Garlic natural health products exhibit variable constituent levels and antimicrobial activity againstNeisseria gonorrhoeae, Staphylococcus aureus andEnterococcus faecalis
Bibliographic record
Abstract
The composition of 19 garlic natural health products (NHPs) and fresh garlic extracts were determined, as was their antibacterial activity. The 19 NHPs and 5 fresh garlic extract standards were analysed for their principal active constituents. They were also extracted for 5, 10 or 15 min in water to fresh garlic equivalents of 200 mg/mL. The extract's minimum inhibitory concentrations (MICs) and minimum bactericidal concentrations (MBCs) against three indicator microorganisms (Neisseria gonorrhoeae, Staphylococcus aureus and Enterococcus faecalis) were determined by the broth microdilution method. While 47% of the aqueous garlic NHP extracts exhibited activity against N. gonorrhoeae, only 16% of the aqueous extracts inhibited S. aureus or E. faecalis at all three timepoints. Generally, products with high antimicrobial activity contained higher levels of garlic constituents with comparable activity to fresh garlic extracts, while products with marginal antibacterial activity often contained lower concentrations of constituents than their product labels indicated. Different extraction times affected antibacterial activity only against N. gonorrhoeae and tended to be correlated with levels of allicin. Thus, many extracts showed discrepancies in both composition, allicin:alliin ratio and antimicrobial activity, raising concerns as to standards of preparation and quality control for these products.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".