An Accurate and Reproducible Method for the Quantitative Analysis of Isoflavones and Their Metabolites in Rat Plasma Using Liquid Chromatography/Mass Spectrometry Combined with Photodiode Array Detection
Bibliographic record
Abstract
To study the safety and potential health benefits of soy isoflavones, a rapid and simple method based on liquid chromatography combined with mass spectrometry (LC/MS) and photodiode array detector (PDA) was developed for the determination of isoflavones in rat plasma. The analytes included daidzein, genistein, glycitein, equol, 4-ethyl phenol, and biochanin A over a concentration range of 1.0-4320.0 nM using 75 microL of rat plasma. Rat plasma samples were hydrolyzed by adding an enzyme mixture from Helix pomatia containing glucuronidase and sulfatase to convert the isoflavone beta-glycosides daidzin, genistin, and glycitin to their active aglycone forms. A liquid-liquid extraction method using ethyl acetate as the extraction solvent was used to extract aglycones and the internal standards (phenolphthalein beta-D glucuronide, 4-methylumbelliferyl sulfate, and apigenin) from digested plasma samples. The extract was evaporated to dryness under a nitrogen stream, reconstituted with 0.1% formic acid in water-acetonitrile (85 + 15), and injected into a Zorbax SB-CN reversed-phase column (4.6 x 75 mm, 3.5 microm particle size). The Micromass ZQ detector was operated in the positive ion selected-ion monitoring mode. The flow rate for LC was 1.0 mL/min, with a split where 25% of the effluent was introduced into the electrospray ionization probe of the MS instrument and 75% into the PDA. The chromatographic run time was 16.0 min, with delay of 10 min/injection. The interday precision and accuracy of the standard samples were <2.6% relative standard deviation and <10% relative error, respectively. Recovery of the reported isoflavones with this method varied from 86 to 100%.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".