Effectiveness of honey on <i>Staphylococcus aureus</i> and <i>Pseudomonas aeruginosa</i> biofilms
Bibliographic record
Abstract
OBJECTIVES: Biofilms formed by Pseudomonas aeruginosa (PA) and Staphylococcus aureus (SA) have been shown to be an important factor in the pathophysiology of chronic rhinosinusitis (CRS). As well, honey has been used as an effective topical antimicrobial agent for years. Our objective is to determine the in vitro effect of honey against biofilms produced by PA and SA. STUDY DESIGN: In vitro testing of honey against bacterial biofilms. METHODS: We used a previously established biofilm model to assess antibacterial activity of honey against 11 methicillin-susceptible SA (MSSA), 11 methicillin-resistant SA (MRSA), and 11 PA isolates. Honeys were tested against both planktonic and biofilm-grown bacteria. RESULTS: Honey was effective in killing 100 percent of the isolates in the planktonic form. The bactericidal rates for the Sidr and Manuka honeys against MSSA, MRSA, and PA biofilms were 63-82 percent, 73-63 percent, and 91-91 percent, respectively. These rates were significantly higher (P<0.001) than those seen with single antibiotics commonly used against SA. CONCLUSION: Honey, which is a natural, nontoxic, and inexpensive product, is effective in killing SA and PA bacterial biofilms. This intriguing observation may have important clinical implications and could lead to a new approach for treating refractory CRS.
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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 | 0.001 |
| 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".