Beyond host defense and tissue injury: the emerging role of neutrophils in tissue repair
Bibliographic record
Abstract
Neutrophils, the most abundant immune cells in human blood, play a fundamental role in host defense against invading pathogens and tissue injury. Neutrophils carry potentially lethal weaponry to the affected site. Inadvertent and perpetual neutrophil activation could lead to nonresolving inflammation and tissue damage, a unifying mechanism of many common diseases. The prevailing view emphasizes the dichotomy of their function, host defense versus tissue damage. However, tissue injury may also persist during neutropenia, which is associated with disease severity and poor outcome. Numerous studies highlight neutrophil phenotypic heterogeneity and functional versatility, indicating that neutrophils play more complex roles than previously thought. Emerging evidence indicates that neutrophils actively orchestrate resolution of inflammation and tissue repair and facilitate return to homeostasis. Thus, neutrophils mobilize multiple mechanisms to limit the inflammatory reaction, assure debris removal, matrix remodeling, cytokine scavenging, macrophage reprogramming, and angiogenesis. In this review, we will summarize the homeostatic and tissue-reparative functions and mechanisms of neutrophils across organs. We will also discuss how the healing power of neutrophils might be harnessed to develop novel resolution and repair-promoting therapies while maintaining their defense functions.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".