Autoimmune response to COVID-19 characterised by eosinophilia, NETosis and EETosis
Bibliographic record
Abstract
A previously healthy man in his 50s was admitted with severe COVID-19 pneumonia requiring extracorporeal membrane oxygenation (ECMO) support. He was found to have persistent eosinophilia, with a peak level of 7.5×109/L. He had received multiple courses of steroids, including dexamethasone and methylprednisolone, with no improvement. On day 47, hydrocortisone was initiated for the treatment of septic shock. This resulted in the normalisation of the eosinophil count, which correlated with radiographic and clinical improvement. The patient was transitioned off ECMO and eventually weaned off mechanical ventilation. Studies conducted using the patient’s serum identified circulating autoantibodies, which induced neutrophil extracellular traps (NETosis) and eosinophil extracellular traps (EETosis). A varying response of the eosinophils to different corticosteroids was observed in vivo that corresponded with the ex vivo experiments. This case highlights an unrecognised phenomenon of differential response to corticosteroids in severe COVID-19. Furthermore, this is the first reported case of EETosis in COVID-19.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".