Innate defence regulator peptide 1018 protects mice in the <i>Pseudomonas aeruginosa</i> acute lung infection model (P4045)
Bibliographic record
Abstract
Abstract Host defence peptides (HDPs) are small peptides with antibacterial and immunomodulatory properties that represent a novel approach to anti-infective therapy. Innate defence regulator (IDR) peptides, such as IDR-1018, are synthetic variants of HDPs that are more potent and less toxic than their naturally-occurring counterparts. IDR-1018 kills bacteria, exhibits potent chemokine activities, and decreases LPS-induced TNF-α release. The goal of the current study was to determine whether IDR-1018 protects mice in the Pseudomonas aeruginosa acute lung infection model. To this end, CD1 mice were given IDR-1018 (4 mg/kg) or the saline control intravenously 4 h prior to infection. Mice were infected intranasally with P. aeruginosa (1 x 10^6). Mice were sacrificed 18 h post infection, and blood and bronchoalveolar lavage fluid (BALF) was collected. P. aeruginosa CFUs and leukocyte counts in the BALF were determined. Cytokine concentrations (TNF-α, MCP-1, KC, and IL-6) in the blood and BALF were quantified by ELISA. Our data showed that IDR-1018-treated mice had fewer CFUs and neutrophils and more monocytes in the BALF, and lower concentrations of proinflammatory cytokines in the BALF and in the blood than did saline-treated mice. These results suggest that IDR-1018 may have potential for the treatment of acute lung infections. Future studies aim to optimize the delivery strategy by identifying a delivery vehicle that enhances the half life and ease of peptide delivery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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".