Honey microbiota, methods for determining the microbiological composition and the antimicrobial effect of honey – A review
Bibliographic record
Abstract
Honey is a natural product used since ancient times due to its taste, aroma, and therapeutic properties (antibacterial, antiviral, anti-inflammatory, and antioxidant activity). The purpose of this review is to present the species of microorganisms that can survive in honey and the effect they can have on bees and consumers. The techniques for identifying the microorganisms present in honey are also described in this study. Honey contains bacteria, yeasts, molds, and viruses, and some of them may present beneficial properties for humans. The antimicrobial effect of honey is due to its acidity and high viscosity, high sugar concentration, low water content, the presence of hydrogen peroxide and non-peroxidase components, particularly methylglyoxal (MGO), phenolic acids, flavonoids, proteins, peptides, and non-peroxidase glycopeptides. Honey has antibacterial action (it has effectiveness against bacteria, e.g. Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus, and Acinetobacter, etc.), antifungal (effectiveness against Candida spp., Aspergillus spp., Fusarium spp., Rhizopus spp., and Penicillium spp.), antiviral (effectiveness against SARS-CoV-2, Herpes simplex virus type 1, Influenza virus A and B, Varicella zoster virus), and antiparasitic action (effectiveness against Plasmodium berghei, Giardia and Trichomonas, Toxoplasma gondii) demonstrated by numerous studies that are comprised and discussed in this review.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".