Antibiotic Resistance in <i>Staphylococcus aureus</i> : Effects of Quorum Sensing Inhibition and DNA Fragmentation
Bibliographic record
Abstract
Abstract Background Antimicrobial resistance (AMR) is a global crisis, causing 2.8 million infections and 35,000 deaths annually. Staphylococcus aureus is mainly responsible for causing these challenging infections through biofilm formation and the action of efflux pumps. A limited number of studies on Hop (Humulus lupulus) have shown its potential to inhibit quorum sensing in pathogenic bacteria. Objective Therefore, a novel treatment approach was used in this study, which investigated Hop’s β-acids, particularly the combination of colupulone and n+adlupulone, as well as in combination with fluoroquinolone antibiotics ciprofloxacin and ofloxacin. As ciprofloxacin remains a highly effective antibiotic against Staphylococcus aureus but resistance can develop, and ofloxacin exhibits naturally higher resistance in S. aureus, this study hypothesized that combining Hop (containing colupulone & n+adlupulone) with the two antibiotics separately would result in a greater reduction in biofilm growth of S. aureus compared to their individual potency levels. Methods Antimicrobial activity was assessed using disk diffusion assays and minimum inhibitory concentration for biofilms at multiple concentrations through 2-fold serial dilutions. Results Our data demonstrate that Hop-derived β-acids possess direct antimicrobial activity and when combined with the fluoroquinolone antibiotics, exhibit additive or synergistic effects by acting on different targets in Staphylococcus aureus. Conclusions This study provides insight into how natural products can potentially mitigate the development of resistance to antibiotics like ciprofloxacin in the highly pathogenic bacterium S. aureus. It also highlights how adding natural compounds could improve drug effectiveness. Therefore, this demonstrates the potential of natural compounds and antibiotics like ofloxacin, which are known to be ineffective against S. aureus. It offers a promising natural-conventional hybrid approach to addressing antimicrobial resistance.
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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".