Mast Cell and Eosinophil Activation Are Associated With COVID-19 and TLR-Mediated Viral Inflammation: Implications for an Anti-Siglec-8 Antibody
Why this work is in the frame
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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 receptors in vitro and administration of synthetic viral RNA in vivo induced 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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it