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Record W2157500996 · doi:10.3389/fmicb.2012.00242

Powerful bacterial killing by buckwheat honeys is concentration-dependent, involves complete DNA degradation and requires hydrogen peroxide

2012· article· en· W2157500996 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueFrontiers in Microbiology · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBee Products Chemical Analysis
Canadian institutionsBrock University
FundersOntario Centres of Excellence
KeywordsHydrogen peroxideDegradation (telecommunications)DNAChemistryMicrobiologyDNA damageFood scienceBiochemistryBiology

Abstract

fetched live from OpenAlex

Exposure of bacterial cells to honey inhibits their growth and may cause cell death. Our previous studies showed a cause-effect relationship between hydroxyl radical generated from honey hydrogen peroxide and growth arrest. Here we explored the role of hydroxyl radicals as inducers of bacterial cells death. The bactericidal effect of ·OH on antibiotic-resistant clinical isolates of MRSA and VRE and standard bacterial strains of E. coli and B. subtiles was examined using a broth microdilution assay supplemented with 3'-(p-aminophenyl) fluorescein (APF) as the ·OH trap, followed by colony enumeration. Bactericidal activities of eight honeys (six varieties of buckwheat, blueberry and manuka honeys) were analyzed. The MBC/MIC ratio ≤4 and the killing curves indicated that honeys exhibited powerful, concentration-dependent bactericidal effect. The extent of killing depended on the ratio of honey concentration to bacterial load, indicating that honey dose was critical for its bactericidal efficacy. The killing rate and potency varied between honeys and ranged from over a 6-log(10) to 4-log(10) CFU/ml reduction of viable cells, equivalent to complete bacterial eradication. The maximal killing was associated with the extensive degradation of bacterial DNA. Honey concentration at which DNA degradation occurred correlated with cell death observed in the concentration-dependent cell-kill on agar plates. There was no quantitative relationship between the ·OH generation by honey and bactericidal effect. At the MBC, where there was no surviving cells and no DNA was visible on agarose gels, the ·OH levels were on average 2-3x lower than at Minimum Inhibitory Concentration (MICs) (p < 0.0001). Pre-treatment of honey with catalase, abolished the bactericidal effect. This raised possibilities that either the abrupt killing prevented accumulation of ·OH (dead cells did not generate ·OH) or that DNA degradation and killing is the actual footprint of ·OH action. In conclusion, honeys of buckwheat origin exhibited powerful, concentration-dependent bactericidal effect. The killing and DNA degradation showed a cause-effect relationship. Hydrogen peroxide was an active part of honey killing mechanism.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.080
Threshold uncertainty score0.363

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.196
Teacher spread0.183 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it