Toxicology of a Peruvian botanical remedy to support healthy liver function
Bibliographic record
Abstract
BACKGROUND: The purpose of these studies was to determine the safety of a botanical treatment for supporting healthy liver function developed in Peru. The formulation, A4+, contains extracts of Curcuma longa L. rhizome (A4R), Cordia lutea Lam. flower (A4F) and Annona muricata L. leaf (A4L). The tests were used to support an application for a non-traditional Natural Health Product Licence from the Natural Health Product Directorate of Health Canada and future clinical trials. METHODS: Besides reviewing the scientific and clinical information from Peru on the ingredients and conducting an initial Ames test for mutagenicity, we analysed A4+ for its chemical profile and tested genotoxicity (micronucleus test) and general toxicity (28-day repeated dose). RESULTS: A4+ and extracts from the three plants provided distinctive chemical fingerprints. A4L contained acetogenins, requiring a second chromatographic method to produce a specific fingerprint. The Ames test proved positive at the highest concentration (5,000 μg/mL) but A4+ showed no evidence of genotoxicity in the more specific mouse micronucleus test. The 28-day repeated dose (general toxicity) study in rats showed no toxicity at 2,000 mg/kg. CONCLUSIONS: We conclude that under the conditions of these studies, A4+ shows no evidence of toxicity at the levels indicated. A no observed adverse effect level (NOAEL) of 2,000 mg/kg was assigned.
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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 | 0.000 | 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) | 0.003 | 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".