The collected data should be readily usable for epidemiological research and in fact numerous reports have been published utilizing this data.
Two outcomes studies in geriatric and medical-surgical patients in the critical care setting have recently been published utilizing Subjective Global Assessment (SGA) (Atalay et al, 2008; Sungertken et al, 2008) reporting mixed results between malnutrition and mortality endpoints.
Since then, several articles were published utilizing this technique.
In response to these shortcomings, computational methods for the prediction of PPISs have been developed, starting with Jones and Thornton's pioneering analysis of surface patches [ 5, 6], and many predictors have since been published [ 7- 43], utilizing a wide variety of algorithmic approaches to the problem.
Several imaging studies have been published on utilizing F-FDG PET and cardiac MRI for the diagnosis of CS to date, but almost all of them were validated using JMHW's original guidelines [ 32– 40].
Several reports have been published to date, utilizing sequencing technologies to characterize the RNA content of exosomes derived from THP1 and HUVEC cells [ 22], DC cells [ 23], hES-MSC cells [ 24], placenta [ 25], and breast milk [ 26].
Most work that has been published using this model utilized a prophylactic dosing regimen, where treatment was started prior to disease onset.
Nonetheless, only a few QTL studies have been published that have utilized ESTs and SNP in pines (Markussen et al. 2003; Brown et al. 2003; Pot et al. 2006).
To further validate our prediction system, we tested our PCT-TC-derived predictors on two other groups of breast cancer patients whose survival and microarray expression data have been published but which utilized different microarray platforms [a Duke University report, using Affymetrix arrays [4], and a University of North Carolina (UNC) report, using cDNA spotted arrays [5]].
