The presence of honey triggers inhibition of pseudomonas aeruginosa virulence and metabolism
Bibliographic record
Abstract
Honey is an ancient remedy with a high potency against drug-resistant bacteria, which has gained renewed interest in naturopathic medicine for its beneficial effect in treatment and prevention of wound infections. The exact antibacterial property and mode of action of honey is still unknown, however in recent years, there has been various studies focusing on the effect of honey on bacterial gene regulation. With the focus of current literature being at the molecular level, the first aim of this study was to examine the effect of honey at the cell level and its influence on the metabolism of P. aeruginosa biofilm. The second objective of this study was to test the influence of the combination of the iron chelating agent (EDTA) and honey on biofilm metabolism. P. aeruginosa metabolism in this study was analyzed through (i) siderophore excretion and (ii) monitoring of the biofilm CO2 respiration rate with a Carbon dioxide Evolution Measurement System (CEMS). The results obtained indicate that honey reduces biofilm metabolism and inhibits siderophore production, while the combination of honey and EDTA has a greater impact on biofilm metabolism, which influences P. aeruginosa iron homeostasis, inhibits siderophore production, and increases bacterial recovery time after exposure. However, when provided at concentrations lower than its inhibitory concentration, honey is used as a nutrient source for biofilm development. The results obtained illustrated the importance of the environmental conditions on biofilm metabolism, as the biofilm response varied with minor changes in the composition of their media. In summary, this study showed that biofilm cells shut down their metabolism in the presence of honey, which also inhibits bacterial siderophore production and can play an importance role on the virulence of P. aeruginosa.
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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 | 0.001 |
| 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".