The Role of Nerve Growth Factor on the Ocular Surface: A Review of the Current Experimental Research and Clinical Practices
Bibliographic record
Abstract
The ocular surface is susceptible to a wide spectrum of inflammatory, degenerative, and neurotrophic diseases that can impair vision. The complex pathophysiology and limited therapeutic options associated with these conditions continue to pose significant clinical challenges. Nerve Growth Factor (NGF), a neurotrophin initially recognized for its role in neuronal survival and differentiation, has emerged as a key regulator of ocular surface homeostasis and repair. Beyond its neurotrophic functions, NGF is suggested to influence epithelial proliferation, immune responses, tear secretion, and angiogenesis. Experimental and clinical studies have implicated NGF in both the pathogenesis and potential treatment of various ocular surface diseases, including allergic conjunctivitis, neurotrophic keratopathy (NK), immune-mediated and herpetic keratitis, and dry eye disease (DED), as well as post-surgical corneal wound healing. Notably, recombinant human NGF (rhNGF, cenegermin) has been approved as the first topical biologic therapy for NK. Despite encouraging clinical outcomes, challenges such as high treatment costs, limited long-term data, and potential proangiogenic effects remain. This review consolidates current evidence on the role of NGF in ocular surface health and disease, highlighting its biological mechanisms, clinical applications, and future therapeutic potential.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".