Efficiency of Machine Learning Algorithms for the Determination of Macrovesicular Steatosis in Frozen Sections Stained with Sudan to Evaluate the Quality of the Graft in Liver Transplantation.

Liver transplantation is the only curative treatment option in patients diagnosed with end-stage liver disease. The low availability of organs demands an accurate selection procedure based on histological analysis, in order to evaluate the allograft. This assessment, traditionally carried out by a pathologist, is not exempt from subjectivity. In this sense, new tools based on machine learning and artificial vision are continuously being developed for the analysis of medical images of different typologies. Accordingly, in this work, we develop a computer vision-based application for the fast and automatic objective quantification of macrovesicular steatosis in histopathological liver section slides stained with Sudan stain. For this purpose, digital microscopy images were used to obtain thousands of feature vectors based on the RGB and CIE L*a*b* pixel values. These vectors, under a supervised process, were labelled as fat vacuole or non-fat vacuole, and a set of classifiers based on different algorithms were trained, accordingly. The results obtained showed an overall high accuracy for all classifiers (>0.99) with a sensitivity between 0.844 and 1, together with a specificity >0.99. In relation to their speed when classifying images, KNN and Naïve Bayes were substantially faster than other classification algorithms. Sudan stain is a convenient technique for evaluating ME in pre-transplant liver biopsies, providing reliable contrast and facilitating fast and accurate quantification through the machine learning algorithms tested.

Silk fibroin scaffolds seeded with Wharton's jelly mesenchymal stem cells enhance re-epithelialization and reduce formation of scar tissue after cutaneous wound healing.

BACKGROUND: The treatment of extensive and/or chronic skin wounds is a widespread and costly public health problem. Mesenchymal stem cells (MSCs) have been proposed as a potential cell therapy for inducing wound healing in different clinical settings, alone or in combination with biosynthetic scaffolds. Among them, silk fibroin (SF) seeded with MSCs has been shown to have increased efficacy in skin wound healing experimental models. METHODS: In this report, we investigated the wound healing effects of electrospun SF scaffolds cellularized with human Wharton's jelly MSCs (Wj-MSCs-SF) using a murine excisional wound splinting model. RESULTS: Immunohistopathological examination after transplant confirmed the presence of infiltrated human fibroblast-like CD90-positive cells in the dermis of the Wj-MSCs-SF-treated group, yielding neoangiogenesis, decreased inflammatory infiltrate and myofibroblast proliferation, less collagen matrix production, and complete epidermal regeneration. CONCLUSIONS: These findings indicate that Wj-MSCs transplanted in the wound bed on a silk fibroin scaffold contribute to the generation of a well-organized and vascularized granulation tissue, enhance reepithelization of the wound, and reduce the formation of fibrotic scar tissue, highlighting the potential therapeutic effects of Wj-MSC-based tissue engineering approaches to non-healing wound treatment.