Neutrophil defensins mediate acute inflammatory response and lung dysfunction in dose-related fashion
Bibliographic record
Abstract
High concentrations of neutrophil defensins from airway and blood have been reported in patients with inflammatory lung diseases, but their exact role is unclear. We investigated the direct effect of defensins on the lungs of mice. Intratracheal instillation of purified defensins (5-30 mg/kg) induced a progressive reduction in peripheral arterial O(2) saturation, increased lung permeability, and enhanced the lung cytochrome c content. These indexes of acute lung dysfunction were associated with an increased total cell number and a significant neutrophil influx into the lung [5.1 +/- 0.04% in control vs. 48.6 +/- 12.7% in the defensin (30 mg/kg) group, P < 0.05]. Elastase concentrations in the bronchoalveolar lavage (BAL) fluids increased from 38 +/- 11 ng/ml (control) to 80 +/- 4 ng/ml (defensins, P < 0.05). Five hours after defensin instillation, concentrations of tumor necrosis factor-alpha and macrophage inflammatory protein-2 in BAL fluid were significantly increased. High levels of monocyte chemoattractant protein-1 in BAL fluid and plasma were also found after defensin stimulation. We conclude that intratracheal instillation of defensins causes acute lung inflammation and dysfunction, suggesting that high concentrations of defensins in the airways may play an important role in the pathogenesis of inflammatory lung diseases.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".