Direct and indirect bacterial killing functions of neutrophil defensins in lung explants
Bibliographic record
Abstract
Studies of the antimicrobial activity of neutrophil defensins have mostly been carried out in microbiological media, and their effects on the host defense in physiological conditions are unclear. We examined 1) the antibacterial activity of defensins in physiological media with and without lung tissue present, 2) the effect of defensins on hydrogen peroxide (H(2)O(2)) production by lung tissue that had been exposed to bacteria, and 3) the effect of diphenyleneiodonium (DPI), an inhibitor of reactive oxygen species formation, on the antibacterial activity of defensins in the presence of lung tissue. Defensins were incubated with Escherichia coli or Pseudomonas aeruginosa in the absence or presence of primary cultured mouse lung explants. Defensins reduced bacterial counts by approximately 65-fold and approximately 25-fold, respectively, at 48 h; bacterial counts were further decreased by approximately 600-fold and approximately 12,000-fold, respectively, in the presence of lung tissue. Defensins induced H(2)O(2) production by lung tissue, and the rate of killing of E. coli by defensins was reduced by approximately 2,500-fold in the presence of 10 microM DPI. We conclude that defensins exert a significant antimicrobial effect under physiological conditions and that this effect is enhanced in the presence of lung tissue by a mechanism that involves the production of reactive oxygen species.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".