Evidence map›Paper›PMID 40644415›Full record

ArticlePloS one2025

Modelling key ecological factors influencing the distribution and content of silymarin antioxidant in Silybum marianum L.

Mahboobe Hojati, Ruhollah Naderi, Mohsen Edalat, Hamid Reza Pourghasemi

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Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Mahboobe HojatiDepartment of Plant Production and Genetics, School of Agriculture, Shiraz University, Shiraz, Iran.
Ruhollah NaderiDepartment of Plant Production and Genetics, School of Agriculture, Shiraz University, Shiraz, Iran.ORCID https://orcid.org/0000-0003-2252-8124
Mohsen EdalatDepartment of Plant Production and Genetics, School of Agriculture, Shiraz University, Shiraz, Iran.
Hamid Reza PourghasemiDepartment of Soil Science, School of Agriculture, Shiraz University, Shiraz, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The increasing demand for natural medicine has increased the significance of Silybum marianum as a valuable medicinal plant. It is used to restore liver cells; reduce blood cholesterol; prevent prostate, skin, and breast cancer; and protect cervical cells and kidneys. To identify ecological factors affecting the distribution and amount of silymarin in S. marianum three machine learning algorithms including boosted regression trees (BRT), random forest (RF), and support vector machines (SVM) have been applied in Fars Province, Iran. Fourteen factors affecting S. marianum growth and development were determined and subsequently converted into raster maps for the modeling phase using a Geographic Information System (GIS). Subsequently, the Receiver Operating Characteristic (ROC) curve and random forest algorithm were used to evaluate the models and the significance of the factors, respectively. Results showed that The RF (ROC: 0.99), BRT (ROC: 0.98), and SVM (ROC: 0.96) models were highly accurate in predicting the habitat suitability of S. marianum. The results of the RF algorithm also revealed that factors such as distance from roads, elevation, and mean annual rainfall had the most significant influence on the habitat suitability of S. marianum. In addition, the mean annual rainfall, mean annual temperature, and elevation had the highest effects on silymarin accumulation. In general, the northern and northwestern regions of the Fars Province offer optimal environmental conditions for the growth of S. marianum. The southern and southwestern regions of Fars Province, characterized by higher temperatures and lower precipitation, are suitable for the enhanced biosynthesis of silymarin and expansion of its cultivation and production. This study provides a robust framework for understanding the ecological preferences of S. marianum and optimizing its cultivation and management for pharmaceutical applications. By identifying the most influential environmental variables, this research has the potential for the sustainable utilization of this species, enhancing both its conservation and use as a medicinal resource.

Indexed as

AntioxidantsSilybum marianumSilymarinAlgorithmsEcosystemIranSupport Vector MachineAntioxidantsSilymarin

Identifiers

PMID40644415
PMCPMC12250520

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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.