. More studies may be conducted in vivo to further ascertain the level of interactions using animal or human model.

Keywords – Dissolution, Interactions, Lisinopril, Metformin, Polypills