Evidence map›Paper›PMID 39200436›Full record

ArticleFoods (Basel, Switzerland)2024

Design of Experiments and Optimization of Monacolin K Green Extraction from Red Yeast Rice by Ultra-High-Performance Liquid Chromatography.

Lara Davani, Cristina Terenzi, Angela De Simone, Vincenzo Tumiatti, Vincenza Andrisano, Serena Montanari

Abstract read
In one paragraph

Article in Foods (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Lara DavaniDepartment for Life Quality Studies, University of Bologna, Corso D'Augusto 237, 47921 Rimini, Italy.
Cristina TerenziDepartment for Life Quality Studies, University of Bologna, Corso D'Augusto 237, 47921 Rimini, Italy.ORCID 0000-0002-2757-5547
Angela De SimoneDepartment of Drug Science and Technology University of Torino, Via P. Giuria 9, 10125 Torino, Italy.ORCID 0000-0003-1915-458X
Vincenzo TumiattiDepartment for Life Quality Studies, University of Bologna, Corso D'Augusto 237, 47921 Rimini, Italy.ORCID 0000-0002-8839-3093
Vincenza AndrisanoDepartment for Life Quality Studies, University of Bologna, Corso D'Augusto 237, 47921 Rimini, Italy.ORCID 0000-0003-4396-1904
Serena MontanariDepartment for Life Quality Studies, University of Bologna, Corso D'Augusto 237, 47921 Rimini, Italy.ORCID 0000-0002-7890-3096

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Monacolin K (MK), in red yeast rice (RYR) in the forms of lactone (LMK) and hydroxy acid (AMK), is known for its anti-hypercholesterolemic activity. Under the rising demand for natural bioactive molecules, we present a green ultrasound-assisted extraction (UAE) optimization study for MK in RYR. The development and validation of a fast, sensitive, selective, reproducible, and accurate ultra-high-performance liquid chromatography (UHPLC) method coupled to diode array detection for LMK and AMK allowed us to evaluate the MK recovery in different extract media. Firstly, the ethanol comparability to acetonitrile was assessed (recovery of 80.7 ± 0.1% for ethanol and 85.5 ± 0.2% for acetonitrile). Then, water/ethanol mixtures, with decreasing percentages of organic solvent, were tested by modulating temperature and extraction times. Water extractions at 80 °C for 10 min produced MK yield > 85%. Thus, UAE conditions were optimized by a DOE study using a water-based formulation (mouthwash). The optimal total MK extraction yield (86.6 ± 0.4%) was found under the following conditions: 80 °C, 45 min, 5 mg mL

Indexed as

DOEenriched mouthwashgreen extractionMonacolin KUHPLC-DAD method

Identifiers

PMID39200436
PMCPMC11353663

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.