<scp>LAPC</scp>s contribute to the pathogenesis of allergen‐induced allergic airway inflammation in mice
Bibliographic record
Abstract
BACKGROUND: The inflammatory immune response associated with allergic airway inflammation in asthma involves T helper type 2 (Th2) immunity. Given the data that a newly described late activator antigen-presenting cell (LAPC) population promotes Th2 immunity in viral infections, we undertook studies to investigate whether LAPCs have a pathogenic role in allergic airway inflammation. METHODS: We employed acute ovalbumin (OVA) and house dust mite (HDM) sensitization and challenge models to establish allergic airway inflammation in mice, followed by the analysis of lungs and draining lymph node (DLN) cell infiltrates, immunoglobulin E (IgE) production, and airway hyper-responsiveness (AHR). We tested whether adoptive transfer of LAPCs isolated from mice with established allergic airway inflammation augments the development of sensitization in naïve mice. RESULTS: We provide evidence that in both OVA and HDM mouse models of allergic inflammation, LAPCs accumulate in the lungs and draining lymph nodes (DLNs), concomitant with the onset of lung pathology, allergen-specific IgE production, eosinophilia, and Th2 cytokine production. Adoptive transfer experiments using OVA-activated LAPCs reveal exacerbation of disease pathology with an increase in lung inflammatory cells, eosinophilia, circulating IgE, Th2 cytokine production, and a worsening of AHR. OVA-activated LAPCs preferentially increased GATA-3 induction in naïve CD4(+) T cells. CONCLUSIONS: Together, these data suggest an important role for LAPCs in polarizing the Th2 response in mouse models of allergic airway inflammation.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".