Age-Related Macular Degeneration: Challenges and Opportunities
A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Ophthalmology".
Deadline for manuscript submissions: 20 February 2026 | Viewed by 14
Special Issue Editor
Special Issue Information
Dear Colleagues,
Age-related macular degeneration (AMD) is a complex, multifactorial disease characterized by progressive photoreceptor and retinal pigment epithelium degeneration, predominantly affecting individuals over 60 years of age. Genetic and lifestyle factors interact to contribute to its etiology. While recent advancements have yielded biologic therapies inhibiting vascular endothelial growth factor for neovascular AMD and complement activation for geographic atrophy, these treatments are limited by cost, injection-related risks, and suboptimal efficacy. Consequently, there is a need for novel, targeted therapies with improved endpoints that demonstrate both structural and functional benefits. A significant barrier to AMD drug discovery and research is the absence of preclinical models accurately recapitulating the phenotypes and progression of dry AMD.
This Special Issue, entitled “Age-Related Macular Degeneration: Challenges and Opportunities”, aims to collate submissions that advance our current understanding of existing gaps in the literature and identify new directions in clinical AMD research. Papers related to the topics mentioned in the Keywords are welcome to be submitted.
Topics of interest include the following:
- Epidemiology and genetics of AMD;
- Biomarkers for AMD detection and stratification;
- Structural and functional endpoints;
- Use of artificial intelligence in AMD research;
- Role of complement system in AMD pathogenesis;
- Role of oxidative stress in AMD;
- Cell culture and preclinical animal models of AMD;
- Novel drug delivery mechanisms for AMD.
Dr. Deepayan Kar
Guest Editor
Manuscript Submission Information
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Keywords
- age-related macular degeneration
- imaging biomarkers
- visual function testing
- optical coherence tomography
- anti-VEGF
- complement
- artificial intelligence
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