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Biomedicines
Special Issues
Advanced Research on Diabetic Retinopathy
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Advanced Research on Diabetic Retinopathy

A special issue of Biomedicines (ISSN 2227-9059). This special issue belongs to the section "Molecular and Translational Medicine".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 106

Special Issue Editor

Dr. Stamatios Lampsas

E-Mail Website
Guest Editor
2nd Department of Ophthalmology, Attikon University Hospital, Medical School, National and Kapodis-Trian University of Athens, 11528 Athens, Greece
Interests: diabetic retinopathy; endothelial properties; risk factor; retinal diseases

Special Issue Information

Dear Colleagues,

This Special Issue aims to highlight cutting-edge research into the mechanisms, diagnostics, and therapeutics of diabetic retinopathy. We are calling for high-quality submissions that push the boundaries of diabetic retinopathy research. Diabetic retinopathy is a rapidly evolving and highly active area of research, driven by the global rise in diabetes prevalence and its status as a leading cause of vision loss. With emerging technologies and novel therapeutic targets, the race to prevent and reverse vision loss in diabetes has never been more urgent or promising. This Special Issue will bring together the latest advancements in pathophysiology, biomarkers, imaging, and treatment approaches for diabetic retinopathy.

Dr. Stamatios Lampsas
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Biomedicines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • diebetic retinopathy
  • retinal diseases
  • novel treatment strategies
  • progression
  • pathogenetic mechanisms
  • non-proliferative diabetic retinopathy
  • proliferative diabetic retinopathy

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Published Papers (1 paper)

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17 pages, 5404 KiB  
Article
AI-Enhanced Fluorescein Angiography Detection of Diabetes-Induced Silent Retinal Capillary Dropout and RNA-Seq Identification of Pre-Symptomatic Biomarkers
by Yiyan Peng, Huishi Toh, Dennis Clegg and Peng Jiang
Biomedicines 2025, 13(8), 1926; https://doi.org/10.3390/biomedicines13081926 - 7 Aug 2025
Abstract
Objective: Retinal capillary dropout, characterized by acellular capillaries or “ghost vessels,” is an early pathological sign of diabetic retinopathy (DR) that remains undetectable through standard clinical imaging techniques until visible morphological changes, such as microaneurysms or hemorrhages, occur. This study aims to [...] Read more.
Objective: Retinal capillary dropout, characterized by acellular capillaries or “ghost vessels,” is an early pathological sign of diabetic retinopathy (DR) that remains undetectable through standard clinical imaging techniques until visible morphological changes, such as microaneurysms or hemorrhages, occur. This study aims to develop a non-destructive artificial intelligence (AI)-based method using fluorescein angiography (FA) images to detect early-stage, silent retinal capillary dropout. Methods: We utilized 94 FA images and corresponding destructive retinal capillary density measurements obtained through retinal trypsin digestion from 51 Nile rats. Early capillary dropout was defined as having an acellular capillary density of ≥18 counts per mm2. A DenseNet based deep learning model was trained to classify images into early capillary dropout or normal. A Bayesian framework incorporating diabetes duration was used to enhance model predictions. RNA sequencing was conducted on retinal vasculature to identify molecular markers associated with capillary early dropout. Results: The AI-based FA imaging model demonstrated an accuracy of 80.85%, sensitivity of 84.21%, specificity of 75.68%, and an AUC of 0.86. Integration of diabetes duration into a Bayesian predictive framework further improved the model’s performance (AUC = 0.90). Transcriptomic analysis identified 43 genes significantly upregulated in retinal tissues preceding capillary dropout. Notably, inflammatory markers such as Bcl2a1, Birc5, and Il20rb were among these genes, indicating that inflammation might play a critical role in early DR pathogenesis. Conclusions: This study demonstrates that AI-enhanced FA imaging can predict silent retinal capillary dropout before conventional clinical signs of DR emerge. Combining AI predictions with diabetes duration data significantly improves diagnostic performance. The identified gene markers further highlight inflammation as a potential driver in early DR, offering novel insights and potential therapeutic targets for preventing DR progression. Full article
(This article belongs to the Special Issue Advanced Research on Diabetic Retinopathy)
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