Transplantation of Mesenchymal Stem Cells Promotes Tissue Regeneration in a Glaucoma Model Through Laser-Induced Paracrine Factor Secretion and Progenitor Cell Recruitment
Bibliographic record
Abstract
Among bone marrow cells, hematopoietic and mesenchymal components can contribute to repair damaged organs. Such cells are usually used in acute diseases but few options are available for the treatment of chronic disorders. In this study, we have used a laser-induced model of open angle glaucoma (OAG) to evaluate the potential of bone marrow cell populations and the mechanisms involved in tissue repair. In addition, we investigated laser-induced tissue remodeling as a method of targeting effector cells into damaged tissues. We demonstrate that among bone marrow cells, mesenchymal stem cells (MSC) induce trabecular meshwork regeneration. MSC injection into the ocular anterior chamber leads to far more efficient decrease in intraocular pressure (IOP) (p < .001) and healing than hematopoietic cells. This robust effect was attributable to paracrine factors from stressed MSC, as injection of conditioned medium from MSC exposed to low but not to normal oxygen levels resulted in an immediate decrease in IOP. Moreover, MSC and their secreted factors induced reactivation of a progenitor cell pool found in the ciliary body and increased cellular proliferation. Proliferating cells were observed within the chamber angle for at least 1 month. Laser-induced remodeling was able to target MSC to damaged areas with ensuing specific increases in ocular progenitor cells. Thus, our results identify MSC and their secretum as crucial mediators of tissue repair in OAG through reactivation of local neural progenitors. In addition, laser treatment could represent an appealing strategy to promote MSC-mediated progenitor cell recruitment and tissue repair in chronic diseases.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".