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Record W4408876698 · doi:10.1136/bcr-2024-260300

Autoimmune response to COVID-19 characterised by eosinophilia, NETosis and EETosis

2025· article· en· W4408876698 on OpenAlexaff
Aram Karkar, Parameswaran Nair, Manali Mukherjee, Natya Raghavan

Bibliographic record

VenueBMJ Case Reports · 2025
Typearticle
Languageen
FieldImmunology and Microbiology
TopicNeutrophil, Myeloperoxidase and Oxidative Mechanisms
Canadian institutionsHamilton Health SciencesHamilton General HospitalMcMaster University
Fundersnot available
KeywordsMethylprednisoloneMedicineEosinophiliaExtracorporeal membrane oxygenationDexamethasoneEosinophilMechanical ventilationSeptic shockEx vivoImmunologyExtracellularCoronavirus disease 2019 (COVID-19)Neutrophil extracellular trapsPneumoniaGastroenterologyInternal medicineIn vivoAsthmaInflammationBiologySepsis

Abstract

fetched live from OpenAlex

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 7.5×10 9 /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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.435
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.015
GPT teacher head0.296
Teacher spread0.280 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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