Determination of ginsenoside Rh3 in rat plasma by LC–MS/MS and its application to a pharmacokinetic study
Bibliographic record
Abstract
Abstract Ginsenoside Rh3 (GRh3) is a bacterial metabolite of ginsenoside Rg5, which is the main component of hot‐processed ginseng. A simple, efficient and sensitive method was developed and validated for the determination of GRh3 in rat plasma by LC–tandem mass spectrometry. After protein precipitation with methanol/acetonitrile (1:1, vol/vol) using propranolol as the internal standard, the target analytes were separated on an XDB C 18 column, with methanol containing 0.1% formic acid and water containing 0.1% formic acid used as mobile phases for gradient elution. Mass spectrometry was performed in electrospray ion source–positive ion mode and multiple reaction monitoring mode, monitoring the transitions m/z 622.5 → 425.5 and m/z 260.1 → 116.1 for GRh3 and internal standard, respectively. The concentration range of GRh3 was 20–20,000 ng/mL and the correlation coefficient (r 2 ) was greater than 0.99. The accuracy error and relative standard deviation were below 15%. The extraction recovery and matrix effect were 74.2% to 78.7% and 96.9% to 108.4%, respectively. Under different conditions, GRh3 was stable in the range of 1.8%–8.7%. This method has been successfully applied to study the pharmacokinetics of GRh3 with an oral dose of 10.0 mg/kg and an intravenous dose of 2.0 mg/kg in rats, respectively. The absolute bioavailability of GRh3 was 37.6%.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| 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".