Technical Brief 19: Archeological Collections and the Public: UsingResources for the Public Benefit
Bibliographic record
Abstract
Biofilm-related infections account for at least 65% of all human infections, but there are no available antimicrobials that specifically target biofilms. Their elimination by available treatments is inefficient since biofilm cells are between 10- and 1,000-fold more resistant to conventional antibiotics than planktonic cells. Here we describe the synergistic interactions, with different classes of antibiotics, of a recently characterized antibiofilm peptide, 1018, to potently prevent and eradicate bacterial biofilms formed by multidrug-resistant ESKAPE (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter species) pathogens. Combinations of peptide 1018 and the antibiotic ceftazidime, ciprofloxacin, imipenem, or tobramycin were synergistic in 50% of assessments and decreased by 2- to 64-fold the concentration of antibiotic required to treat biofilms formed by Pseudomonas aeruginosa, Escherichia coli, Acinetobacter baumannii, Klebsiella pneumoniae, Salmonella enterica, and methicillin-resistant Staphylococcus aureus. Furthermore, in flow cell biofilm studies, combinations of low, subinhibitory levels of the peptide (0.8 μg/ml) and ciprofloxacin (40 ng/ml) decreased dispersal and triggered cell death in mature P. aeruginosa biofilms. In addition, short-term treatments with the peptide in combination with ciprofloxacin prevented biofilm formation and reduced P. aeruginosa PA14 preexisting biofilms. PCR studies indicated that the peptide suppressed the expression of various antibiotic targets in biofilm cells. Thus, treatment with the peptide represents a novel strategy to potentiate antibiotic activity against biofilms formed by multidrug-resistant pathogens.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".