Combinatorial efficacy of Manuka honey and antibiotics in the in vitro control of staphylococci and their small colony variants
Bibliographic record
Abstract
Introduction Staphylococci are among the list of problematic bacteria contributing to the global antibiotic resistance (ABR) crisis. Their ability to adopt the small colony variant (SCV) phenotype, induced by prolonged antibiotic chemotherapy, complicates staphylococcal infection control options. Novel and alternative approaches are needed to tackle staphylococcal infections and curb ABR. Manuka honey (MH), a non-antibiotic alternative is recognized for its unique antibacterial activity based on its methylglyoxal (MGO) component. Methods In this study, MH (MGO 830+) was tested in combination with gentamicin (GEN), rifampicin (RIF), or vancomycin (VA) against staphylococcal wildtype (WT) and SCVs. To our knowledge, there are no current studies in the literature documenting the effects of MH on staphylococcal SCVs. While Staphylococcus aureus is well-studied for its international ABR burden, limited data exists demonstrating the effects of MH on S. epidermidis and S. lugdunensis whose pathogenic relevance and contribution to ABR is also rising. Results & discussion The three staphylococci were most susceptible to RIF (0.06-0.24 μg/ml), then GEN (0.12-0.49 μg/ml), and lastly VA (0.49-0.96 μg/ml). The MICs of MH were 7%, 7-8%, and 6-7% (w/v), respectively. Fractional inhibitory concentration (FIC) evaluations showed that the combined MH + antibiotic effect was either additive (FICI 1-2), or partially synergistic (FICI >0.5-1). While all three antibiotics induced SCVs in vitro , stable SCVs were observed in GEN treatments only. The addition of MH to these GEN-SCV-induction analyses resulted in complete suppression of SCVs (p<0.001) in all three staphylococci, suggesting that MH’s antibacterial properties interfered with GEN’s SCV induction mechanisms. Moreover, the addition of MH to growth cultures of recovered stable SCVs resulted in the inhibition of SCV growth by at least 99%, indicating MH’s ability to prevent subsequent SCV growth. These in vitro analyses demonstrated MH’s broad-spectrum capabilities not only in improving WT staphylococci susceptibility to the three antibiotics, but also mitigated the development and subsequent growth of their SCV phenotypes. MH in combination with antibiotics has the potential to not only resensitize staphylococci to antibiotics and consequently require less antibiotic usage, but in instances where prolonged chemotherapy is employed, the development and growth of SCVs would be hampered, providing a better clinical outcome, all of which mitigate ABR.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".