Mesenchymal stem cells <scp>derived‐exosomes</scp> enhanced amniotic membrane extract promotes corneal keratocyte proliferation
Bibliographic record
Abstract
Abstract Amniotic membrane extract (AME) and Wharton's jelly mesenchymal stem cells derived‐exosomes (WJ‐MSC‐Exos) are promising therapeutic solutions explored for their potential in tissue engineering and regenerative medicine, particularly in skin and corneal wound healing applications. AME is an extract form of human amniotic membrane and known to contain a plethora of cytokines and growth factors, making it a highly attractive option for topical applications. Similarly, WJ‐MSC‐Exos have garnered significant interest for their wound healing properties. Although WJ‐MSC‐Exos and AME have been used separately for wound healing research, their combined synergistic effects have not been studied extensively. In this study, we evaluated the effects of both AME and WJ‐MSC‐Exos, individually and together, on the proliferation of corneal keratocytes as well as their ability to promote in vitro cell migration, wound healing, and their impact on cellular morphology. Our findings indicated that the presence of both exosomes (3 × 105 Exo/mL) and AME (50 μg/mL) synergistically enhance the proliferation of corneal keratocytes. Combined use of these solutions (3 × 105 Exo/mL + 50 μg/mL) increased cell proliferation compared to only 50 μg/mL AME treatment on day 3 (**** p < 0.0001). This mixture treatment (3 × 105 Exo/mL + 50 μg/mL) increased wound closure rate compared to isolated WJ‐MSC‐Exo treatment (3 × 105 Exo/mL) (*p < 0.05). Overall, corneal keratocytes treated with AME and WJ‐MSC‐Exo (3 × 105 Exo/mL + 50 μg/mL) mixture resulted in enhanced proliferation and wound healing tendency. Utilization of combined use of AME and WJ‐MSC‐Exo can pave the way for a promising foundation for corneal repair research.
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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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".