Objective-We have previously demonstrated the ability to construct 3-dimensional microvascular beds in vitro via angiogenesis from isolated, intact, microvessel fragments that retain endothelial cells and perivascular cells. Our objective was to develop and characterize an experimental model of tissue vascularization, based on the implantation of this microvascular construct, which recapitulated angiogenesis, vessel differentiation, and network maturation. Methods and Results-On implantation in a severe combined-immunodeficient mouse model, vessels in the microvascular constructs rapidly inosculated with the recipient host circulation. Ink perfusion of implants via the left ventricle of the host demonstrated that vessel inosculation begins within the first day after implantation. Evaluation of explanted constructs over the course of 28 days revealed the presence of a mature functional microvascular bed. Using a probe specific for the original microvessel source, 91.7%Ϯ11% and 88.6%Ϯ19% of the vessels by day 5 and day 28 after implantation, respectively, were derived from the original microvessel isolate. Similar results were obtained when human-derived microvessels were used to build the microvascular construct. Key Words: vascularization Ⅲ microcirculation Ⅲ angiogenesis Ⅲ human Ⅲ vascular remodeling V ascularization is the process by which perfusion pathway length and vessel segment number are increased and organized into a functional vascular bed. In normal situations, this effective increase in vessel density delivers more blood to the tissue, facilitating tissue growth and/or increased tissue activity. 1,2 Consequently, vascularization is a primary component of tissue growth and repair, such as occurs during development, 3 after an upstream occlusive event leading to tissue ischemia, 4 or during proliferative events, as seen in tissue healing 5,6 and tumor growth. 7 Although we know of many factors and signals that initiate or terminate the vascularization process, little is known about the rules that govern vascularization as an integrated process that includes angiogenesis, 3 arteriogenesis, 8 vascular remodeling, 9 vessel adaptation, 10 and arterio-venous polarization. 11 We have previously shown that isolated intact microvessel fragments retain angiogenic potential and are capable of forming a simple microvascular bed when cultured in a 3-dimensional collagen I gel. 12 In this microvascular construct, the vessel fragments undergo stereotypical angiogenesis, forming neovessels that maintain patent lumen and perivascular cell associations. Furthermore, the vessel fragments within this culture system are responsive to proangiogenic conditions. 12,13 All of this occurs in the absence of blood flow and relatively few nonvascular cells. Conclusions-WithHere we report the development and characterization of an experimental model of tissue vascularization based on the implantation of this microvascular construct. Precultured or freshly formed microvascular constructs implanted subcutaneously inosculate with the...

The goals of this study were to determine the time course and spatial dependence of structural diameter changes in the mouse gracilis artery after a redistribution of blood flow and to compare the observations with predictions of computational models for structural adaptation. Diameters were measured 1, 2, 7, 14, 21, 28, and 56 days after resection of one of the two blood supplies to the artery. Overall average diameter, normalized with respect to diameters in untreated vessels, increased slightly during the first 7 days, then increased more rapidly, reaching a peak around day 21, and then decreased. This transient increase in diameter was spatially nonuniform, being largest toward the point of resection. A previously developed theoretical model, in which diameter varies in response to stimuli derived from local metabolic and hemodynamic conditions, was extended to include effects of time-delayed remodeling stimuli in regions of reduced perfusion. Predictions of this model were consistent with observed diameter changes, including the transient increase in diameters near the point of resection, when a remodeling stimulus with a time delay of approximately 7 days was included. The results suggest that delayed stimuli significantly influence the dynamic characteristics of vascular remodeling resulting from reduced blood supply.

IntroductionConfocal laser endomicroscopy (CLE) is becoming a popular method for optical biopsy of digestive mucosa for both diagnostic and therapeutic procedures. Computer aided diagnosis of CLE images, using image processing and fractal analysis can be used to quantify the histological structures in the CLE generated images. The aim of this study is to develop an automatic diagnosis algorithm of colorectal cancer (CRC), based on fractal analysis and neural network modeling of the CLE-generated colon mucosa images.Materials and MethodsWe retrospectively analyzed a series of 1035 artifact-free endomicroscopy images, obtained during CLE examinations from normal mucosa (356 images) and tumor regions (679 images). The images were processed using a computer aided diagnosis (CAD) medical imaging system in order to obtain an automatic diagnosis. The CAD application includes image reading and processing functions, a module for fractal analysis, grey-level co-occurrence matrix (GLCM) computation module, and a feature identification module based on the Marching Squares and linear interpolation methods. A two-layer neural network was trained to automatically interpret the imaging data and diagnose the pathological samples based on the fractal dimension and the characteristic features of the biological tissues.ResultsNormal colon mucosa is characterized by regular polyhedral crypt structures whereas malignant colon mucosa is characterized by irregular and interrupted crypts, which can be diagnosed by CAD. For this purpose, seven geometric parameters were defined for each image: fractal dimension, lacunarity, contrast correlation, energy, homogeneity, and feature number. Of the seven parameters only contrast, homogeneity and feature number were significantly different between normal and cancer samples. Next, a two-layer feed forward neural network was used to train and automatically diagnose the malignant samples, based on the seven parameters tested. The neural network operations were cross-entropy with the results: training: 0.53, validation: 1.17, testing: 1.17, and percent error, resulting: training: 16.14, validation: 17.42, testing: 15.48. The diagnosis accuracy error was 15.5%.ConclusionsComputed aided diagnosis via fractal analysis of glandular structures can complement the traditional histological and minimally invasive imaging methods. A larger dataset from colorectal and other pathologies should be used to further validate the diagnostic power of the method.