ReviewOphthalmology and therapy2026
Preclinical Models of Rare Corneal and Ocular Surface Diseases: a Comprehensive Narrative Review.
Review in Ophthalmology and therapy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
8 authors.
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Abstract
Rare eye diseases of the cornea and ocular surface (REDs) remain a clinical challenge owing to their low prevalence, heterogeneous presentation, and limited therapeutic options. As REDs mechanisms are often complex, preclinical models are essential to advance mechanistic understanding and support the development of targeted treatments. This review provides a comprehensive overview of the experimental platforms currently available to study REDs, including in vitro models such as primary and engineered cell systems, coculture approaches, and emerging 3D organoid technologies. In addition, we summarize in vivo strategies ranging from surgically induced models to genetic and transgenic systems that reproduce the relevant ocular phenotypes. By comparing the strengths, limitations, and translational value of these complementary approaches, this review offers an integrated perspective on how preclinical modeling can be optimized to investigate REDs pathophysiology. Furthermore, we highlight how emerging tools-such as organ-on-chip platforms and artificial intelligence-assisted analytics-may enhance model fidelity and accelerate the identification of effective therapeutic strategies.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.