Mast Cell and Eosinophil Activation Are Associated With COVID-19 and TLR-Mediated Viral Inflammation: Implications for an Anti-Siglec-8 Antibody
Bibliographic record
Abstract
Coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 infection represents a global health crisis. Immune cell activation via pattern recognition receptors has been implicated as a driver of the hyperinflammatory response seen in COVID-19. However, our understanding of the specific immune responses to SARS-CoV-2 remains limited. Mast cells (MCs) and eosinophils are innate immune cells that play pathogenic roles in many inflammatory responses. Here we report MC-derived proteases and eosinophil-associated mediators are elevated in COVID-19 patient sera and lung tissues. Stimulation of viral-sensing toll-like receptorsin vitroand administration of synthetic viral RNAin vivoinduced features of hyperinflammation, including cytokine elevation, immune cell airway infiltration, and MC-protease production—effects suppressed by an anti-Siglec-8 monoclonal antibody which selectively inhibits MCs and depletes eosinophils. Similarly, anti-Siglec-8 treatment reduced disease severity and airway inflammation in a respiratory viral infection model. These results suggest that MC and eosinophil activation are associated with COVID-19 inflammation and anti-Siglec-8 antibodies are a potential therapeutic approach for attenuating excessive inflammation during viral infections.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".