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Macrofungi as Medicinal Resources in Uzbekistan: Biodiversity, Ethnomycology, and Ethnomedicinal Practices.

Yusufjon GafforovMilena RašetaSylvie RapiorManzura YarashevaXuewei WangLiwei ZhouWan Abd Al Qadr Imad Wan-MohtarMuhammad ZafarYoung Woon LimMengcen WangBekhzod AbdullaevRainer W BussmannMuhammad Imran TousifJia-Jia Chen
Published in: Journal of fungi (Basel, Switzerland) (2023)
Interest in edible and medicinal macrofungi is millennial in terms of their uses in health and food products in Central Asia, while interest in inedible and medicinal macrofungi has grown in popularity in recent years. Edible and inedible medicinal basidiomycetes were collected during field surveys from different regions of Uzbekistan. The morphological characters and similarity assessment of rDNA-Internal Transcribed Spacer sequence data were used to measure diversity and habitat associations. A number of 17 species of medicinal macrofungi of ethnomycological and medicinal interest was found associated with 23 species of trees and shrubs belonging to 11 families and 14 genera. Polyporaceae and Hymenochaetaceae were represented by the highest number of species followed by Ganodermataceae , Fomitopsidaceae , Auriculariaceae , Cerrenaceae , Grifolaceae , Phanerochaetaceae , Laetiporaceae , Schizophyllaceae , and Stereaceae . The highest number of medicinal basidiomycete species was reported in the following host genera: Acer , Betula , Celtis , Crataegus , Juglans , Juniperus , Lonicera , Malus , Morus , Platanus , Populus , Prunus , Quercus , and Salix . An updated list of edible and inedible medicinal mushrooms identified in Uzbekistan, their morphological characteristics, and phylogenetic placement are given for the first time. Information is provided on their uses in traditional and modern medicine. Their bioactive compounds and extracts can be applied as medicines, as well as food and cosmetic ingredients.
Keyphrases
  • healthcare
  • primary care
  • public health
  • mental health
  • climate change
  • risk assessment
  • electronic health record
  • machine learning
  • genetic diversity
  • cross sectional