Benoit Lewden

In Vivo SPECT Quantification of Transplanted Cell Survival After Engraftment Using 111In-Tropolone in Infarcted Canine Myocardium

Current investigations of cell transplant therapies in damaged myocardium are limited by the inability to quantify cell transplant survival in vivo. We describe how the labeling of cells with 111In can be used to monitor transplanted cell viability in a canine infarction model. Methods: We experimentally determined the contribution of the 111In signal associated with transplanted cell (TC) death and radiolabel leakage to the measured SPECT signal when 111In-labeled cells were transplanted into the myocardium. Three groups of experiments were performed in dogs. Radiolabel leakage was derived by labeling canine myocardium in situ with free 111In-tropolone (n = 4). To understand the contribution of extracellular 111In (e.g., after cell death), we developed a debris impulse response function (DIRF) by injecting lysed 111In-labeled cells within reperfused (n = 3) and nonreperfused (n = 5) myocardial infarcts and within normal (n = 3) canine myocardium. To assess the application of the modeling derived from these experiments, 111In-labeled cells were transplanted into infarcted myocardium (n = 4; 3.1 × 107 ± 5.4 × 106 cells). Serial SPECT images were acquired after direct epicardial injection to determine the time-dependent radiolabel clearance. Clearance kinetics were used to correct for 111In associated with viable TCs. Results: 111In clearance followed a biphasic response and was modeled as a biexponential with a short (\batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} \(\mathrm{T}_{1/2}^{\mathrm{s}}\) \end{document}) and long (\batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} \(\mathrm{T}_{1/2}^{\mathrm{l}}\) \end{document}) biologic half-life. The \batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} \(\mathrm{T}_{1/2}^{\mathrm{s}}\) \end{document} was not significantly different between experimental groups, suggesting that initial losses were due to transplantation methodology, whereas the \batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} \(\mathrm{T}_{1/2}^{\mathrm{l}}\) \end{document} reflected the clearance of retained 111In. DIRF had an average \batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} \(\mathrm{T}_{1/2}^{\mathrm{l}}\) \end{document} of 19.4 ± 4.1 h, and the \batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} \(\mathrm{T}_{1/2}^{\mathrm{l}}\) \end{document} calculated from free 111In-tropolone injected in situ was 882.7 ± 242.8 h. The measured \batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} \(\mathrm{T}_{1/2}^{\mathrm{l}}\) \end{document} for TCs was 74.3 h and was 71.2 h when corrections were applied. Conclusion: A new quantitative method to assess TC survival in myocardium using SPECT and 111In has been introduced. At the limits, method accuracy is improved if appropriate corrections are applied. In vivo 111In imaging most accurately describes cell viability half-life if \batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} \(\mathrm{T}_{1/2}^{\mathrm{l}}\) \end{document} is between 20 h and 37 d.

Data collection with iPhone Web apps efficiently collecting patient data using mobile devices

The use of mobile and ubiquitous computing devices is advantageous for collecting and sharing patient data at the bedside or in hospital waiting areas. iPhone web applications — or web apps — combine the power of Internet based solutions with the simplicity of multi-touch and gesture technology, all one portable device. Since many data collection platforms have moved to an online paradigm (or are in the process of doing so), a web app is an ideal solution. In this work, we show the advantages of using a web app for patient data collection, as an imaging engine, and for patient feedback and survey systems. This can be achieved by taking advantage of simple functions available on the iPhone OS when using an online collection platform.

Cluster Analysis and Decision Trees of MR Imaging in Patients Suffering Alzheimer’s

The use of novel analytical techniques (such as data clustering and decision trees) that can model and predict patient disease outcomes has great potential for assessing disease process and progression in Alzheimer’s disease and mild cognitive impairment. For this study, 43 different variables (generated from image data, demographics and clinical data) have been compiled and analyzed using a modified clustering algorithm. Our aim was to determine the influence of these variables on the incidence of Alzheimer’s and mild cognitive impairment. Furthermore, we used a decision tree algorithm to model the level of “importance” of variants influencing this decision.

Multi-analytical Approaches Informing the Risk of Sepsis

Sepsis is a significant cause of mortality and morbidity and is often associated with increased hospital resource utilization, prolonged intensive care unit (ICU) and hospital stay. The economic burden associated with sepsis is huge. With advances in medicine, there are now aggressive goal oriented treatments that can be used to help these patients. If we were able to predict which patients may be at risk for sepsis we could start treatment early and potentially reduce the risk of mortality and morbidity. Analytic methods currently used in clinical research to determine the risk of a patient developing sepsis may be further enhanced by using multi-modal analytic methods that together could be used to provide greater precision. Researchers commonly use univariate and multivariate regressions to develop predictive models. We hypothesized that such models could be enhanced by using multiple analytic methods that together could be used to provide greater insight. In this paper, we analyze data about patients with and without sepsis using a decision tree approach and a cluster analysis approach. A comparison with a regression approach shows strong similarity among variables identified, though not an exact match. We compare the variables identified by the different approaches and draw conclusions about the respective predictive capabilities,while considering their clinical significance.

Predicting Sepsis: A Comparison of Analytical Approaches

Sepsis is a significant cause of mortality and morbidity and is often associated with increased hospital resource utilization, prolonged intensive care unit and hospital stay. With advances in medicine, there is now aggressive goal oriented treatments that can be used to help patients that may be at risk for sepsis. To predict this risk, we hypothesized that commonly used univariate and multivariate models could be enhanced by using multiple analytic methods to providing greater precision. As a first step, we analyze data about patients with and without sepsis using multiple regression, decision trees and cluster analysis. We compare the predictive accuracy of the three different approaches in predicting which patients are likely (or not likely) to develop sepsis. The precision analysis suggests that decision trees may provide a better predictive model than either regression methods or cluster analysis.