Unsupervised machine learning identifies biomarkers of disease progression in post-kala-azar dermal leishmaniasis in Sudan.

Journal: PLoS neglected tropical diseases
PMID: 40067811

Abstract

BACKGROUND: Post-kala-azar dermal leishmaniasis (PKDL) appears as a rash in some individuals who have recovered from visceral leishmaniasis caused by Leishmania donovani. Today, basic knowledge of this neglected disease and how to predict its progression remain largely unknown.

Authors

  • Ana Torres
    WHO Collaborating Centre for Leishmaniasis. Spanish National Centre for Microbiology, Instituto de Salud Carlos III, Majadahonda (Madrid), Spain.
  • Brima Musa Younis
    Institute of Endemic Diseases, University of Khartoum, Khartoum, Sudan.
  • Samuel Tesema
    Drugs for Neglected Diseases Initiative, Nairobi, Kenya.
  • Jose Carlos Solana
    WHO Collaborating Centre for Leishmaniasis. Spanish National Centre for Microbiology, Instituto de Salud Carlos III, Majadahonda (Madrid), Spain.
  • Javier Moreno
    Department of Computer Science and Industrial Engineering, Universitat de Lleida, Jaume II, 69, 25001 Lleida, Spain. jmoreno@diei.udl.cat.
  • Antonio J Martín-Galiano
    Core Scientific and Technical Units, Instituto de Salud Carlos III, Madrid, Spain.
  • Ahmed Mudawi Musa
    Institute of Endemic Diseases, University of Khartoum, Khartoum, Sudan.
  • Fabiana Alves
    Drugs for Neglected Diseases Initiative, Geneva, Switzerland.
  • Eugenia Carrillo
    WHO Collaborating Centre for Leishmaniasis. Spanish National Centre for Microbiology, Instituto de Salud Carlos III, Majadahonda (Madrid), Spain.

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