Anti-Biofilm and Immunomodulatory Activities of Peptides That Inhibit Biofilms Formed by Pathogens Isolated from Cystic Fibrosis Patients
Bibliographic record
Abstract
Cystic fibrosis (CF) patients often acquire chronic respiratory tract infections due to Pseudomonas aeruginosa and Burkholderia cepacia complex (Bcc) species. In the CF lung, these bacteria grow as multicellular aggregates termed biofilms. Biofilms demonstrate increased (adaptive) resistance to conventional antibiotics, and there are currently no available biofilm-specific therapies. Using plastic adherent, hydroxyapatite and flow cell biofilm models coupled with confocal and scanning electron microscopy, it was demonstrated that an anti-biofilm peptide 1018 prevented biofilm formation, eradicated mature biofilms and killed biofilms formed by a wide range of P. aeruginosa and B. cenocepacia clinical isolates. New peptide derivatives were designed that, compared to their parent peptide 1018, showed similar or decreased anti-biofilm activity against P. aeruginosa biofilms, but increased activity against biofilms formed by the Gram-positive bacterium methicillin resistant Staphylococcus aureus. In addition, some of these new peptide derivatives retained the immunomodulatory activity of 1018 since they induced the production of the chemokine monocyte chemotactic protein-1 (MCP-1) and suppressed lipopolysaccharide-mediated tumor necrosis factor-α (TNF-α) production by human peripheral blood mononuclear cells (PBMC) and were non-toxic towards these cells. Peptide 1018 and its derivatives provide promising leads for the treatment of chronic biofilm infections and hyperinflammatory lung disease in CF patients.
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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".