Simultaneous Determination of Oleanolic Acid, p-Coumaric Acid, Ferulic Acid, Kaemperol and Quercetin in Rat Plasma by LC–MS-MS and Application to a Pharmacokinetic Study of Oldenlandia diffusa Extract in Rats
Bibliographic record
Abstract
A simple, rapid and sensitive liquid chromatography tandem mass spectrometry method is presented for the simultaneous determination of oleanolic acid, p-coumaric acid, ferulic acid, kaemperol and quercetin in rat plasma. Glycyrrhetinic acid was used as an internal standard, and sample pretreatment consisted of a liquid-liquid extraction. Chromatographic separation was achieved on a Gemini 110A C18 column (50 × 2.0 mm i.d., 5 µm) by gradient elution with a mobile phase consisting of methanol, acetonitrile and 0.01% formic acid in water. Tandem mass spectrometric detection was conducted using multiple reaction monitoring under negative ionization mode. Calibration curves offered linear ranges of two orders of magnitude with r > 0.99. The method was validated in terms of matrix effect, intra-day and inter-day precision, accuracy, linearity, specificity and stability. The relative standard deviation of intra-day and inter-day variations ranged from 2.66 to 14.74% and 1.9 to 14.55%. No substantial endogenous interference from blank plasma was observed. The method has been successfully applied to a pharmacokinetic study of Oldenlandia diffusa extract after oral administration in rats.
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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) | 0.001 | 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) | 0.000 | 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 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".