Methylglyoxal: (active agent of manuka honey) in vitro activity against bacterial biofilms
Bibliographic record
Abstract
BACKGROUND: Pseudomonas aeruginosa (PA) and Staphylococcus aureus (SA) biofilms are associated with poor chronic rhinosinusitis (CRS) disease control following surgery. Manuka honey (MH) has been shown to be both an effective in vitro treatment agent for SA and PA biofilms and nontoxic to sinonasal respiratory mucosa. Methylglyoxal (MGO) has been reported to be the major antibacterial agent in MH. The effect of this agent against SA and PA biofilms has yet to be reported. Our objective was to determine the in vitro effect of MGO against biofilms of SA and PA, via in vitro testing of MGO against bacterial biofilms. METHODS: An established biofilm model was used to determine the effective concentration (EC) of MGO against 10 isolates of methicillin-resistant SA (MRSA) and PA. The EC of MGO was also determined against planktonic (free-swimming) MRSA and PA. RESULTS: For MRSA, the EC against planktonic organisms was a concentration of 0.08 mg/mL to 0.3 mg/mL whereas against the biofilm MRSA isolates, the EC ranged from 0.5 mg/mL to 3.6 mg/mL. For PA, the EC against planktonic organisms was a concentration of 0.15 mg/mL to 1.2 mg/mL for planktonic organisms whereas against the biofilm PA isolates, the EC ranged from 1.8 mg/mL to 7.3 mg/mL. CONCLUSION: MGO, a component of MH, is an effective antimicrobial agent against both planktonic and biofilm MRSA and PA organisms in vitro.
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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) | 0.001 | 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".