Goblet Cell Rebound and Airway Dysfunction with Corticosteroid Withdrawal in a Mouse Model of Asthma
Bibliographic record
Abstract
RATIONALE: Although corticosteroids are highly effective at preventing allergen-induced increases in goblet cell numbers, we observed in unpublished experiments a rebound increase in goblet cell numbers in mice after the simultaneous withdrawal of corticosteroid and cessation of exposure to allergen that reached levels greater than those observed in mice exposed to allergen alone, without corticosteroid treatment. OBJECTIVES: To formally explore the goblet cell hyperplasia rebound observed after corticosteroid withdrawal in allergen-exposed mice to determine the mechanism responsible for this previously undescribed pathology. METHODS: Mice airways were assessed for mucin-containing goblet cells after exposure to varying durations of allergen and corticosteroid. MEASUREMENTS AND MAIN RESULTS: We confirmed that the simultaneous withdrawal of corticosteroid and cessation of exposure to allergen resulted in a goblet cell hyperplasia rebound that reached levels greater than those observed in allergen-exposed corticosteroid naive mice. Importantly, the goblet cell rebound was associated with a significant airway dysfunction greater than that observed in allergen-exposed corticosteroid naive mice. The goblet cell hyperplasia rebound is independent of the type of corticosteroid or allergen and was associated with an increased level of bronchoalveolar lavage IL-13. Inhibition of IL-13, but not CD4+ T cells, completely inhibited the goblet cell hyperplasia rebound and, critically, the associated airway dysfunction. CONCLUSIONS: These findings suggest that certain corticosteroid treatment regimes may actually potentiate airway remodeling and dysfunction in patients with asthma and lead to increased exacerbations and worsening of asthma symptoms.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".