HPLC‐MS/MS analysis of anthocyanins in human plasma and urine using protein precipitation and dilute‐and‐shoot sample preparation methods, respectively
Bibliographic record
Abstract
A high-performance liquid chromatography tandem-mass spectrometry (HPLC-MS/MS) method has been developed to analyze anthocyanins in urine and plasma to further understand their absorption, distribution, metabolism and excretion. The method employed a Synergi RP-Max column (250 × 4.6 mm, 4 μm) and an API 4000 mass spectrometer. A gradient elution system consisted of mobile phase A (water-1% formic acid) and mobile phase B (acetonitrile) with a flow rate of 0.60 mL/min. The gradient was initiated at 5% B, increased to 21% B at 20 min, and then increased to 40% B at 35 min. The analysis of anthocyanins presents a challenge because of the poor stability of anthocyanins during sample preparation, especially during solvent evaporation. In this method, the degradation of anthocyanins was minimized using protein precipitation and dilute-and-shoot and sample preparation methods for plasma and urine, respectively. No interferences were observed from endogenous compounds. The method has been used to analyze anthocyanin concentrations in urine and plasma samples from volunteers administered saskatoon berries. Cyanidin-3-galactoside, cyanidin-3-glucoside, cyanidin-3-arabinoside, cyanidin-3-xyloside and quercetin-3-galactoside, the five major flavonoid components in saskatoon berries, were identified in plasma and urine samples.
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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 | 0.001 | 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 | 0.001 |
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".