Progression of Cystic Fibrosis Lung Disease from Childhood to Adulthood: Neutrophils, Neutrophil Extracellular Trap (NET) Formation, and NET Degradation
Bibliographic record
Abstract
Genetic defects in cystic fibrosis (CF) transmembrane conductance regulator (CFTR) gene cause CF. Infants with CFTR mutations show a peribronchial neutrophil infiltration prior to the establishment of infection in their lung. The inflammatory response progressively increases in children that include both upper and lower airways. Infectious and inflammatory response leads to an increase in mucus viscosity and mucus plugging of small and medium-size bronchioles. Eventually, neutrophils chronically infiltrate the airways with biofilm or chronic bacterial infection. Perpetual infection and airway inflammation destroy the lungs, which leads to increased morbidity and eventual mortality in most of the patients with CF. Studies have now established that neutrophil cytotoxins, extracellular DNA, and neutrophil extracellular traps (NETs) are associated with increased mucus clogging and lung injury in CF. In addition to opportunistic pathogens, various aspects of the CF airway milieux (e.g., airway pH, salt concentration, and neutrophil phenotypes) influence the NETotic capacity of neutrophils. CF airway milieu may promote the survival of neutrophils and eventual pro-inflammatory aberrant NETosis, rather than the anti-inflammatory apoptotic death in these cells. Degrading NETs helps to manage CF airway disease; since DNAse treatment release cytotoxins from the NETs, further improvements are needed to degrade NETs with maximal positive effects. Neutrophil-T cell interactions may be important in regulating viral infection-mediated pulmonary exacerbations in patients with bacterial infections. Therefore, clarifying the role of neutrophils and NETs in CF lung disease and identifying therapies that preserve the positive effects of neutrophils, while reducing the detrimental effects of NETs and cytotoxic components, are essential in achieving innovative therapeutic advances.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".