Granulocyte macrophage colony‐stimulating factor promotes regeneration of retinal ganglion cells in vitro through a mammalian target of rapamycin‐dependent mechanism
Bibliographic record
Abstract
Lack of regeneration in the adult central nervous system (CNS) is a major hurdle that limits recovery from neurological ailments. Although accumulating research suggests the possibility of axon regeneration by targeting intrinsic signaling mechanisms, it remains a matter of controversy whether functional recovery can be achieved by manipulating aspects of molecular signaling. Recent studies have shown that granulocyte macrophage colony-stimulating factor (GM-CSF) may be an effective means of targeting repair following CNS injury; how this molecule is able to produce this effect is not known. Indeed, GM-CSF has been shown to promote neuronal survival, potentially through activation of as yet unknown cytokine-dependent signals and potentially through regulation of antiapoptotic mechanisms. It is well established that the loss of intrinsic regenerative ability is highly correlated with development of CNS neurons. We therefore designed experiments, using a well-established in vitro retinal ganglion cell (RGC) culture system, to evaluate the effect of GM-CSF on axon growth and cell survival and define possible mechanisms involved in GM-CSF-mediated effects in vitro. Several developmental stages were evaluated, with particular focus placed on stages at which axon growth is known to be significantly diminished. Our results reveal that GM-CSF not only promotes axon growth in postnatal RGCs but also enhances cell survival through a mammalian target of rapamycin (mTOR)-dependent mechanism.
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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".