Validation of a HPLC method for flavonoid biomarkers in skullcap (Scutellaria) and its use to illustrate wide variability in the quality of commercial tinctures.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: To compare the flavonoid biomarker content (baicalin, baicalein and wogonin) of eleven commercial tinctures derived from Scutellaria lateriflora aerial parts (n=7) and Scutellaria baicalensis root (n=4). S. lateriflora tinctures are used in by western herbal practitioners to treat anxiety whereas S. baicalensis tinctures are used to treat inflammatory disease. METHODS: Baicalin and baicalein were purchased from Aldrich Chemical Co. and Wogonin was purchased from ChromaDex. The internal standard (4-hydroxybenzoic acid) was obtained from Acros Organics. The column used was a Luna C18, 5 m (150 x 4.6 mm, Phenomenex) maintained at ambient room temperature. A HP1050 HPLC system was used, comprising a gradient pump with degasser, a variable wavelength UV detector set to 270 nm, and an autosampler. Gradient elution was performed using 0.1% formic acid (eluent A) and methanol (eluent B). The gradient elution initial conditions were 45% B with linear gradient to 60% from 2 to 10 min, followed by linear gradient to 70% B at 30 min, and then linear gradient to 99% B at 31 min, this proportion being maintained for 1 min. The mobile phase was then returned to initial conditions at 33 min and maintained until the end of the run at 35 min. The flow rate was 1 mL/min. The assay was validated for sensitivity, accuracy and reproducibility. RESULTS: The concentration range of biomarkers (baicalin, baicalein and wogonin) in commercial tinctures is reported for S. lateriflora (baicalin: 0-12.66 mg/mL; baicalein: 0-0.63 mg/mL; wogonin: 0-0.16 mg/mL) and for S. baicalensis (baicalin: 0.12-10.61 mg/mL; baicalein: 0.52-5.88 mg/mL; wogonin: 0.08-1.61 mg/mL). CONCLUSION: The wide variability in biomarker concentrations between commercial tinctures has important implications for the manufacturers of commercial tinctures, for herbal practitioners in the choice of tinctures and not least for pharmacology and clinical researchers.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it