Emergence of dysfunctional neutrophils with a defect in arginase-1 release in severe COVID-19
Bibliographic record
Abstract
Neutrophilia occurs in patients infected with SARS-CoV-2 (COVID-19) and is predictive of poor outcomes. Here, we link heterogenous neutrophil populations to disease severity in COVID-19. We identified neutrophils with features of cellular aging and immunosuppressive capacity in mild COVID-19 and features of neutrophil immaturity and activation in severe disease. The low-density neutrophil (LDN) number in circulating blood correlated with COVID-19 severity. Many of the divergent neutrophil phenotypes in COVID-19 were overrepresented in the LDN fraction and were less detectable in normal-density neutrophils. Functionally, neutrophils from patients with severe COVID-19 displayed defects in neutrophil extracellular trap formation and reactive oxygen species production. Soluble factors secreted by neutrophils from these patients inhibited T cell proliferation. Neutrophils from patients with severe COVID-19 had increased expression of arginase-1 protein, a feature that was retained in convalescent patients. Despite this increase in intracellular expression, there was a reduction in arginase-1 release by neutrophils into serum and culture supernatants. Furthermore, neutrophil-mediated T cell suppression was independent of arginase-1. Our results indicate the presence of dysfunctional, activated, and immature neutrophils in severe COVID-19.
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How this classification was reachedexpand
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 | 0.001 |
| 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) | 0.003 | 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 teacher head, 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".