Mesenchymal Stem Cell-Derived Exosomes in Ophthalmology: A Comprehensive Review
Bibliographic record
Abstract
Over the past decade, the field of mesenchymal stem cell (MSC) therapy has exhibited rapid growth. Due to their regenerative, reparatory, and immunomodulatory capacities, MSCs have been widely investigated as therapeutic agents in the cell-based treatment of chronic ophthalmic pathologies. However, the applicability of MSC-based therapy is limited by suboptimal biocompatibility, penetration, and delivery to the target ocular tissues. An emerging body of research has elucidated the role of exosomes in the biological functions of MSCs, and that MSC-derived extracellular vesicles (EVs) possess anti-inflammatory, anti-apoptotic, tissue repairing, neuroprotective, and immunomodulatory properties similar to MSCs. The recent advances in MSCs-derived exosomes can serve as solutions to the challenges faced by MSCs-therapy. Due to their nano-dimensions, MSC-derived exosomes can rapidly penetrate biological barriers and reach immune-privileged organs, allowing for efficient delivery of therapeutic factors such as trophic and immunomodulatory agents to ocular tissues that are typically challenging to target by conventional therapy and MSCs transplantation. In addition, the use of EVs minimizes the risks associated with mesenchymal stem cell transplantation. In this literature review, we focus on the studies published between 2017 and 2022, highlighting the characteristics of EVs derived from MSCs and their biological functions in treating anterior and posterior segment ocular diseases. Additionally, we discuss the potential use of EVs in clinical settings. Rapid advancements in regenerative medicine and exosome-based drug delivery, in conjunction with an increased understanding of ocular pathology and pharmacology, hold great promise for the treatment of ocular diseases. The potential of exosome-based therapies is exciting and can revolutionize the way we approach these ocular conditions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".