Digital Image Analysis of Angiogenesis in Diabetic Wounds Treated with Muntingia calabura L. Extract Cream

Abstract

Diabetes mellitus is a chronic metabolic disease that causes delayed wound healing due to persistent hyperglycemia, oxidative stress, prolonged inflammation, and angiogenesis disorders. This condition encourages the development of topical therapy based on natural ingredients combined with digital technology to increase the effectiveness of therapy as well as the objectivity of histopathological evaluation. This study aims to analyze the effect of kersen leaf ethanol extract cream (Muntingia calabura L.) on angiogenesis in wound healing of rats (Rattus norvegicus) in the diabetes mellitus model using a digital image analysis approach. The novelty of this study lies in the integration of kersen leaf ethanol extract cream formulation with digital image-based histopathological analysis to quantitatively, objectively, and reproductively identify and count the number of new blood vessels. The study used a Complete Random Design (RAL) consisting of five treatment groups with three replicates. Skin tissue samples were collected on days 3, 7, and 14, then processed using Hematoxilin-Eosin (HE) staining. The preparations were observed using a digital microscope and analyzed through image processing software to measure the number of new blood vessels as an indicator of angiogenesis. The results showed that the administration of kersen leaf ethanol extract cream significantly increased angiogenesis compared to the control group, especially on days 7 and 14, which was characterized by an increase in the density of new blood vessels and an acceleration of the tissue proliferation phase. The digital analysis approach results in more accurate, consistent measurements, and minimizes the subjectivity of microscopic observations. These findings suggest that kersen leaf ethanol extract cream has the potential to be developed as a natural topical therapy to accelerate the healing of diabetic wounds, while also introducing a digital approach in histopathological evaluation that supports the development of digital pathology in biomedical research.