Publications by authors named "D Prater"
This work rests on our recent report on the successful use of tissue nanotransfection (TNT) delivery of Ascl1, Brn2, and Myt1l (TNT) to directly convert skin fibroblasts into electrophysiologically active induced neuronal cells (iN) in vivo. Here we report that in addition to successful neurogenic conversion of cells, TNT caused neurotrophic enrichment of the skin stroma. Thus, we asked whether such neurotrophic milieu of the skin can be leveraged to rescue pre-existing nerve fibers under chronic diabetic conditions.
View Article and Find Full Text PDFArticle Synopsis
- The study evaluated a new machine learning-based software for measuring right ventricular (RV) size and function using three-dimensional echocardiography (3DE).
- It found that the automated algorithm was accurate and reproducible in 32% of patients without the need for adjustments, while minor edits were necessary for the remaining patients but still yielded good accuracy.
- These findings suggest that ML-enhanced 3DE could improve routine clinical practice for assessing RV function, though it may still require some manual intervention.
Background: Although 3D echocardiography (3DE) circumvents many limitations of 2D echocardiography by allowing direct measurements of left ventricular (LV) mass, it is seldom used in clinical practice due to time-consuming analysis. A recently developed 3DE machine learning (ML) approach allows automated determination of LV mass. We aimed to evaluate the accuracy of this new approach by comparing it to cardiac magnetic resonance (CMR) reference and to conventional 3DE volumetric analysis.
View Article and Find Full Text PDFMachine learning based automated dynamic quantification of left heart chamber volumes.
Eur Heart J Cardiovasc Imaging
May 2019
Aims: Studies have demonstrated the ability of a new automated algorithm for volumetric analysis of 3D echocardiographic (3DE) datasets to provide accurate and reproducible measurements of left ventricular and left atrial (LV, LA) volumes at end-systole and end-diastole. Recently, this methodology was expanded using a machine learning (ML) approach to automatically measure chamber volumes throughout the cardiac cycle, resulting in LV and LA volume-time curves. We aimed to validate ejection and filling parameters obtained from these curves by comparing them to independent well-validated reference techniques.
View Article and Find Full Text PDFObjectives: The goal of this study was to test the feasibility and accuracy of an automated algorithm that simultaneously quantifies 3-dimensional (3D) transthoracic echocardiography (TTE)-derived left atrial (LA) and left ventricular (LV) volumes and left ventricular ejection fraction (LVEF). Conventional manual 3D TTE tracings and cardiac magnetic resonance (CMR) images were used as a reference for comparison.
Background: Cardiac chamber quantification from 3D TTE is superior to 2D TTE measurements.