Antimicrobial and Biophysical Properties of Surfactant Supplemented with an Antimicrobial Peptide for Treatment of Bacterial Pneumonia
Bibliographic record
Abstract
Antibiotic-resistant bacterial infections represent an emerging health concern in clinical settings, and a lack of novel developments in the pharmaceutical pipeline is creating a "perfect storm" for multidrug-resistant bacterial infections. Antimicrobial peptides (AMPs) have been suggested as future therapeutics for these drug-resistant bacteria, since they have potent broad-spectrum activity, with little development of resistance. Due to the unique structure of the lung, bacterial pneumonia has the additional problem of delivering antimicrobials to the site of infection. One potential solution is coadministration of AMPs with exogenous surfactant, allowing for distribution of the peptides to distal airways and opening of collapsed lung regions. The objective of this study was to test various surfactant-AMP mixtures with regard to maintaining pulmonary surfactant biophysical properties and bactericidal functions. We compared the properties of four AMPs (CATH-1, CATH-2, CRAMP, and LL-37) suspended in bovine lipid-extract surfactant (BLES) by assessing surfactant-AMP mixture biophysical and antimicrobial functions. Antimicrobial activity was tested against methillicin-resistant Staphylococcus aureus and Pseudomonas aeruginosa. All AMP/surfactant mixtures exhibited an increase of spreading compared to a BLES control. BLES+CATH-2 mixtures had no significantly different minimum surface tension versus the BLES control. Compared to the other cathelicidins, CATH-2 retained the most bactericidal activity in the presence of BLES. The BLES+CATH-2 mixture appears to be an optimal surfactant-AMP mixture based on in vitro assays. Future directions involve investigating the potential of this mixture in animal models of bacterial pneumonia.
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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".