The Nerve Growth Factor Signaling and Its Potential as Therapeutic Target for Glaucoma
Bibliographic record
Abstract
Neuroprotective therapies which focus on factors leading to retinal ganglion cells (RGCs) degeneration have been drawing more and more attention. The beneficial effects of nerve growth factor (NGF) on the glaucoma have been recently suggested, but its effects on eye tissue are complex and controversial in various studies. Recent clinical trials of systemically and topically administrated NGF demonstrate that NGF is effective in treating several ocular diseases, including glaucoma. NGF has two receptors named high affinity NGF tyrosine kinase receptor TrkA and low affinity receptor p75NTR. Both receptors exist in cells in retina like RGC (expressing TrkA) and glia cells (expressing p75NTR). NGF functions by binding to TrkA or p75NTR alone or both together. The binding of NGF to TrkA alone in RGC promotes RGC's survival and proliferation through activation of TrkA and several prosurvival pathways. In contrast, the binding of NGF to p75NTR leads to apoptosis although it also promotes survival in some cases. Binding of NGF to both TrkA and p75NTR at the same time leads to survival in which p75NTR functions as a TrkA helping receptor. This review discusses the current understanding of the NGF signaling in retina and the therapeutic implications in the treatment of glaucoma.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".