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3 results on '"Venu M. Pothula"'

1. A Role for Noncoding Variation in Schizophrenia

Author

Panos Roussos, Amanda C. Mitchell, Georgios Voloudakis, John F. Fullard, Venu M. Pothula, Jonathan Tsang, Eli A. Stahl, Anastasios Georgakopoulos, Douglas M. Ruderfer, Alexander Charney, Yukinori Okada, Katherine A. Siminovitch, Jane Worthington, Leonid Padyukov, Lars Klareskog, Peter K. Gregersen, Robert M. Plenge, Soumya Raychaudhuri, Menachem Fromer, Shaun M. Purcell, Kristen J. Brennand, Nikolaos K. Robakis, Eric E. Schadt, Schahram Akbarian, and Pamela Sklar

Subjects
Biology (General), QH301-705.5
Abstract

A large portion of common variant loci associated with genetic risk for schizophrenia reside within noncoding sequence of unknown function. Here, we demonstrate promoter and enhancer enrichment in schizophrenia variants associated with expression quantitative trait loci (eQTL). The enrichment is greater when functional annotations derived from the human brain are used relative to peripheral tissues. Regulatory trait concordance analysis ranked genes within schizophrenia genome-wide significant loci for a potential functional role, based on colocalization of a risk SNP, eQTL, and regulatory element sequence. We identified potential physical interactions of noncontiguous proximal and distal regulatory elements. This was verified in prefrontal cortex and -induced pluripotent stem cell–derived neurons for the L-type calcium channel (CACNA1C) risk locus. Our findings point to a functional link between schizophrenia-associated noncoding SNPs and 3D genome architecture associated with chromosomal loopings and transcriptional regulation in the brain.

Published
2014
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2. Neural Network Prediction of ICU Length of Stay Following Cardiac Surgery Based on Pre-Incision Variables

Author

Venu M. Pothula, Keshar Paul Kubal, Suryanarayana Pothula, Albert Yusupov, M. E. Inchiosa, Richard Perline, Stephen M. Oleszkiewicz, Mario A. Inchiosa, Rocco J. Lafaro, Stanley C. Yuan, David A. Maerz, and Lucresia Montes

Subjects
Male, medicine.medical_specialty, lcsh:Medicine, Hematocrit, Risk Assessment, Cross-validation, medicine, Odds Ratio, Humans, Cardiac Surgical Procedures, lcsh:Science, Multidisciplinary, medicine.diagnostic_test, Artificial neural network, business.industry, lcsh:R, Linear model, Odds ratio, Predictive analytics, Length of Stay, Cardiac surgery, Surgery, Intensive Care Units, Quartile, Emergency medicine, Linear Models, Female, lcsh:Q, Neural Networks, Computer, business, Research Article
Abstract

Background Advanced predictive analytical techniques are being increasingly applied to clinical risk assessment. This study compared a neural network model to several other models in predicting the length of stay (LOS) in the cardiac surgical intensive care unit (ICU) based on pre-incision patient characteristics. Methods Thirty six variables collected from 185 cardiac surgical patients were analyzed for contribution to ICU LOS. The Automatic Linear Modeling (ALM) module of IBM-SPSS software identified 8 factors with statistically significant associations with ICU LOS; these factors were also analyzed with the Artificial Neural Network (ANN) module of the same software. The weighted contributions of each factor (“trained” data) were then applied to data for a “new” patient to predict ICU LOS for that individual. Results Factors identified in the ALM model were: use of an intra-aortic balloon pump; O2 delivery index; age; use of positive cardiac inotropic agents; hematocrit; serum creatinine ≥ 1.3 mg/deciliter; gender; arterial pCO2. The r2 value for ALM prediction of ICU LOS in the initial (training) model was 0.356, p

Published
2015

3. Neural Network Prediction of ICU Length of Stay Following Cardiac Surgery Based on Pre-Incision Variables.

Author

Rocco J LaFaro, Suryanarayana Pothula, Keshar Paul Kubal, Mario Emil Inchiosa, Venu M Pothula, Stanley C Yuan, David A Maerz, Lucresia Montes, Stephen M Oleszkiewicz, Albert Yusupov, Richard Perline, and Mario Anthony Inchiosa

Subjects
Medicine, Science
Abstract

BACKGROUND:Advanced predictive analytical techniques are being increasingly applied to clinical risk assessment. This study compared a neural network model to several other models in predicting the length of stay (LOS) in the cardiac surgical intensive care unit (ICU) based on pre-incision patient characteristics. METHODS:Thirty six variables collected from 185 cardiac surgical patients were analyzed for contribution to ICU LOS. The Automatic Linear Modeling (ALM) module of IBM-SPSS software identified 8 factors with statistically significant associations with ICU LOS; these factors were also analyzed with the Artificial Neural Network (ANN) module of the same software. The weighted contributions of each factor ("trained" data) were then applied to data for a "new" patient to predict ICU LOS for that individual. RESULTS:Factors identified in the ALM model were: use of an intra-aortic balloon pump; O2 delivery index; age; use of positive cardiac inotropic agents; hematocrit; serum creatinine ≥ 1.3 mg/deciliter; gender; arterial pCO2. The r2 value for ALM prediction of ICU LOS in the initial (training) model was 0.356, p

Published
2015
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