Targeting Neutrophil Apoptosis for Enhancing the Resolution of Inflammation
Bibliographic record
Abstract
Resolution of acute inflammation is an active process that requires inhibition of further leukocyte recruitment and removal of leukocytes from inflamed sites. Emigrated neutrophils undergo apoptosis before being removed by scavenger macrophages. Recent studies using a variety of gene knockout, transgenic and pharmacological strategies in diverse models of inflammation established neutrophil apoptosis as a critical control point in resolving inflammation. Analysis of death mechanisms revealed distinct features in executing the death program in neutrophils, which can be exploited as targets for controlling the lifespan of neutrophils. Indeed, anti-inflammatory and pro-resolution lipid mediators derived from essential fatty acids, such as lipoxin A4 and resolvin E1, autacoids and proteins, such as annexin A1 and TRAIL, and cyclin-dependent kinase inhibitors, can enhance the resolution of inflammation through induction of neutrophil apoptosis and promoting their removal by efferocytosis. In this review, we discuss recent advances in understanding the molecular basis of these actions, highlighting the potential of therapeutic induction of neutrophil apoptosis for dampening neutrophil-mediated tissue injury and inflammation underlying a variety of diseases.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".