The use of biologically derived vessels as small-diameter vascular grafts in vascular diseases is currently intensely studied. Vessel decellularization provides a biocompatible scaffold with very low immunogenicity that avoids immunosuppression after transplantation. Good scaffold preservation is important as it facilitates successful cell repopulation. In addition, mechanical characteristics have to be carefully evaluated when the graft is intended to be used as an artery due to the high pressures the vessel is subjected to. Here, we present a new and fast decellularization protocol for porcine carotid arteries, followed by investigation of the quality of obtained vessel scaffolds in terms of maintenance of important extracellular matrix components, mechanical resistance, and compatibility with human endothelial cells. Our results evidence that our decellularization protocol minimally alters both the presence of scaffold proteins and their mechanical behavior and human endothelial cells could adhere to the scaffold in vitro. We conclude that if a suitable protocol is used, a high-quality decellularized arterial scaffold of non-human origin can be promptly obtained, having a great potential to be recellularized and used as an arterial graft in transplantation medicine.

• Klíčová slova
• ECM proteins, endothelial cell adhesion, mechanical properties, optimized decellularization, porcine carotid artery, scaffold quality,
• Publikační typ
• časopisecké články MeSH

Decellularized tissue is an important source for biological tissue engineering. Evaluation of the quality of decellularized tissue is performed using scanned images of hematoxylin-eosin stained (H&E) tissue sections and is usually dependent on the observer. The first step in creating a tool for the assessment of the quality of the liver scaffold without observer bias is the automatic segmentation of the whole slide image into three classes: the background, intralobular area, and extralobular area. Such segmentation enables to perform the texture analysis in the intralobular area of the liver scaffold, which is crucial part in the recellularization procedure. Existing semi-automatic methods for general segmentation (i.e., thresholding, watershed, etc.) do not meet the quality requirements. Moreover, there are no methods available to solve this task automatically. Given the low amount of training data, we proposed a two-stage method. The first stage is based on classification of simple hand-crafted descriptors of the pixels and their neighborhoods. This method is trained on partially annotated data. Its outputs are used for training of the second-stage approach, which is based on a convolutional neural network (CNN). Our architecture inspired by U-Net reaches very promising results, despite a very low amount of the training data. We provide qualitative and quantitative data for both stages. With the best training setup, we reach 90.70% recognition accuracy.

• Klíčová slova
• H&E, convolutional neural networks, decellularization, liver, semantic segmentation, tissue engineering,
• MeSH
• játra * diagnostické zobrazování   MeSH
• neuronové sítě MeSH
• počítačové zpracování obrazu * MeSH
• sémantika * MeSH
• Publikační typ
• dopisy MeSH