Re-Examining the Role of Hydrogen Peroxide in Bacteriostatic and Bactericidal Activities of Honey
Bibliographic record
Abstract
The aim of this study was to critically analyze the effects of hydrogen peroxide on growth and survival of bacterial cells in order to prove or disprove its purported role as a main component responsible for the antibacterial activity of honey. Using the sensitive peroxide/peroxidase assay, broth microdilution assay and DNA degradation assays, the quantitative relationships between the content of H(2)O(2) and honey's antibacterial activity was established(.) The results showed that: (A) the average H(2)O(2) content in honey was over 900-fold lower than that observed in disinfectants that kills bacteria on contact. (B) A supplementation of bacterial cultures with H(2)O(2) inhibited E. coli and B. subtilis growth in a concentration-dependent manner, with minimal inhibitory concentrations (MIC(90)) values of 1.25 mM/10(7) cfu/ml and 2.5 mM/10(7) cfu/ml for E. coli and B. subtilis, respectively. In contrast, the MIC(90) of honey against E. coli correlated with honey H(2)O(2) content of 2.5 mM, and growth inhibition of B. subtilis by honey did not correlate with honey H(2)O(2) levels at all. (C) A supplementation of bacterial cultures with H(2)O(2) caused a concentration-dependent degradation of bacterial DNA, with the minimum DNA degrading concentration occurring at 2.5 mM H(2)O(2). DNA degradation by honey occurred at lower than ≤2.5 mM concentration of honey H(2)O(2) suggested an enhancing effect of other honey components. (D) Honeys with low H(2)O(2) content were unable to cleave DNA but the addition of H(2)O(2) restored this activity. The DNase-like activity was heat-resistant but catalase-sensitive indicating that H(2)O(2) participated in the oxidative DNA damage. We concluded that the honey H(2)O(2) was involved in oxidative damage causing bacterial growth inhibition and DNA degradation, but these effects were modulated by other honey components.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".