The Ultrashort Peptide OW: A New Antibiotic Adjuvant
Bibliographic record
Abstract
BACKGROUND: The over use of current antibiotics and low discovery rate of the new ones are leading to rapid development of multidrug-resistant pathogens worldwide. Antimicrobial peptides have shown promising results against multidrug-resistant bacteria. OBJECTIVE: To investigate the antimicrobial activity of a new ultrashort hexapeptide (OW). METHODS: The OW hexapeptide was designed and tested against different strains of bacteria with different levels of sensitivity. Bacterial susceptibility assays were performed according to the guidelines of the Clinical and Laboratory Institute (CLSI). The synergistic studies were then conducted using the Checkerboard assay. This was followed by checking the hemolytic effect of the hexapeptide against human blood cells and Human Embryonic Kidney cell line (HEK293). Finally, the antibiofilm activities of the hexapeptide were studied using the Biofilm Calgary method. RESULTS: Synergistic assays showed that OW has synergistic effects with antibiotics of different mechanisms of action. It showed an outstanding synergism with Rifampicin against methicillin resistant Staphylococcus aureus; ΣFIC value was 0.37, and the MIC value of Rifampicin was decreased by 85%. OW peptide also displayed an excellent synergism with Ampicillin against multidrug-resistant Pseudomonas aeruginosa, with ΣFIC value of less than 0.38 and a reduction of more than 96% in the MIC value of Ampicillin. CONCLUSION: This study introduced a new ultrashort peptide (OW) with promising antimicrobial potential in the management of drug-resistant infectious diseases as a single agent or in combination with commonly used antibiotics. Further studies are needed to investigate the exact mechanism of action of these peptides.
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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".