Manuka honey as a non-antibiotic alternative against Staphylococcus spp. and their small colony variant (SCVs) phenotypes
Bibliographic record
Abstract
The antibiotic resistance (ABR) crisis is an urgent global health priority. Staphylococci are among the problematic bacteria contributing to this emergency owing to their recalcitrance to many clinically important antibiotics. Staphylococcal pathogenesis is further complicated by the presence of small colony variants (SCVs), a bacterial subpopulation displaying atypical characteristics including retarded growth, prolific biofilm formation, heightened antibiotic tolerance, and enhanced intracellular persistence. These capabilities severely impede current chemotherapeutics, resulting in chronic infections, poor patient outcomes, and significant economic burden. Tackling ABR requires alternative measures beyond the conventional options that have dominated treatment regimens over the past 8 decades. Non-antibiotic therapies are gaining interest in this arena, including the use of honey, which despite having ancient therapeutic roots has now been reimagined as an alternative treatment beyond just traditional topical use, to include the treatment of an array of difficult-to-treat staphylococcal infections. This literature review focused on Manuka honey (MH) and its efficacy as an anti-staphylococcal treatment. We summarized the studies that have used this product and the technologies employed to study the antibacterial mechanisms that render MH a suitable agent for the management of problematic staphylococcal infections, including those involving staphylococcal SCVs. We also discussed the status of staphylococcal resistance development to MH and other factors that may impact its efficacy as an alternative therapy to help combat 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 | 0.000 | 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) | 0.000 | 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".