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Saprolegniosis in Amphibians: An Integrated Overview of a Fluffy Killer Disease.

Sara CostaIsabel Lopes
Published in: Journal of fungi (Basel, Switzerland) (2022)
Amphibians constitute the class of vertebrates with the highest proportion of threatened species, with infectious diseases being considered among the greatest causes for their worldwide decline. Aquatic oomycetes, known as "water molds", are fungus-like microorganisms that are ubiquitous in freshwater ecosystems and are capable of causing disease in a broad range of amphibian hosts. Various species of Achlya sp., Leptolegnia sp., Aphanomyces sp., and mainly, Saprolegnia sp., are responsible for mass die-offs in the early developmental stages of a wide range of amphibian populations through a disease known as saprolegniosis, aka, molding or a " Saprolegnia -like infection". In this context, the main objective of the present review was to bring together updated information about saprolegniosis in amphibians to integrate existing knowledge, identify current knowledge gaps, and suggest future directions within the saprolegniosis-amphibian research field. Based on the available literature and data, an integrated and critical interpretation of the results is discussed. Furthermore, the occurrence of saprolegniosis in natural and laboratory contexts and the factors that influence both pathogen incidence and host susceptibility are also addressed. The focus of this work was the species Saprolegnia sp., due to its ecological importance on amphibian population dynamics and due to the fact that this is the most reported genera to be associated with saprolegniosis in amphibians. In addition, integrated emerging therapies, and their potential application to treat saprolegniosis in amphibians, were evaluated, and future actions are suggested.
Keyphrases
  • infectious diseases
  • healthcare
  • risk assessment
  • climate change
  • systematic review
  • current status
  • genetic diversity
  • human health
  • machine learning
  • social media
  • candida albicans
  • big data
  • health information