<p>Nerve Growth Factor Signaling and Its Contribution to Pain</p>
Bibliographic record
Abstract
Nerve growth factor (NGF) is a neurotrophic protein essential for the growth, differentiation, and survival of sympathetic and sensory afferent neurons during development. A substantial body of evidence, based on both animal and human studies, demonstrates that NGF plays a pivotal role in modulation of nociception in adulthood. This has spurred development of a variety of novel analgesics that target the NGF signaling pathway. Here, we present a narrative review designed to summarize how NGF receptor activation and downstream signaling alters nociception through direct sensitization of nociceptors at the site of injury and changes in gene expression in the dorsal root ganglion that collectively increase nociceptive signaling from the periphery to the central nervous system. This review illustrates that NGF has a well-known and multifunctional role in nociceptive processing, although the precise signaling pathways downstream of NGF receptor activation that mediate nociception are complex and not completely understood. Additionally, much of the existing knowledge derives from studies performed in animal models and may not accurately represent the human condition. However, available data establish a role for NGF in the modulation of nociception through effects on the release of inflammatory mediators, nociceptive ion channel/receptor activity, nociceptive gene expression, and local neuronal sprouting. The role of NGF in nociception and the generation and/or maintenance of chronic pain has led to it becoming a novel and attractive target of pain therapeutics for the treatment of chronic pain conditions.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".