Evidence map›Paper›PMID 33106579›Full record

ArticleScientific reports2020

DNA metabarcoding to unravel plant species composition in selected herbal medicines on the National List of Essential Medicines (NLEM) of Thailand.

Santhosh Kumar J Urumarudappa, Chayapol Tungphatthong, Pinidphon Prombutara, Suchada Sukrong

Abstract read
In one paragraph

Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

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3 · Its place in the literature

Who cites it

13 citing papers in PubMed.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

4 authors.

Santhosh Kumar J UrumarudappaResearch Unit of DNA Barcoding of Thai Medicinal Plants, Department of Pharmacognosy and Pharmaceutical Botany, Faculty of Pharmaceutical Sciences, Chulalongkorn University, Bangkok, 10330, Thailand.
Chayapol TungphatthongResearch Unit of DNA Barcoding of Thai Medicinal Plants, Department of Pharmacognosy and Pharmaceutical Botany, Faculty of Pharmaceutical Sciences, Chulalongkorn University, Bangkok, 10330, Thailand.
Pinidphon PrombutaraOmics Sciences and Bioinformatics Center, Faculty of Science, Chulalongkorn University, Bangkok, 10330, Thailand.
Suchada SukrongResearch Unit of DNA Barcoding of Thai Medicinal Plants, Department of Pharmacognosy and Pharmaceutical Botany, Faculty of Pharmaceutical Sciences, Chulalongkorn University, Bangkok, 10330, Thailand. suchada.su@chula.ac.th.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Traditional medicines are widely traded across the globe and have received considerable attention in the recent past, with expectations of heightened demand in the future. However, there are increasing global concerns over admixture, which can affect the quality, safety, and efficacy of herbal medicinal products. In this study, we aimed to use DNA metabarcoding to identify 39 Thai herbal products on the Thai National List of Essential Medicines (NLEM) and assess species composition and admixture. Among the products, 24 samples were in-house-prepared formulations, and 15 samples were registered formulations. In our study, DNA metabarcoding analysis using ITS2 and rbcL barcode regions were employed to identify herbal ingredients mentioned in the products. The nuclear region, ITS2, was able to identify herbal ingredients in the products at the genus- and family-levels in 55% and 63% of cases, respectively. The chloroplast gene, rbcL, enabled genus- and family-level identifications in 58% and 73% of cases, respectively. In addition, plant species were detected in larger numbers (Family identified, absolute %) in registered herbal products than in in-house-prepared formulations. The level of fidelity increases concerns about the reliability of the products. This study highlights that DNA metabarcoding is a useful analytical tool when combined with advanced chemical techniques for the identification of plant species in highly processed, multi-ingredient herbal products.

Indexed as

DNA Barcoding, TaxonomicDNA, PlantHerbal MedicinePlant PreparationsPlants, MedicinalReproducibility of ResultsThailandDNA, PlantPlant Preparations

Identifiers

PMID33106579
PMCPMC7588419

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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.