Neutrophil Extracellular Trap Formation: Physiology, Pathology, and Pharmacology
Bibliographic record
Abstract
Neutrophil extracellular traps (NETs), a unique DNA framework decorated with antimicrobial peptides, have been in the scientific limelight for their role in a variety of pathologies ranging from cystic fibrosis to cancer. The formation of NETs, as well as relevant regulatory mechanisms, physiological factors, and pharmacological agents have not been systematically discussed in the context of their beneficial and pathological aspects. Novel forms of NET formation including vital NET formation continue to be uncovered, however, there remain fundamental questions around established mechanisms such as NADPH-oxidase (Nox)-dependent and Nox-independent NET formation. Whether NET formation takes place in the tissue versus the bloodstream, internal factors (e.g. reactive oxygen species (ROS) production and transcription factor activation), and external factors (e.g. alkaline pH and hypertonic conditions), have all been demonstrated to influence specific NET pathways. Elements of neutrophil biology such as transcription and mitochondria, which were previously of unknown significance, have been identified as critical mediators of NET formation through facilitating chromatin decondensation and generating ROS, respectively. While promising therapeutics inhibiting ROS, transcription, and gasdermin D are being investigated, neutrophil phagocytosis plays a critical role in host defense and any therapies targeting NET formation must avoid impairing the physiological functions of these cells. This review summarizes what is known in the many domains of NET research, highlights the most relevant challenges in the field, and inspires new questions that can bring us closer to a unified model of NET formation.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".