EGF receptor activation during allergic sensitization affects IL‐6‐induced T‐cell influx to airways in a rat model of asthma
Bibliographic record
Abstract
EGF receptor (EGFR) is involved in cell differentiation and proliferation in airways and may trigger cytokine production by T cells. We hypothesized that EGFR inhibition at the time of allergic sensitization may affect subsequent immune reactions. Brown Norway rats were sensitized with OVA, received the EGFR tyrosine kinase inhibitor, AG1478 from days 0 to 7 and OVA challenge on day 14. OVA-specific IgE in serum and cytokines and chemokines in BAL were measured 24 h after challenge. To evaluate effects on airway hyperresponsiveness (AHR), rats were sensitized, treated with AG1478, intranasally challenged, and then AHR was assessed. Furthermore chemotactic activity of BALF for CD4(+) T cells was examined. The eosinophils, neutrophils and lymphocytes in BAL were increased by OVA and only the lymphocytes were reduced by AG1478. OVA significantly enhanced IL-6 concentration in BAL, which was inhibited by AG1478. However AHR, OVA-specific IgE and IL-4 mRNA expression in CD4(+) T cells were not affected by AG1478. BALF from OVA-sensitized/challenged rats induced CD4(+) T-cell migration, which was inhibited by both AG1478 treatment in vivo and neutralization of IL-6 in vitro. EGFR activation during sensitization may affect the subsequent influx of CD4(+) T cells to airways, mainly mediated through IL-6.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".