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Collective Experience: A Database-Fuelled, Inter-Disciplinary Team-Led Learning System
Leo A. Celi,Roger G. Mark,Joon Lee,Daniel J. Scott,Trishan Panch 한국정보과학회 2012 Journal of Computing Science and Engineering Vol.6 No.1
We describe the framework of a data-fuelled, interdisciplinary team-led learning system. The idea is to build models using patients from one’s own institution whose features are similar to an index patient as regards an outcome of interest, in order to predict the utility of diagnostic tests and interventions, as well as inform prognosis. The Laboratory of Computational Physiology at the Massachusetts Institute of Technology developed and maintains MIMIC-Ⅱ, a public deidentified high- resolution database of patients admitted to Beth Israel Deaconess Medical Center. It hosts teams of clinicians (nurses, doctors, pharmacists) and scientists (database engineers, modelers, epidemiologists) who translate the day-to-day questions during rounds that have no clear answers in the current medical literature into study designs, perform the modeling and the analysis and publish their findings. The studies fall into the following broad categories: identification and interrogation of practice variation, predictive modeling of clinical outcomes within patient subsets and comparative effectiveness research on diagnostic tests and therapeutic interventions. Clinical databases such as MIMIC-Ⅱ, where recorded health care transactions - clinical decisions linked with patient outcomes - are constantly uploaded, become the centerpiece of a learning system.
Akshay P. Ware,Faiyaz K. Shaikh,Archana N. Panche,Sanjay N. Harke 한국응용곤충학회 2019 Journal of Asia-Pacific Entomology Vol.22 No.1
Gut proteases are accountable for survival of Helicoverpa armigera on protein rich parts of plant devastating many important agricultural crops. The aim of present study was to identify potential natural compounds having inhibitory potency against Helicoverpa armigera gut proteases. We have modeled structure of H. armigera serine protease (UniProt ID: O18447) and analyzed its interactions with maslinic acid (Zinc ID: ZINC38140521). A 3D model was generated using bovine trypsin in complex with analogues of sunflower inhibitor 1 as template with the help of Chimera Modeler 1.11. The PROCHECK and Modfold analysis have revealed 81.8% of residue in favored region. The POOL and COACH analysis have revealed 18 amino acids in the active site. In the 10 ns MD simulations of modeled structure, the RMSD of the protein backbone increased slightly and later stabilized from 7 ns to 10 ns. The modeled structure was stabilized at gyration distance of about 1.65 nm at 7 ns. Potential hit compounds from the ZINC database identified in this study showed good inhibitory bindings with modeled structure. Among these compounds maslinic acid, a plant based pentacyclic triterpenes was found to be potent lead compound with good binding affinity (−9.5 kcal/mol). RMSD profile was < 0.45 nm for complex with stabilization at about 18,000 ps (18 nm) suggesting stable interaction. This work demonstrates reasonable in silico inhibitory action of maslinic acid against H. armigera serine protease and depicts utility of in silico methodologies for designing competent strategies against dreaded insect pests like H. armigera.
Collective Experience: A Database-Fuelled, Inter-Disciplinary Team-Led Learning System
Celi, Leo A.,Mark, Roger G.,Lee, Joon,Scott, Daniel J.,Panch, Trishan Korean Institute of Information Scientists and Eng 2012 Journal of Computing Science and Engineering Vol.6 No.1
We describe the framework of a data-fuelled, interdisciplinary team-led learning system. The idea is to build models using patients from one's own institution whose features are similar to an index patient as regards an outcome of interest, in order to predict the utility of diagnostic tests and interventions, as well as inform prognosis. The Laboratory of Computational Physiology at the Massachusetts Institute of Technology developed and maintains MIMIC-II, a public deidentified high- resolution database of patients admitted to Beth Israel Deaconess Medical Center. It hosts teams of clinicians (nurses, doctors, pharmacists) and scientists (database engineers, modelers, epidemiologists) who translate the day-to-day questions during rounds that have no clear answers in the current medical literature into study designs, perform the modeling and the analysis and publish their findings. The studies fall into the following broad categories: identification and interrogation of practice variation, predictive modeling of clinical outcomes within patient subsets and comparative effectiveness research on diagnostic tests and therapeutic interventions. Clinical databases such as MIMIC-II, where recorded health care transactions - clinical decisions linked with patient outcomes - are constantly uploaded, become the centerpiece of a learning system.