Killing of Pseudomonas aeruginosa by Chicken Cathelicidin-2 Is Immunogenically Silent, Preventing Lung Inflammation <i>In Vivo</i>
Bibliographic record
Abstract
ABSTRACT The development of antibiotic resistance by Pseudomonas aeruginosa is a major concern in the treatment of bacterial pneumonia. In the search for novel anti-infective therapies, the chicken-derived peptide cathelicidin-2 (CATH-2) has emerged as a potential candidate, with strong broad-spectrum antimicrobial activity and the ability to limit inflammation by inhibiting Toll-like receptor 2 (TLR2) and TLR4 activation. However, as it is unknown how CATH-2 affects inflammation in vivo , we investigated how CATH-2-mediated killing of P. aeruginosa affects lung inflammation in a murine model. First, murine macrophages were used to determine whether CATH-2-mediated killing of P. aeruginosa reduced proinflammatory cytokine production in vitro . Next, a murine lung model was used to analyze how CATH-2-mediated killing of P. aeruginosa affects neutrophil and macrophage recruitment as well as cytokine/chemokine production in the lung. Our results show that CATH-2 kills P. aeruginosa in an immunogenically silent manner both in vitro and in vivo . Treatment with CATH-2-killed P. aeruginosa showed reduced neutrophil recruitment to the lung as well as inhibition of cytokine and chemokine production, compared to treatment with heat- or gentamicin-killed bacteria. Together, these results show the potential for CATH-2 as a dual-activity antibiotic in bacterial pneumonia, which can both kill P. aeruginosa and prevent excessive inflammation.
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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) | 0.001 | 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".