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Purpose: The retina contains the highest concentration of the omega 3 fatty acid, docosahexaenoic acid (DHA), in the body. Although epidemiologic studies showed an inverse correlation between the consumption of omega 3 fatty acids and the prevalence of diabetic retinopathy, there are no data showing the effect of diabetes on retinal DHA in humans. In this study, we measured the DHA content of the retina in diabetic and non-diabetic humans as well as mice and determined the effect of diabetes on retinal thickness and function in mice., Methods: Fatty acid composition was determined by gas chromatography/mass spectroscopy. Retinal thickness in mice was measured by optical coherence tomography and retinal function was measured by electroretinogram (ERG). Expression of selected genes involved in inflammation and lipid metabolism was determined by quantitative real-time PCR (qRT-PCR)., Results: We found a 40% reduction of DHA in peripheral retina and a 25% reduction in the macula of diabetic humans compared with nondiabetic controls. There was a 24% reduction in retinal DHA of type 2 diabetic mice (db/db) compared with the controls (db/+). The retinal thickness was significantly decreased in db/db mice, especially in the inner retina, and the ERG b-wave amplitudes were significantly attenuated. Increased expression of proinflammatory genes was observed in both human and mouse diabetic retinas., Conclusions: Retinal DHA is reduced in diabetic humans and mice, which is associated with a thinning of retina and functional defects in diabetic mice. Enriching retinal DHA through diet may be beneficial in the prevention and treatment of diabetic retinopathy.

Corneal scarring, a significant cause of global blindness, results from various insults, including trauma, infections, and genetic disorders. The conventional treatment to replace scarred corneal tissues includes partial or full-thickness corneal transplantation using healthy donor corneas. However, only 1 in 70 individuals with treatable corneal scarring can undergo surgery, due to the limited supply of transplantable donor tissue. Our research focuses on cell-based strategies, specifically ex vivo-expanded corneal stromal stem cells (CSSCs), to address corneal scarring. Preclinical studies have demonstrated the efficacy of CSSC treatment in reducing corneal inflammation and fibrosis, inhibiting scar formation, and regenerating native stromal tissue. Mechanisms include CSSC differentiation into stromal keratocytes and the expression of regenerative cytokines. Here, we present a good manufacturing practice (GMP)-compliant protocol to isolate and expand human CSSCs. This method paves the way to produce clinical-grade CSSCs for transplantation and clinical trials. Key features • This protocol utilizes surgical skills to dissect human corneal tissues for CSSC isolation. • The yield and features of CSSCs rely on donor tissue quality (freshness) and have donor-to-donor variability. • Up to 0.5 billion CSSCs can be generated from a single cornea specimen, and cells at passage 3 are suitable for treatment uses., Competing Interests: Competing interestsThe authors declare that they have no competing interests., (©Copyright : © 2024 The Authors; This is an open access article under the CC BY license.)

Purpose: To determine endothelial cell density (ECD) from real-world donor cornea endothelial cell (EC) images using a self-supervised deep learning segmentation model., Methods: Two eye banks (Eversight, VisionGift) provided 15,138 single, unique EC images from 8169 donors along with their demographics, tissue characteristics, and ECD. This dataset was utilized for self-supervised training and deep learning inference. The Cornea Image Analysis Reading Center (CIARC) provided a second dataset of 174 donor EC images based on image and tissue quality. These images were used to train a supervised deep learning cell border segmentation model. Evaluation between manual and automated determination of ECD was restricted to the 1939 test EC images with at least 100 cells counted by both methods., Results: The ECD measurements from both methods were in excellent agreement with rc of 0.77 (95% confidence interval [CI], 0.75-0.79; P < 0.001) and bias of 123 cells/mm2 (95% CI, 114-131; P < 0.001); 81% of the automated ECD values were within 10% of the manual ECD values. When the analysis was further restricted to the cropped image, the rc was 0.88 (95% CI, 0.87-0.89; P < 0.001), bias was 46 cells/mm2 (95% CI, 39-53; P < 0.001), and 93% of the automated ECD values were within 10% of the manual ECD values., Conclusions: Deep learning analysis provides accurate ECDs of donor images, potentially reducing analysis time and training requirements., Translational Relevance: The approach of this study, a robust methodology for automatically evaluating donor cornea EC images, could expand the quantitative determination of endothelial health beyond ECD.