Evidence mapPaperPMID 41617809Full record

ArticleScientific reports2026

Leveraging metabolic similarity in a

Sumin Seo, Özlem Erol, Hye Kyong Kim, Harald G J van Mil, Jeeyoun Jung, Young Pyo Jang, Dae Young Lee, Mei Wang, Helen Sheridan, Sang Beom Han and 1 more

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

11 authors.

Sumin SeoNatural Products Laboratory, Institute of Biology, Leiden University, 2333 BE, Leiden, The Netherlands.
Özlem ErolNatural Products Laboratory, Institute of Biology, Leiden University, 2333 BE, Leiden, The Netherlands.
Hye Kyong KimNatural Products Laboratory, Institute of Biology, Leiden University, 2333 BE, Leiden, The Netherlands.
Harald G J van MilMathematical Institute of Leiden, Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands.
Jeeyoun JungKorea Medicine Science Research Division, Korea Institute of Oriental Medicine, Daejeon, 34054, Republic of Korea.
Young Pyo JangDepartment of Oriental Pharmaceutical Sciences, College of Pharmacy, Kyung Hee University, Seoul, 02447, Republic of Korea.
Dae Young LeeBK21 FOUR KNU Creative BioResearch Group, School of Life Sciences, Kyungpook National University, Daegu, 41566, Republic of Korea.
Mei WangNaturalis Biodiversity Center, Darwinweg 2, 2333 CR Leiden, Postbus 9517, 2300 RA, Leiden, The Netherlands.
Helen SheridanNatPro Centre, School of Pharmacy and Pharmaceutical Sciences, Trinity College Dublin, Dublin 02, D02 PN40, Dublin, Ireland.
Sang Beom HanCollege of Pharmacy, Chung-Ang University, Seoul, 06974, Republic of Korea.
Young Hae ChoiNatural Products Laboratory, Institute of Biology, Leiden University, 2333 BE, Leiden, The Netherlands. y.choi@chem.leidenuniv.nl.

Funding

Hangzhou Ganzhicao Technology Co. Ltd GanCao 025009Programma EFROWEST-Netherland 2021-2027 203876The Department of Justice, Ireland DOJProject209825The Korea Institute of Oriental Medicine KSN2312021
6 · The paper itself

Abstract

Natural products remain a central resource for drug discovery, and increasing evidence suggests that therapeutic effects often arise from the combined action of multiple constituents rather than single compounds. In this context, metabolomic profiling is essential for comparing complex plant chemical phenotypes, and 1H NMR provides robust whole-profile fingerprints that support cross-species metabolic barcoding and systematic comparison. In this study, we establish and apply a standardized large-scale 1H NMR database to enable macroscopic metabolomic similarity profiling of medicinal plants. Specifically, using 1H NMR profiles from 656 traditional medicinal herbs, we demonstrate how this standardized large-scale metabolomic framework can be applied to key challenges in medicinal plant research, including quality control across different locations and time periods, identification of metabolically similar alternative species, and compositional analysis of multi-herb formulations. Our findings demonstrate the utility of this NMR-based strategy as a scalable approach for standardization, authentication, and holistic characterization of medicinal plants, advancing the field beyond reductionist paradigms. This study establishes a standardized large-scale 1H NMR database of medicinal plants and introduces a macroscopic framework for large-scale metabolomic similarity profiling that enables chemotaxonomic contextualization, quality surveillance, and identification of metabolically similar candidate substitutes.

Indexed as

Databases, FactualMetabolomicsPlants, MedicinalProton Magnetic Resonance SpectroscopyBiological ProductsMagnetic Resonance SpectroscopyMetabolomeBiological ProductsMacroscopic approachMedicinal plantsMetabolic profilingMetabolic similaritiesMulti-herbal drugsNMRQuality control

Identifiers

PMID41617809
PMCPMC12914036

What Socratic holds

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LicenceCC BY-NC-ND
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Registered trials

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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 Socratic graph.