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Informatics lung transplantation

Annette DeVito Dabbs, Mi-Kyung Song, Brad Myers, Robert P Hawkins, Jill Aubrecht, Alex Begey, Mary Connolly, Ruosha Li, Joseph M Pilewski, Christian A Bermudez, Mary Amanda Dew
BACKGROUND: Despite the proliferation of health information technology (IT) interventions, descriptions of the unique considerations for conducting randomized trials of health IT interventions intended for patient use are lacking. PURPOSE: Our purpose is to describe the protocol to evaluate Pocket PATH (Personal Assistant for Tracking Health), a novel health IT intervention, as an exemplar of how to address issues that may be unique to a randomized controlled trial (RCT) to evaluate health IT intended for patient use...
2013: Clinical Trials: Journal of the Society for Clinical Trials
Gloria Bonuccelli, Aristotelis Tsirigos, Diana Whitaker-Menezes, Stephanos Pavlides, Richard G Pestell, Barbara Chiavarina, Philippe G Frank, Neal Flomenberg, Anthony Howell, Ubaldo E Martinez-Outschoorn, Federica Sotgia, Michael P Lisanti
Previously, we proposed a new model for understanding the "Warburg effect" in tumor metabolism. In this scheme, cancer-associated fibroblasts undergo aerobic glycolysis and the resulting energy-rich metabolites are then transferred to epithelial cancer cells, where they enter the TCA cycle, resulting in high ATP production via oxidative phosphorylation. We have termed this new paradigm "The Reverse Warburg Effect." Here, we directly evaluate whether the end-products of aerobic glycolysis (3-hydroxy-butyrate and L-lactate) can stimulate tumor growth and metastasis, using MDA-MB-231 breast cancer xenografts as a model system...
September 1, 2010: Cell Cycle
Dursun Delen, Asil Oztekin, Zhenyu James Kong
OBJECTIVE: The prediction of survival time after organ transplantations and prognosis analysis of different risk groups of transplant patients are not only clinically important but also technically challenging. The current studies, which are mostly linear modeling-based statistical analyses, have focused on small sets of disparate predictive factors where many potentially important variables are neglected in their analyses. Data mining methods, such as machine learning-based approaches, are capable of providing an effective way of overcoming these limitations by utilizing sufficiently large data sets with many predictive factors to identify not only linear associations but also highly complex, non-linear relationships...
May 2010: Artificial Intelligence in Medicine
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