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

IntroductionThe 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.MethodsWe 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).ResultsAfter 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.ConclusionThis 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

Controlled and Synchronised Vascular Regeneration upon the Implantation of Iloprost- and Cationic Amphiphilic Drugs-Conjugated Tissue-Engineered Vascular Grafts into the Ovine Carotid Artery: A Proteomics-Empowered Study

Implementation of small-diameter tissue-engineered vascular grafts (TEVGs) into clinical practice is still delayed due to the frequent complications, including thrombosis, aneurysms, neointimal hyperplasia, calcification, atherosclerosis, and infection. Here, we conjugated a vasodilator/platelet inhibitor, iloprost, and an antimicrobial cationic amphiphilic drug, 1,5-bis-(4-tetradecyl-1,4-diazoniabicyclo [2.2.2]octan-1-yl) pentane tetrabromide, to the luminal surface of electrospun poly(ε-caprolactone) (PCL) TEVGs for preventing thrombosis and infection, additionally enveloped such TEVGs into the PCL sheath to preclude aneurysms, and implanted PCLIlo/CAD TEVGs into the ovine carotid artery (n = 12) for 6 months. The primary patency was 50% (6/12 animals). TEVGs were completely replaced with the vascular tissue, free from aneurysms, calcification, atherosclerosis and infection, completely endothelialised, and had clearly distinguishable medial and adventitial layers. Comparative proteomic profiling of TEVGs and contralateral carotid arteries found that TEVGs lacked contractile vascular smooth muscle cell markers, basement membrane components, and proteins mediating antioxidant defense, concurrently showing the protein signatures of upregulated protein synthesis, folding and assembly, enhanced energy metabolism, and macrophage-driven inflammation. Collectively, these results suggested a synchronised replacement of PCL with a newly formed vascular tissue but insufficient compliance of PCLIlo/CAD TEVGs, demanding their testing in the muscular artery position or stimulation of vascular smooth muscle cell specification after the implantation

Relationship between Epicardial and Coronary Adipose Tissue and the Expression of Adiponectin, Leptin, and Interleukin 6 in Patients with Coronary Artery Disease

Adipose tissue (AT) is an endocrine and paracrine organ that synthesizes biologically active adipocytokines, which affect inflammation, fibrosis, and atherogenesis. Epicardial and perivascular fat depots are of great interest to researchers, owing to their potential effects on the myocardium and blood vessels. The aim of the study was to assess the expression and secretion of adipocytokine genes in the AT of patients with coronary artery disease (CAD) and patients with aortic or mitral valve replacement. This study included 84 patients with CAD and 50 patients with aortic or mitral valve replacement. Adipocytes were isolated from subcutaneous, epicardial (EAT), and perivascular AT (PVAT), and were cultured for 24 h. EAT exhibited the lowest level of adiponectin gene expression and secretion, regardless of nosology, and high expression levels of the leptin gene and interleukin-6 (IL-6). However, EAT adipocytes in patients with CAD were characterized by more pronounced changes in comparison with the group with heart defects. High leptin and IL-6 levels resulted in increased pro-inflammatory activity, as observed in both EAT and PVAT adipocytes, especially in individuals with CAD. Therefore, our results revealed the pathogenetic significance of alterations in the adipokine and cytokine status of adipocytes of EAT and PVAT in patients with CAD