Human Neutrophil Peptides and Phagocytic Deficiency in Bronchiectatic Lungs
Bibliographic record
Abstract
RATIONALE: A well-known clinical paradox is that severe bacterial infections persist in the lungs of patients with cystic fibrosis (CF) despite the abundance of polymorphonuclear neutrophils (PMN) and the presence of a high concentration of human neutrophil peptides (HNP), both of which are expected to kill the bacteria but fail to do so. The mechanisms remain unknown. OBJECTIVES: This study examined several possible mechanisms to understand this paradox. METHODS: PMN were isolated from sputum and blood of subjects with and without CF or non-CF bronchiectasis for phagocytic assays. HNP isolated from patients with CF were used to stimulate healthy PMN followed by phagocytic tests. MEASUREMENTS AND MAIN RESULTS: PMN isolated from the sputum of the bronchiectatic patients display defective phagocytosis that correlated with high concentrations of HNP in the lung. When healthy PMN were incubated with HNP, decreased phagocytic capacity was observed in association with depressed surface Fc gamma RIII, actin-filament remodeling, enhanced intracellular Ca(2+), and degranulation. Treatment of PMN with an intracellular Ca(2+) blocker or alpha1-proteinase inhibitor to attenuate the activity of HNP largely prevented the HNP-induced phagocytic deficiency. Intratracheal instillation of HNP in Pallid mice (genetically deficient in alpha1-proteinase inhibitor) resulted in a greater PMN lung infiltration and phagocytic deficiency compared with wild-type mice. CONCLUSIONS: HNP or PMN alone exert antimicrobial ability, which was lost as a result of their interaction. These effects of HNP may help explain the clinical paradox seen in patients with inflammatory lung diseases, suggesting HNP as a novel target for clinical therapy.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".