Damage-Associated Molecular Patterns Control Neutrophil Recruitment
Bibliographic record
Abstract
Neutrophils are recruited to a site of infection or injury where they help initiate the acute inflammatory response. In instances of sterile inflammation, where no microbial threats are present, this neutrophil recruitment is mediated by the release of danger signals or damage-associated molecular patterns (DAMPs) from disrupted cells and tissues. At basal state, many of these substances are sequestered and remain hidden within the cell, but are released following the rupture of the plasma membrane. In other instances, these DAMPs are undetected by the innate immune system unless chemically or proteolytically modified by tissue damage. DAMPs may be directly detected by neutrophils themselves and modulate their recruitment to sites of damage or, alternatively, they can act on other cell types which in turn facilitate the arrival of neutrophils to a site of injury. In this review, we outline the direct and indirect effects of a number of DAMPs, notably extracellular ATP, mitochondrial formylated peptides and mitochondrial DNA, all of which are released by necrotic cells. We examine the effect of these substances on the recruitment and behaviour of neutrophils to sites of sterile injury. We also highlight research which suggests that neutrophils are actively involved in triggering the resolution phase of an inflammatory response. This review brings to light a growing body of work that demonstrates that the release of DAMPs and the ensuing influx of neutrophils plays an important functional role in the inflammatory response, even when no pathogens are present.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".