Quantification of caffeoylquinic acids and triterpenes as targeted bioactive compounds of Centella Asiatica in extracts and formulations by liquid chromatography mass spectrometry
Why this work is in the frame
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Bibliographic record
Abstract
(CA) is a culinary vegetable and well-known functional food that is widely used as a medicinal herb and dietary supplement. CA is rich in pentacyclic triterpenes (TTs), including asiaticoside (AS), madecassoside (MS) and the related aglycones asiatic acid (AA), madecassic acid (MA). Traditionally, TTs have been associated with the bioactivity and health promoting effect of CA. Recently, mono-caffeoylquinic acids (MonoCQAs) and di-caffeoylquinic acids (DiCQAs) have been found to contribute to the bioactivity of CA as well. This work reports an analytical strategy based on liquid chromatography coupled to multiple reaction monitoring mass spectrometry (LC-MRM-MS) for the simultaneous rapid and accurate quantification of 12 bioactive compounds in CA, namely AS, MS, AA, MA, 5-CQA, 4-CQA, 3-CQA, 1,3-DiCQA, 3,4-DiCQA, 1,5-DiCQA, 3,5-DiCQA, 4,5-DiCQA. Method selectivity, accuracy, precision, repeatability, robustness, linearity range, limit of detection (LOD), and limit of quantitation (LOQ) were validated. The validated LC-MRM-MS method has been successfully applied to quantify the 12 bioactive compounds in CA aqueous extracts and two related formulations: a standardized CA product (CAP) used in a phase I clinical trial and formulated CA rodent diets used in preclinical studies. The validated method allows us to support the standardization of CA products used for clinical trials and conduct routine LC-MRM-MS analyses of formulated preclinical diets to confirm correct levels of CA phytochemical markers.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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