NMR evaluation of total statin content and HMG-CoA reductase inhibition in red yeast rice (Monascus spp.) food supplements
Bibliographic record
Abstract
BACKGROUND: Red yeast rice (i.e., rice fermented with Monascus spp.), as a food supplement, is claimed to be blood cholesterol-lowering. The red yeast rice constituent monacolin K, also known as lovastatin, is an inhibitor of the hydroxymethylglutaryl-CoA (HMG-CoA) reductase. This article aims to develop a sensitive nuclear magnetic resonance (NMR) method to determine the total statin content of red yeast rice products. METHODS: The total statin content was determined by a 400 MHz 1H NMR spectroscopic method, based on the integration of the multiplet at δ 5.37-5.32 ppm of a hydrogen at the hexahydronaphthalene moiety in comparison to an external calibration with lovastatin. The activity of HMG-CoA reductase was measured by a commercial spectrophotometric assay kit. RESULTS: The NMR detection limit for total statins was 6 mg/L (equivalent to 0.3 mg/capsule, if two capsules are dissolved in 50 mL ethanol). The relative standard deviations were consistently lower than 11%. The total statin concentrations of five red yeast rice supplements were between 1.5 and 25.2 mg per specified daily dose. A dose-dependent inhibition of the HMG-CoA reductase enzyme activity by the red yeast rice products was demonstrated. CONCLUSION: A simple and direct NMR assay was developed to determine the total statin content in red yeast rice. The assay can be applied for the determination of statin content for the regulatory control of red yeast rice products.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".