Non-swarm-based computational approach for mining cancer drug target modules in protein interaction network.

Cancer is a lethal disease that drew the entire world over the past decades. Currently, numerous researches focused on these cancer treatments. Most familiar among them is the targeted therapy; a customized treatment type depends on the cancer drug targets. Further, the selection of targets is a quite sensitive task. The computational approaches are lagging in this field. This paper is intended to propose an optimized multi-functional score-based co-clustering with MapReduce (MR-CoCopt) approach for drug target module mining with optimal functional score set selection. This approach uses biological functional measures for co-clustering, MapReduce framework for handling redundant modules and complex protein interaction network (PIN), and non-swarm intelligence algorithm-bladderworts suction for optimal functional score set selection. It extracts the cancer-specific drug target modules in protein interaction networks. The protein complex coverage of the results is compared with the existing approach. The biological significance of the results is analyzed for the presence of cancer drug targets and drug target characteristics. From these results, novel cancer drug target modules are suggested for the targeted therapy and the active pharmaceutical drugs for these modules are also highlighted.

Ovarian masses: changing clinico histopathological trends.

OBJECTIVE: To study the clinical and histopathological presentation of ovarian masses. METHOD: Retrospective analysis of 205 cases from May 2009 to June 2013. RESULTS: Incidence of ovarian masses was 6.9 %. Among 205 cases, 68 % were neoplastic. Among the neoplasms, 87.8 % were benign, 10 % malignant, and 2.2 % borderline. Mean ages of malignant and benign neoplasm were 41 and 39 years, respectively. 42.9 % malignant tumors presented with non-specific abdominal and constitutional symptoms. Serous cystadenoma was the commonest benign tumor (67 %) followed by Mucinous (19 %) and Dermoid (11.6 %). Most common malignant ovarian tumor was Serous cystadenocarcinoma (42.9 %). Out of the malignant cases, all were primary except one secondary deposit from Non-Hodgkin's Lymphoma. Only 28.6 % presented at stage I, remaining presented at stage III/IV. CONCLUSION: Ovarian neoplasms have twice the incidence of non-neoplasms. Mean age of malignant tumors is decreased. Rising trend in Mucinous cystadenoma is noted.

Production and optimization of indole acetic acid by indigenous micro flora using agro waste as substrate.

Indole Acetic Acid (IAA) producing bacterium was isolated from the Rhizosphere soil and identified as Rhizobium sp. and Bacillus sp., Optimization of Indole acetic acid production was carried out at different cultural conditions, such as pH, temperature and substrate with Rhizobium sp., Bacillus sp. and Rhizobium sp., produced higher amount of Indole acetic acid (6.1 mg mL(-1)) than the Bacillus sp., (4.4 mg mL(-1)) at pH 7 and 37 degrees C in the Bengal gram substrate. Partial purification of Indole acetic acid was done by Thin Layer Chromatography (TLC). In conclusion Rhizobium sp., appear to be a suitable soil microorganism for high level of IAA production.