Acellular bioscaffolds redirect cardiac fibroblasts and promote functional tissue repair in rodents and humans with myocardial injury.

Coronary heart disease is a leading cause of death. Tissue remodeling and fibrosis results in cardiac pump dysfunction and ischemic heart failure. Cardiac fibroblasts may rebuild damaged tissues when prompted by suitable environmental cues. Here, we use acellular biologic extracellular matrix scaffolds (bioscaffolds) to stimulate pathways of muscle repair and restore tissue function. We show that acellular bioscaffolds with bioinductive properties can redirect cardiac fibroblasts to rebuild microvascular networks and avoid tissue fibrosis. Specifically, when human cardiac fibroblasts are combined with bioactive scaffolds, gene expression is upregulated and paracrine mediators are released that promote vasculogenesis and prevent scarring. We assess these properties in rodents with myocardial infarction and observe bioscaffolds to redirect fibroblasts, reduce tissue fibrosis and prevent maladaptive structural remodeling. Our preclinical data confirms that acellular bioscaffold therapy provides an appropriate microenvironment to stimulate pathways of functional repair. We translate our observations to patients with coronary heart disease by conducting a first-in-human observational cohort study. We show that bioscaffold therapy is associated with improved perfusion of infarcted myocardium, reduced myocardial scar burden, and reverse structural remodeling. We establish that clinical use of acellular bioscaffolds is feasible and offers a new frontier to enhance surgical revascularization of ischemic heart muscle.

Semiautomatic vessel wall detection and quantification of wall thickness in computed tomography images of human abdominal aortic aneurysms.

PURPOSE: Quantitative measurements of wall thickness in human abdominal aortic aneurysms (AAAs) may lead to more accurate methods for the evaluation of their biomechanical environment. METHODS: The authors describe an algorithm for estimating wall thickness in AAAs based on intensity histograms and neural networks involving segmentation of contrast enhanced abdominal computed tomography images. The algorithm was applied to ten ruptured and ten unruptured AAA image data sets. Two vascular surgeons manually segmented the lumen, inner wall, and outer wall of each data set and a reference standard was defined as the average of their segmentations. Reproducibility was determined by comparing the reference standard to lumen contours generated automatically by the algorithm and a commercially available software package. Repeatability was assessed by comparing the lumen, outer wall, and inner wall contours, as well as wall thickness, made by the two surgeons using the algorithm. RESULTS: There was high correspondence between automatic and manual measurements for the lumen area (r = 0.978 and r = 0.996 for ruptured and unruptured aneurysms, respectively) and between vascular surgeons (r = 0.987 and r = 0.992 for ruptured and unruptured aneurysms, respectively). The authors' automatic algorithm showed better results when compared to the reference with an average lumen error of 3.69%, which is less than half the error between the commercially available application Simpleware and the reference (7.53%). Wall thickness measurements also showed good agreement between vascular surgeons with average coefficients of variation of 10.59% (ruptured aneurysms) and 13.02% (unruptured aneurysms). Ruptured aneurysms exhibit significantly thicker walls (1.78 +/- 0.39 mm) than unruptured ones (1.48 +/- 0.22 mm), p = 0.044. CONCLUSIONS: While further refinement is needed to fully automate the outer wall segmentation algorithm, these preliminary results demonstrate the method's adequate reproducibility and low interobserver variability.