Auto-amplification and spatial propagation of neutrophil extracellular traps
Bibliographic record
Abstract
The release of cellular DNA as neutrophil extracellular traps (NETs) plays a pivotal role in the immune response to pathogens by physically entrapping and killing microbes. NET release occurs at a greater frequency within neutrophil clusters and swarms, indicating a potential for collective behavior. However, little is known about how dense clustering of cells influences the frequency of NET release. Using an image-based assay for NETosis in nanowells, we show that the frequency of NETosis increases with cell density. We then co-incubate NETotic neutrophils with naïve neutrophils and find that NETotic neutrophils can induce secondary NETosis in naïve neutrophils in a cell density-dependent manner. Further mechanistic studies show that secondary NETosis is caused by a combination of DNA and protein factors. Finally, we immobilize NETotic neutrophils in a plaque, and then place the plaque near naïve neutrophils to characterize the spatial propagation of secondary NETosis. We find that secondary NETosis from naïve neutrophils increases over time, but remains spatially restricted to the periphery of the plaque. Together, we show that NETosis is an auto-amplified process, but that the spatial propagation of NET release is strictly regulated.
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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 | 0.000 |
| 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".