Dichotomy between Neurokinin Receptor Actions in Modulating Allergic Airway Responses in an Animal Model of Helper T Cell Type 2 Cytokine-associated Inflammation
Bibliographic record
Abstract
Neurokinins (NKs), which include substance P (SP) and neurokinin A (NKA), act through NK-1 and NK-2 receptors. There is considerable evidence of interaction between the neurogenic and the immune systems, and NKs are candidates for mediating such interactions. We hypothesized that selective inhibition of pulmonary NK-1 or NK-2 receptors may modulate immune responses so as to prevent the development of allergic airway responses in the atopic BN rat sensitized to ovalbumin (OA). To address this hypothesis, we have validated our animal model by showing that NK-1 and NK-2 receptors are expressed in the lungs, and that SP is released in the airways after allergen challenge. The selective NK-1 (CP-99,994) or NK-2 (SR-48968) antagonists before allergen challenge failed to reduce the allergic early airway responses. In contrast, both neurokinin antagonists decreased allergen-induced late airway responses in OA-challenged animals. However, only the NK-2 antagonist decreased the eosinophil numbers in the bronchoalveolar lavage (BAL). Likewise, the NK-2, but not NK-1, antagonist decreased both Th1 (INF-gamma) and Th2 (IL-4 and -5) cytokine expression in BAL cells by in situ hybridization. These results provide initial in vivo evidence linking neurokinins to the regulation of cytokine expression in cells without discrimination as to their phenotype. We conclude that there is a dichotomy between NK receptors in the modulation of the allergic airway inflammation, which has important implications for future therapeutic strategies for asthma using the NK antagonists.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".