Nerve Growth Factor Expression and Release in Allergic Inflammatory Disease of the Upper Airways
Bibliographic record
Abstract
It is well known that allergic airways disease is characterized by inflammation and hyperresponsiveness, but the link between these two conditions has not been elucidated. We have previously shown that in allergic rhinitis, hyperresponsiveness is attributable to increased neural reactivity. We thus hypothesized that nerve growth factor (NGF), which is expressed by inflammatory cells and effects changes that lead to increased neural responsiveness, could be a pivotal mediator in this disease. Using reverse transcription-polymerase chain reaction (RT-PCR), Western immunoblotting, and ELISA to evaluate NGF expression and release, we found that subjects with allergic rhinitis have significantly decreased NGF mRNA in superficial nasal scrapings and significantly higher baseline concentrations of NGF protein in nasal lavage fluids, compared with control subjects. Nasal provocation with allergen significantly increased NGF protein in nasal lavage fluids of subjects with allergic rhinitis, but not of control subjects. The concentrations of NGF protein in nasal lavage fluids were not affected by provocation with the vehicle for allergen or with histamine. These data provide the first evidence of a steady state of dysregulation in mucosal NGF expression and release in allergic rhinitis, and support a role of this neurotrophin in the pathophysiology of allergic inflammatory disease of the human airways.
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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".