<p>The evaluation of the synergistic antimicrobial and antibiofilm activity of AamAP1-Lysine with conventional antibiotics against representative resistant strains of both Gram-positive and Gram-negative bacteria</p>
Bibliographic record
Abstract
Background and purpose: Antimicrobial resistance toward antibiotics is reaching historical unprecedented levels. There is an urgent and imminent need to develop novel antimicrobial alternatives. Antimicrobial peptides could prove to be a successful group of antimicrobials for drug development. Recently, we have designed a novel synthetic peptide named AamAP1-Lysine. The peptide displayed potent wide-spectrum antimicrobial activities against Gram-positive and Gram-negative bacteria. The purpose of this study is to evaluate the antimicrobial effect of combining AamAP1-Lysine with five different conventional antibiotics each representing a distinct mechanism of action in order to explore the possibility of producing a synergistic mode of action against a resistant strain of Gram-positive and a resistant strain of Gram-negative bacteria. Methodology: The antimicrobial activity of AamAP1-Lysine in combination with five different antibiotics were evaluated for their antimicrobial activity employing standard antimicrobial assays, the synergistic activity of the peptide-antibiotic combinations were evaluated using checkerboard technique in addition to real-time time-kill assays. For the antibiofilm studies, the MBEC values were determined by employing the Calgary device. Results: The combination strategy displayed potent synergistic activities against planktonic bacteria in a significant number of peptide-antibiotic combinations. The synergistic activity managed to reduce the effective minimum inhibitory concentration (MIC) concentrations dramatically with some combinations exhibiting a 64-fold decrease in the effective MIC of AamAP1-Lysine individually. Additionally, the combined synergistic activities of the peptide antibiotics were evaluated, and our results have identified two peptide antibiotic combinations with potent synergistic activities against biofilm growing strains of resistant bacteria. Conclusion: Our results clearly indicate that peptide-antibiotic combinations could prove to be a very effective strategy in combatting multidrug-resistant bacteria and biofilm caused infections. Keywords: antimicrobial peptides, AamAP1-Lysine, bacterial resistance, antibiofilm, synergy
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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".