Antimicrobial Peptide Therapeutics for Cystic Fibrosis
Bibliographic record
Abstract
Greater than 90% of lung infections in cystic fibrosis (CF) patients are caused by Pseudomonas aeruginosa, and the majority of these patients subsequently die from lung damage. Current therapies are either targeted at reducing obstruction, reducing inflammation, or reducing infection. To identify potential therapeutic agents for the CF lung, 150 antimicrobial peptides consisting of three distinct structural classes were screened against mucoid and multidrug-resistant clinical isolates of P. aeruginosa, Stenotrophomonas maltophilia, Achromobacter xylosoxidans, and Staphylococcus aureus. Five peptides that retained potent antimicrobial activities in physiological salt and divalent cation environment were further characterized in vivo using a rat chronic lung infection model. All animals were inoculated intratracheally with 10(4) P. aeruginosa mucoid PAO1 cells in agar beads. Three days following inoculation treatment was initiated. Animals were treated daily for 3 days with 100 microl of peptide solution (1 mg/ml) in 10 mM sodium citrate, which was deposited via either intratracheal instillation or aerosolization. Control animals received daily exposure to vehicle alone. At the end of the treatment the lungs of the animals were removed for quantitative culture. Four peptides, HBCM2, HBCM3, HBCPalpha-2, and HB71, demonstrated significant reduction in Pseudomonas bioburden in the lung of rats. Further in vivo studies provided direct evidence that anti-inflammatory activity was associated with three of these peptides. Therefore, small bioactive peptides have the potential to attack two of the components responsible for the progression of lung damage in the CF disease: infection and inflammation.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".