Ginsenoside variation and phytochemistry of Ontario-grown North American ginseng (Panax quinquefolius): Assessing land race diversity and biological activities
Bibliographic record
Abstract
North American ginseng (Panax quinquefolius L.) is a valuable and widely used medicinal plant and Ontario has become the largest grower of ginseng in North America. Ginseng has been grown in Ontario for over 50 years and has reached the land race stage. The phytochemistry of Ontario ginseng land races was characterized using high performance liquid chromatography (HPLC) coupled with a diode array detector (DAD), evaporative light scattering detector (ELSD), or mass spectrometry (MS) to assess ginsenoside and monosaccharide content. Nuclear magnetic resonance (NMR) was used successfully as a metabolomic tool to distinguish Ontario ginseng land races and ginseng species. Ginsenoside variation was high within and among Ontario ginseng land races and variation in ginsenoside content was correlated positively to the level of inhibition of the drug metabolizing cytochrome P450 enzyme, CYP3A4. Along with the assessment of ginsenosides, LC/MS/MS and LC/ELSD methods were developed to characterize malonyl ginsenosides and monosaccharide components respectively in Ontario ginseng. Malonyl ginsenosides accounted for a significant percentage of total ginsenoside content and glucose was found to be the major monosaccharide component. Although chromatographic ginsenoside analysis did not differentiate land races, 1H NMR was successfully applied to distinguish ginseng species and two of five land races. The results from this study contribute to the validation and characterization of Ontario ginseng and add to the value of this important medicinal crop.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".