Synthetic host defense peptide IDR-1002 reduces inflammation in Pseudomonas aeruginosa lung infection
Bibliographic record
Abstract
Pseudomonas aeruginosa is a frequent cause of lung infections, particularly in chronic infections in cystic fibrosis patients. However, treatment is challenging due to P. aeruginosa evasion of the host immune system and the rise of antibiotic resistant strains. Host defense peptides (HDPs) and synthetic derivatives called innate defense regulators (IDRs) have shown promise in several infection models as an alternative to antibiotic treatment. Here we tested peptide IDR-1002 against P. aeruginosa in vitro and in vivo. Treatment of bronchial epithelial cells and macrophages with IDR-1002 or in combination with live P. aeruginosa or its LPS led to the reduction of agonist-induced cytokines and chemokines and limited cell killing by live P. aeruginosa. In an in vivo model using P. aeruginosa combined with alginate to mimic a chronic model, IDR-1002 did not reduce the bacterial burden in the lungs, but IDR-1002 mice showed a significant decrease in IL-6 in the lungs and in gross pathology of infection, while histology revealed that IDR-1002 treated mice had reduced alveolar macrophage infiltration around the site of infection and reduced inflammation. Overall, these results indicate that IDR-1002 has promise for combating P. aeruginosa lung infections and their resulting 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 | 0.001 |
| 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".