Nerve Growth Factor and Neuroimmune Interactions in Inflammatory Diseases
Bibliographic record
Abstract
Discovered almost 50 years ago, nerve growth factor (NGF) has been extensively studied in various biological systems. NGF has recently been suggested to play an important role in mediating and/or regulating immune response, in addition to its trophic and tropic effects on nerve growth and regeneration It is clear that in complex interactions between immune cells and nervous system NGF plays a central role. We have only just begun to identify and understand the direct mechanisms by which NGF activates target cells, the precise identity of the target cells, and the particular factors released from target cells. Nerve growth factor together with possibly other neurotrophins such as BDNF (brain-derived nerve growth factor), GDNF (glial-derived nerve growth factor) or NT3 are important modulators of immunity. More detailed studies are needed at the receptor, mediator and cellular levels to better understand the neuroimmunomodulatory properties of neurothrophins and NGF. The nature of the involvement of NGF in inflammation and inflammatory diseases remains a particularly interesting question. By blocking NGF or mediators released upon NGF activation, we are able to control the progress of inflammation, thereby opening many therapeutic opportunities for the future.
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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 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".