CNS Progenitor Cells Promote a Permissive Environment for Neurite Outgrowth via a Matrix Metalloproteinase-2-Dependent Mechanism
Bibliographic record
Abstract
Transplantation of progenitor cells to the CNS has shown promise in neuronal and glial replacement and as a means of rescuing host neurons from apoptosis. Here we examined the effect of progenitor grafts on neurite extension in the degenerating retina of rd1 (retinal degeneration 1) mice. Transplantation of retinal progenitor cells induced increased matrix metalloproteinase-2 (MMP2) secretion, partly from activated glial cells, which was then activated by neuronally expressed MMP14. Active MMP2 resulted in proteolysis of the neurite outgrowth inhibitors CD44 and neurocan in the degenerative retina, allowing significantly increased neurite outgrowth across the border between abutting nondystrophic and rd1 retinas. Progenitor-induced enhancement of outgrowth was abrogated by an MMP inhibitor or by coculture with retinal explants from MMP2-/- mice. This study provides the first identification of an MMP2-dependent mechanism by which exogenous progenitor cells alter the host environment to promote neural regeneration. This suggests a novel therapeutic role for progenitor cells in the treatment of CNS degenerative 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 | 0.000 | 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".