An Eye on Extracellular Vesicles: Trends and Clinical Translations in Vision Research

To perform a review of research, funding, and clinical translation efforts for extracellular vesicles (EVs) within vision science.

Retrospective analysis of publication, funding, and clinical trials data.

A pretrained large language model (Jina2) was used to create semantic embeddings for 41 282 abstracts from articles related to EVs archived on EMBASE and published between January 1966 and January 2024. The articles were projected and clustered according to semantic embedding similarity, and research subdomains for EVs were determined through inspection of term frequency-inverse document frequency weighted word clouds. Mann-Kendall trend analysis was performed to identify current areas of growth within EV research. Additionally, National Institutes of Health funding data from RePORT Expenditures and Results and clinical trials data from ClinicalTrials.gov were analyzed to correlate publication trends with funding support and clinical translation efforts.

Unsupervised clustering and Mann-Kendall trend analysis identified wound healing/regeneration (P = 0.030) and neurodegenerative disease (P = 0.049) as significantly accelerating in growth of publication over time. Ophthalmology-restricted subset analysis identified that publications in age-related macular degeneration (P = 0.191) and clinical applications (P = 0.086) are no longer growing at a significant rate. Analysis of funding data identified that the National Cancer Institute was the top funding institution overall, but that the National Institute on Aging is rapidly advancing in terms of funding EV research and trials. Analysis of ClinicalTrials.gov data highlights a dearth of clinical trials within ophthalmology despite a growing number of studies in other medical subfields.

Extracellular vesicles remain a promising substrate for both the identification and treatment of vision-threatening diseases. A better understanding of the current landscape of research and funding trends should help to inform future funding and translational efforts.

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.