Neutrophil Extracellular Traps Increase Airway Mucus Viscoelasticity and Slow Mucus Particle Transit
Bibliographic record
Abstract
Abstract Mucus obstruction is a key feature of many inflammatory airway diseases. Neutrophil extracellular traps (NETs) are released upon neutrophil stimulation and consist of extracellular chromatin networks studded with cytotoxic proteins. When released in the airways, these NETs can become part of the airway mucus. We hypothesized that the extracellular DNA and/or oxidative stress (e.g., by the release of reactive oxygen species and myeloperoxidase during NETs formation in the airways) would increase mucus viscoelasticity. We collected human airway mucus from endotracheal tubes of healthy patients admitted for elective surgery and coincubated these samples with NETs from phorbol 12-myristate 13-acetate-stimulated neutrophils. Unstimulated neutrophils served as controls, and blocking experiments were performed with dornase alfa for extracellular DNA and the free radical scavenger dimethylthiourea for oxidation. Compared with controls, the coincubation of mucus with NETs resulted in 1) significantly increased mucus viscoelasticity (macrorheology) and 2) significantly decreased mesh pore size of the mucus and decreased movement of muco-inert nanoparticles through the mucus (microrheology), but 3) NETs did not cause visible changes in the microstructure of the mucus by scanning EM. Incubation with either dornase alfa or dimethylthiourea attenuated the observed changes in macrorheology and microrheology. This suggests that the release of NETs may contribute to airway mucus obstruction by increasing mucus viscoelasticity and that this effect is not solely due to the release of DNA but may in part be due to oxidative stress.
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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) | 0.002 | 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".