Protease allergens as initiators–regulators of allergic inflammation
Bibliographic record
Abstract
Tremendous progress in the last few years has been made to explain how seemingly harmless environmental proteins from different origins can induce potent Th2-biased inflammatory responses. Convergent findings have shown the key roles of allergens displaying proteolytic activity in the initiation and progression of the allergic response. Through their propensity to activate IgE-independent inflammatory pathways, certain allergenic proteases are now considered as initiators for sensitization to themselves and to non-protease allergens. The protease allergens degrade junctional proteins of keratinocytes or airway epithelium to facilitate allergen delivery across the epithelial barrier and their subsequent uptake by antigen-presenting cells. Epithelial injuries mediated by these proteases together with their sensing by protease-activated receptors (PARs) elicit potent inflammatory responses resulting in the release of pro-Th2 cytokines (IL-6, IL-25, IL-1β, TSLP) and danger-associated molecular patterns (DAMPs; IL-33, ATP, uric acid). Recently, protease allergens were shown to cleave the protease sensor domain of IL-33 to produce a super-active form of the alarmin. At the same time, proteolytic cleavage of fibrinogen can trigger TLR4 signaling, and cleavage of various cell surface receptors further shape the Th2 polarization. Remarkably, the sensing of protease allergens by nociceptive neurons can represent a primary step in the development of the allergic response. The goal of this review is to highlight the multiple innate immune mechanisms triggered by protease allergens that converge to initiate the allergic response.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".