Envisioning a learning surveillance system for tuberculosis.

Surveillance is critical for interrupting transmission of global epidemics. Research has highlighted gaps in the surveillance for tuberculosis that range from failure to collect real-time data to lack of standardization of data for informed decision-making at different levels of the health system. Our research aims to advance conceptual and methodological foundations for the development of a learning surveillance system for Tuberculosis, that involves systematic collection, analysis, interpretation, and feedback of outcome-specific data. It would concurrently involve the health care delivery system, public health laboratory, and epidemiologists. For our study, we systemically framed the cyber environment of TB surveillance as an ontology of the learning surveillance system. We validated the ontology by binary coding of dimensions and elements of the ontology with the metadata from an existing surveillance platform-GPMS TB Transportal. Results show GPMS TB Transportal collects a critical range of data for active case investigation and presumptive case screening for identifying and detecting confirmed TB cases. It is therefore targeted at assisting the Active Case Finding program. Building on the results, we demonstrate enhanced surveillance strategies for GPMS that are enumerated as pathways in the ontology. Our analysis reveals the scope for embedding learning surveillance pathways for digital applications in Direct Benefit Transfer, and Drug Resistance Treatment in National TB Elimination Programme in India. We discuss the possibilities of developing the transportal into a multi-level computer-aided decision support system for TB, using the innumerable pathways encapsulated in the ontology.

A novel terpenoid from elephantopus scaber - antibacterial activity on Staphylococcus aureus: a substantiate computational approach.

Staphylococcus aureus has gained much attention in the last decade as it is a major cause of the Urinary Tract Infection in Diabetic patients. The Extended Spectrum ß-Lactamases (ESßL) producers are highly resistant to several conventional antibiotics. This limits the therapeutic options.Hence efforts are now taken to screen few medicinal plants, which are both economic and less toxic. Among the several plants screened, we have chosen the acetone extract of Elephantopus scaber from which we purified a new terpenoid for our study. Its structure was generated using CHEMSKETCH software and the activity prediction was done using PASS PREDICTION software. We have confirmed the mechanism of anti-bacterial effect of terpenoid using Computer - Aided Drug Design (CADD) with computational methods to simulate drug - receptor interactions. The Protein-Ligand interaction plays a significant role in the structural based drug designing. In this present study we have taken the Autolysin, the bacteriolytic enzyme, that digest the cell wall peptidoglycon. The autolysin and terpenoid were docked using HEX docking software and the docking score with minimum energy value of -209.54 was calculated. It infers that the terpenoid can inhibit the activity of autolysin by forming a strong atomic interaction with the active site residues. Hence the terpenoid can act as a drug for bacterial infections. Further investigations can be carried out to predict the activity of terpeniod on other targets.