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.

Identification of natural inhibitors of Bcr-Abl for the treatment of chronic myeloid leukemia.

Chronic myeloid leukemia (CML) is a clonal myeloproliferative disorder of the hematopoietic stem cells, characterized at the molecular level by the bcr/abl gene rearrangement. Even though targeting the fusion gene product Bcr-Abl protein is a successful strategy, development of drug resistance and that of drug intolerance are currently the limitations for Bcr-Abl-targeted CML therapy. With an aim to develop natural Bcr-Abl inhibitors, we performed virtual screening (VS) of ZINC natural compound database by docking with Abl kinase using Glide software. Two natural inhibitors ZINC08764498 (hit1) and ZINC12891610 (hit2) were selected by considering their high Glide docking score and critical interaction with the hinge region residue Met-318 of Abl kinase. The reactivity of the two molecules was assessed computationally by density functional theory calculations. Further, the conformational transition, hydrogen bond interactions, and the binding energies were investigated during 10-ns molecular dynamics simulation of the Abl-hit complex. When tested in vitro, hit1 compared to hit2 showed selective inhibition of cell proliferation and induction of apoptosis in Bcr-Abl-positive K-562 leukemia cells. In summary, our results demonstrate that ZINC08764498, a coumarin derivative identified through VS, is a potential natural inhibitor for the treatment of CML.