ML-driven segmentation of microvascular features during histological examination of tissue-engineered vascular grafts.

Introduction: The development of next-generation tissue-engineered medical devices such as tissue-engineered vascular grafts (TEVGs) is a leading trend in translational medicine. Microscopic examination is an indispensable part of animal experimentation, and histopathological analysis of regenerated tissue is crucial for assessing the outcomes of implanted medical devices. However, the objective quantification of regenerated tissues can be challenging due to their unusual and complex architecture. To address these challenges, research and development of advanced ML-driven tools for performing adequate histological analysis appears to be an extremely promising direction. Methods: We compiled a dataset of 104 representative whole slide images (WSIs) of TEVGs which were collected after a 6-month implantation into the sheep carotid artery. The histological examination aimed to analyze the patterns of vascular tissue regeneration in TEVGs in situ. Having performed an automated slicing of these WSIs by the Entropy Masker algorithm, we filtered and then manually annotated 1,401 patches to identify 9 histological features: arteriole lumen, arteriole media, arteriole adventitia, venule lumen, venule wall, capillary lumen, capillary wall, immune cells, and nerve trunks. To segment and quantify these features, we rigorously tuned and evaluated the performance of six deep learning models (U-Net, LinkNet, FPN, PSPNet, DeepLabV3, and MA-Net). Results: After rigorous hyperparameter optimization, all six deep learning models achieved mean Dice Similarity Coefficients (DSC) exceeding 0.823. Notably, FPN and PSPNet exhibited the fastest convergence rates. MA-Net stood out with the highest mean DSC of 0.875, demonstrating superior performance in arteriole segmentation. DeepLabV3 performed well in segmenting venous and capillary structures, while FPN exhibited proficiency in identifying immune cells and nerve trunks. An ensemble of these three models attained an average DSC of 0.889, surpassing their individual performances. Conclusion: This study showcases the potential of ML-driven segmentation in the analysis of histological images of tissue-engineered vascular grafts. Through the creation of a unique dataset and the optimization of deep neural network hyperparameters, we developed and validated an ensemble model, establishing an effective tool for detecting key histological features essential for understanding vascular tissue regeneration. These advances herald a significant improvement in ML-assisted workflows for tissue engineering research and development.

Proteolytic Degradation Is a Major Contributor to Bioprosthetic Heart Valve Failure.

Background Whereas the risk factors for structural valve degeneration (SVD) of glutaraldehyde-treated bioprosthetic heart valves (BHVs) are well studied, those responsible for the failure of BHVs fixed with alternative next-generation chemicals remain largely unknown. This study aimed to investigate the reasons behind the development of SVD in ethylene glycol diglycidyl ether-treated BHVs. Methods and Results Ten ethylene glycol diglycidyl ether-treated BHVs excised because of SVD, and 5 calcified aortic valves (AVs) replaced with BHVs because of calcific AV disease were collected and their proteomic profile was deciphered. Then, BHVs and AVs were interrogated for immune cell infiltration, microbial contamination, distribution of matrix-degrading enzymes and their tissue inhibitors, lipid deposition, and calcification. In contrast with dysfunctional AVs, failing BHVs suffered from complement-driven neutrophil invasion, excessive proteolysis, unwanted coagulation, and lipid deposition. Neutrophil infiltration was triggered by an asymptomatic bacterial colonization of the prosthetic tissue. Neutrophil elastase, myeloblastin/proteinase 3, cathepsin G, and matrix metalloproteinases (MMPs; neutrophil-derived MMP-8 and plasma-derived MMP-9), were significantly overexpressed, while tissue inhibitors of metalloproteinases 1/2 were downregulated in the BHVs as compared with AVs, together indicative of unbalanced proteolysis in the failing BHVs. As opposed to other proteases, MMP-9 was mostly expressed in the disorganized prosthetic extracellular matrix, suggesting plasma-derived proteases as the primary culprit of SVD in ethylene glycol diglycidyl ether-treated BHVs. Hence, hemodynamic stress and progressive accumulation of proteases led to the extracellular matrix degeneration and dystrophic calcification, ultimately resulting in SVD. Conclusions Neutrophil- and plasma-derived proteases are responsible for the loss of BHV mechanical competence and need to be thwarted to prevent SVD.

Calciprotein Particles Link Disturbed Mineral Homeostasis with Cardiovascular Disease by Causing Endothelial Dysfunction and Vascular Inflammation.

An association between high serum calcium/phosphate and cardiovascular events or death is well-established. However, a mechanistic explanation of this correlation is lacking. Here, we examined the role of calciprotein particles (CPPs), nanoscale bodies forming in the human blood upon its supersaturation with calcium and phosphate, in cardiovascular disease. The serum of patients with coronary artery disease or cerebrovascular disease displayed an increased propensity to form CPPs in combination with elevated ionised calcium as well as reduced albumin levels, altogether indicative of reduced Ca2+-binding capacity. Intravenous administration of CPPs to normolipidemic and normotensive Wistar rats provoked intimal hyperplasia and adventitial/perivascular inflammation in both balloon-injured and intact aortas in the absence of other cardiovascular risk factors. Upon the addition to primary human arterial endothelial cells, CPPs induced lysosome-dependent cell death, promoted the release of pro-inflammatory cytokines, stimulated leukocyte adhesion, and triggered endothelial-to-mesenchymal transition. We concluded that CPPs, which are formed in the blood as a result of altered mineral homeostasis, cause endothelial dysfunction and vascular inflammation, thereby contributing to the development of cardiovascular disease.

EMbedding and Backscattered Scanning Electron Microscopy: A Detailed Protocol for the Whole-Specimen, High-Resolution Analysis of Cardiovascular Tissues.

Currently, an ultrastructural analysis of cardiovascular tissues is significantly complicated. Routine histopathological examinations and immunohistochemical staining suffer from a relatively low resolution of light microscopy, whereas the fluorescence imaging of plaques and bioprosthetic heart valves yields considerable background noise from the convoluted extracellular matrix that often results in a low signal-to-noise ratio. Besides, the sectioning of calcified or stent-expanded blood vessels or mineralised heart valves leads to a critical loss of their integrity, demanding other methods to be developed. Here, we designed a conceptually novel approach that combines conventional formalin fixation, sequential incubation in heavy metal solutions (osmium tetroxide, uranyl acetate or lanthanides, and lead citrate), and the embedding of the whole specimen into epoxy resin to retain its integrity while accessing the region of interest by grinding and polishing. Upon carbon sputtering, the sample is visualised by means of backscattered scanning electron microscopy. The technique fully preserves calcified and stent-expanded tissues, permits a detailed analysis of vascular and valvular composition and architecture, enables discrimination between multiple cell types (including endothelial cells, vascular smooth muscle cells, fibroblasts, adipocytes, mast cells, foam cells, foreign-body giant cells, canonical macrophages, neutrophils, and lymphocytes) and microvascular identities (arterioles, venules, and capillaries), and gives a technical possibility for quantitating the number, area, and density of the blood vessels. Hence, we suggest that our approach is capable of providing a pathophysiological insight into cardiovascular disease development. The protocol does not require specific expertise and can be employed in virtually any laboratory that has a scanning electron microscope.