The Roles of Neutrophils in Cytokine Storms
Bibliographic record
Abstract
A cytokine storm is an abnormal discharge of soluble mediators following an inappropriate inflammatory response that leads to immunopathological events. Cytokine storms can occur after severe infections as well as in non-infectious situations where inflammatory cytokine responses are initiated, then exaggerated, but fail to return to homeostasis. Neutrophils, macrophages, mast cells, and natural killer cells are among the innate leukocytes that contribute to the pathogenesis of cytokine storms. Neutrophils participate as mediators of inflammation and have roles in promoting homeostatic conditions following pathological inflammation. This review highlights the advances in understanding the mechanisms governing neutrophilic inflammation against viral and bacterial pathogens, in cancers, and in autoimmune diseases, and how neutrophils could influence the development of cytokine storm syndromes. Evidence for the destructive potential of neutrophils in their capacity to contribute to the onset of cytokine storm syndromes is presented across a multitude of clinical scenarios. Further, a variety of potential therapeutic strategies that target neutrophils are discussed in the context of suppressing multiple inflammatory conditions.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".