One Health: New Approaches, Research, and Innovation to Zoonoses—2nd Edition

Special Issue Editor


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Guest Editor
Department of Biomedical Scinences, University of West Attica, Egaleo Park Campus, Agiou Spiridonos Str. Egaleo, 12243 Athens, Greece
Interests: parasitology; drug resistance; zoonoses; animal welfare; one health
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Special Issue Information

Dear Colleagues,

Zoonoses are diseases or infections that are naturally transmissible from vertebrate animals to humans. Zoonotic pathogens may be bacterial, viral, or parasitic or may involve unconventional agents and can spread to humans through direct contact or through food, water, or the environment. They represent a major public health problem around the world due to our close relationship with animals in agriculture, as companions, and in the natural environment.

The One Health concept highlights the strong relationship between the health of people, animals, and ecosystems and emphasizes the necessity of collaboration among professionals from various domains — including medicine, veterinary science, and environmental science —for effective control and health risk prevention.

The overall goal of this Special Issue is the presentation of research and innovation for an improved comprehension of zoonoses focusing on the human–animal–environment interaction, covering the role of bacterial, viral, parasitic, fungal, and prion infections, as well as wildlife and other vectors (including arthropods), in the (re-)emergence and transmission of zoonotic pathogens.

Dr. Chrysa Voyiatzaki
Guest Editor

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Keywords

  • zoonoses
  • human–animal–environment interaction
  • bacteria
  • fungi
  • parasites
  • prion
  • virus

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Related Special Issue

Published Papers (1 paper)

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Research

14 pages, 2014 KiB  
Article
FungID: Innovative Fungi Identification Method with Chromogenic Profiling of Colony Color Patterns
by John Pouris, Konstantinos Konstantinidis, Ioanna Pyrri, Effie G. Papageorgiou and Chrysa Voyiatzaki
Pathogens 2025, 14(3), 242; https://doi.org/10.3390/pathogens14030242 - 3 Mar 2025
Viewed by 741
Abstract
Fungi play crucial roles in many ecosystems; however, traditional identification methods are often time- and labor-intensive. In this study, we introduce FungID, a pilot and novel deep learning algorithm, alongside its user-friendly software implementation, developed by analyzing various fungal species for identification based [...] Read more.
Fungi play crucial roles in many ecosystems; however, traditional identification methods are often time- and labor-intensive. In this study, we introduce FungID, a pilot and novel deep learning algorithm, alongside its user-friendly software implementation, developed by analyzing various fungal species for identification based on chromogenic profiling of colony color patterns via a Convolutional Neural Network. Training and testing FungID upon a set of 269 images showed remarkable performance in terms of model robustness and classification efficacy. These findings demonstrate that FungID offers a potential method for rapid and reliable identification of fungal species through chromogenic profiling, providing additional tools to conventional techniques being employed in the fields of health, microbiology, biotechnology, and more. Our research underscores the promising role of deep learning algorithms in enhancing the understanding of the taxonomy and ecological functions of fungi that can be grown in pure cultures, while also emphasizing the importance of carefully assessing the scope and limitations of these methods. Full article
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