The impact of COVID-19 infection on eosinophil dynamics
Bibliographic record
Abstract
Background: Recent studies report significantly lower leukocyte counts, particularly eosinophils, during COVID-19 infection. We hypothesized that eosinophils migrate from blood to the airway tissue in response to chemoattractants expressed by airway epithelials cells during the viral invasion, where they release their granule proteins. Methods: Blood samples were obtained from 65 COVID-ve and 84 COVID+ve patients. CBC results were extracted from medical records upon admission (day 0), during hospitalization (day 10) and at follow up (3 months). Eosinophil granule proteins were quantified from blood sampled during hospitalization using the Luminex assay. Results: Eosinophil counts were consistently lower in COVID +ve patients across all three time points (p < 0.01), with a slower rate of recovery compared to COVID -ve patients. Levels of all eosinophil granule proteins were significantly lower in COVID +ve compared to COVID -ve patients (p < 0.05). Conclusion: Eosinophil levels and their associated degranulation products were both consistently lower in patients with COVID-19, suggesting these cells were not accumulating and degranulating in the infected airway tissue. The slow recovery of eosinophil in these patients during the follow-up visit suggests a potential role of the viral infection in depleting these immune cells from the bloodstream. The underlying mechanisms and implications for COVID-19 pathogenesis requires further elucidation. erj;64/suppl_68/PA1510/F1 F1 F1
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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 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".