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Record W2148252321 · doi:10.18433/j39g6v

Validation of a HPLC method for flavonoid biomarkers in skullcap (Scutellaria) and its use to illustrate wide variability in the quality of commercial tinctures.

2008· article· en· W2148252321 on OpenAlexvenueno aff
Jiayu Gao, Alberto Sánchez-Medina, Barbara Pendry, Michael Hughes, Geoffrey P. Webb, Olivia Corcoran

Bibliographic record

VenueJournal of Pharmacy & Pharmaceutical Sciences · 2008
Typearticle
Languageen
FieldMedicine
TopicFlavonoids in Medical Research
Canadian institutionsnot available
FundersSir Richard Stapley Educational TrustUniversity of East London
KeywordsBaicalinWogoninBaicaleinChromatographyScutellariaScutellaria baicalensisChemistryGradient elutionHigh-performance liquid chromatographyFlavonoidTraditional medicineMedicinePharmacologyBiochemistry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.333
GPT teacher head0.552
Teacher spread0.219 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations55
Published2008
Admission routes1
Has abstractyes

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