TNF is required for TLR ligand–mediated but not protease-mediated allergic airway inflammation
Bibliographic record
Abstract
Asthma is associated with exposure to a wide variety of allergens and adjuvants. The extent to which overlap exists between the cellular and molecular mechanisms triggered by these various agents is poorly understood, but it might explain the differential responsiveness of patients to specific therapies. In particular, it is unclear why some, but not all, patients benefit from blockade of TNF. Here, we characterized signaling pathways triggered by distinct types of adjuvants during allergic sensitization. Mice sensitized to an innocuous protein using TLR ligands or house dust extracts as adjuvants developed mixed eosinophilic and neutrophilic airway inflammation and airway hyperresponsiveness (AHR) following allergen challenge, whereas mice sensitized using proteases as adjuvants developed predominantly eosinophilic inflammation and AHR. TLR ligands, but not proteases, induced TNF during allergic sensitization. TNF signaled through airway epithelial cells to reprogram them and promote Th2, but not Th17, development in lymph nodes. TNF was also required during the allergen challenge phase for neutrophilic and eosinophilic inflammation. In contrast, TNF was dispensable for allergic airway disease in a protease-mediated model of asthma. These findings might help to explain why TNF blockade improves lung function in only some patients with asthma.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".