A RP‐HPLC‐DAD‐APCI/MSD method for the characterisation of medicinal Ericaceae used by the Eeyou Istchee Cree First Nations
Why this work is in the frame
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Bibliographic record
Abstract
INTRODUCTION: Ericaceae medicinal plants are traditionally used by the Eeyou Istchee Cree and other northern peoples of North America to treat type 2 diabetic symptoms. Because of the importance of phenolics as potential cures for degenerative diseases including type 2 diabetes, an analytical method was developed to detect them in the leaf extracts of 14 Ericaceae plants. OBJECTIVE: To develop an optimised method which is applicable to a relatively large number of Ericaceae plants using their leaf extracts. For this purpose phenolics with a wide range of polarity, including a glucosylated benzoquinone, two phenolic acids, three flavanols, a flavanone, a flavone and five flavonols, were included in this study. METHODOLOGY: Characterisation of phytochemicals in extracts was undertaken by automated matching to the UV spectra to those of an in house library of plant secondary metabolites and the authentication of their identity was achieved by reversed phase-high-performance chromatography-diode array detection-atmospheric pressure chemical ionisation/mass selective detection. RESULTS: Twenty-six phenolics were characterised within 26 min of chromatographic separation in 80% ethanol extracts of 14 Ericaceae plants. The calibration curves were linear within 0.5-880 microg/g dry mass of the plant with regression values better than 0.995. The limits of detection ranged from 0.3 for microg/mL for (+)-catechin to 2.6 microg/mL for chlorogenic acid. This is a first study dealing with relatively large number of Ericaceae extracts and is applicable to other plants of same family.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it