Bibliographic record
Abstract
Acute inflammation is a localized and self-limited innate host-defense mechanism against invading pathogens and tissue injury. Neutrophils, the most abundant immune cells in humans, play pivotal roles in host defense by eradicating invading pathogens and debris. Ideally, elimination of the offending insult prompts repair and return to homeostasis. However, the neutrophils` powerful weaponry to combat microbes can also cause tissue damage and neutrophil-driven inflammation is a unifying mechanism for many diseases. For timely resolution of inflammation, in addition to stopping neutrophil recruitment, emigrated neutrophils need to be disarmed and removed from the affected site. Accumulating evidence documents the phenotypic and functional versatility of neutrophils far beyond their antimicrobial functions. Hence, understanding the receptors that integrate opposing cues and checkpoints that determine the fate of neutrophils in inflamed tissues provides insight into the mechanisms that distinguish protective and dysregulated, excessive inflammation and govern resolution. This review aims to provide a brief overview and update with key points from recent advances on neutrophil heterogeneity, functional versatility and signaling, and discusses challenges and emerging therapeutic approaches that target neutrophils to enhance the resolution of inflammation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.003 | 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".