No abstract

Around 50.9 Million People in India suffer from diabetics and Tamil Nadu stands second in the list of Indian states. The main objective of this paper is to develop prediction modeling of the given medical data of patients with and without diabetics. Through this paper, we aim to create hybrid models that can be easily used by doctors to treat patients with diabetics. Naïve Bayes and Random forest algorithms are used to predict whether a person having diabetics or not, by keeping his health conditions in mind. Thus this process enables doctors to easily group, classify and categorize the disease type accordingly treatment can be given to them. We split the Dataset into 1) Training set and 2) Testing Set and perform analysis on them. The Pima Indian dataset was used to study and analyze the data, alongside with data mining techniques. It is the data obtained from the National Institute for Diabetics patients which contains n number of medical predictor variables and one target variable. Initially, we replace the null values that are there in the dataset with the mean values of the respective columns. We then split the dataset into different ways to perform analysis on them: 85/15, 80/20, 70/30, 60/40. After procuring the data set, we apply Naïve Bayes and Random Forest algorithms on this. The Naïve Bayes algorithm is used here to find the probability of the independent features/columns. The data set is given as an input and the prediction takes place according to the NB Model. The Random Forest algorithm is used here in order to perform feature selection. It takes n inputs from the dataset and builds numerous uncorrelated decision trees during the time of training. It then displays the class that is the mode of all of the class outputs by individual trees.

Background: Colon cancer is one of the most common malignancies in many regions of the world and is thought to arise from the accumulation of mutations in a single epithelial cell of the colon and rectum. The benzimidazole comprises a important pharmacophore and privileged structure in modern drug discovery. Various substituted benzimidazole derivatives have been found to possess potential anticancer properties. Objective: The study aimed to prove the anti-colon cancer activity of novel benzimidazole derivative 4-(1H-benzo[d]imidazol-2-yl)-6-phenylpyrimidin-2-amine loaded chitosan nanoparticle (BZI 3 nano) by an 1, 2 Dimethylhydrazine (DMH) Induced rat model in-vivo study and identify the targeting efficiency of BZI 3 nano to treat colorectal cancer. Method: The effect of novel benzimidazole derivative 4-(1H-benzo[d]imidazol-2-yl)-6-phenylpyrimidin-2-amine loaded chitosan nanoparticle (BZI 3 nano) on the formation of aberrant crypt foci (ACF), apoptosis, histopathology, body weight, organs weight and heamotological parameters were studied in 1,2-dimethylhydrazine (DMH)-induced colon cancer in rats. Results: BZI 3 nano (5 mg/kg, p.o) administration significantly reduced ACF number and increased the weight gain and apoptotic index compared to DMH treated group. The histological alterations induced by DMH were also significantly improved. Conclusion: In-vivo anticancer activities results revealed that the presence substituted benzimidazole derivative nanoparticle (BZI 3 nano) could have the anticancer potential of the scaffold and selective, good target for drug discovery, which can be regarded as promising anticancer potential.

Objective: To understand the essential structural features required for pancreatic lipase (PL) inhibitory activity and to design novel chemical entities, ligand-based pharmacophore modeling, virtual screening and docking studies were carried out. Methods:The pharmacophore model was generated based on 133 compounds with PL inhibitory activity using PHASE. An external test set and decoy dataset methods were applied to validate the hypothesis and to retrieve potential PL inhibitors. The generated hypothesis model was further subjected to virtual screening and molecular docking studies. Results:A five point pharmacophoric hypothesis model which consists of three hydrogen bond acceptor sites and two hydrophobic sites was developed. The generated pharmacophore gave significant 3D QSAR (three-dimensional Quantitative Structural Activity Relationship) model with r 2 of 0.9389 and Q 2 value of 0.4016. After database screening, five molecules were found to have better glide scores and binding interactions with the active site amino acid residues. Conclusion:As an outcome of this study, five hit molecules were suggested as potent PL inhibitors as they showed good glide scores as well as binding interactions with required active site amino acids. The five molecules obtained from this study may serve as potential leads for the development of promising anti-obesity agents.

Averrhoa bilimbi is a tropical plant which is commonly known as Bilimbi. The plant has enormous economic importance since its leaves, flower, fruit, bark, seeds, roots or the whole plant are used to treat a variety of diseases in the alternative system of medicine. In the present work, attempt was made to isolate a flavonoid compound from Averrhoa bilimbi. From the methanolic extract of the fruits of Averrhoa bilimbi, a pentahydroxyl flavanonol has been isolated as a major compound for the first time in this plant. The isolate was purified, analysed and characterised by using UV, FTIR, Mass, NMR, HPTLC and HPLC. The Rf value for HPTLC was found to be 0.24, λ max of UV spectra was obtained at 277 nm and retention time in HPLC was 2.55. The structure of this isolated compound has been characterised as dihydromyricetin i.e '(2R,3R)-3,5,7-trihydroxy-2-(3,4,5trihydroxyphenyl)-2,3-dihydrochromen-4-one' with molecular formula C 15 H 12 O 8 and molecular mass 320.0529. The structure is established on the basis of 1D and 2D Nuclear Magnetic Resonance (NMR) and also High Resolution Mass Spectral i.e HRMS data.