Ginseng Metabolism Study using Hybrid Quadrupole Linear Ion Trap (QqLIT)
Bibliographic record
Abstract
Herbal medicines are gaining popularity all over the world. Ginseng is one of the most commonly used herbal remedies. Ginsenosides are the major active ingredients in ginseng and have shown pharmacological benefits to human health because they appear to affect multiple pathways. The work presented here demonstrates the ability of the hybrid Quadrupole linear ion trap (QqLIT) mass spectrometer; QTRAP 5500 ® and LightSight ® software for the rapid identification of metabolites using positive and negative ion detection. Adult male Zucker rats were dosed daily with Ontario-grown North American ginseng by oral gavage for 42 days to evaluate the effect on vascular and reproductive health. For the present study, plasma was obtained at the time of sacrifice. Ginsenosides can be ionized both in positive and negative polarities; however, some ginsenosides are better detected as positive ions. Also, when using positive ionization, the ginsenosides and their metabolites are mainly detected as the sodium adduct, which makes structural elucidation difficult. Therefore, the samples should be analyzed using both polarities. A LC-MS method with polarity switching allows for all the information about metabolites to be collected from a single injection. A total of 6 ginsenosides (Rg1, Re, Rb1, Rc, Rb2 and Rd) account for over 90% of the ginsenosides content in ginseng root which reduces the complexity of the analysis. Some of the mechanisms identified and characterized include oxidation, glucuronidation and loss of glucose moieties. The software allows for detection and structural elucidation using both the positive ion and negative ion data.
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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 | 0.001 |
| 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) | 0.002 | 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".