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The Laboratory Diagnosis of Malaria: A Focus on the Diagnostic Assays in Non-Endemic Areas.
Calderaro, Adriana; Piccolo, Giovanna; Chezzi, Carlo.
Afiliação
  • Calderaro A; Department of Medicine and Surgery, University of Parma, Viale A. Gramsci 14, 43126 Parma, Italy.
  • Piccolo G; Department of Medicine and Surgery, University of Parma, Viale A. Gramsci 14, 43126 Parma, Italy.
  • Chezzi C; Department of Medicine and Surgery, University of Parma, Viale A. Gramsci 14, 43126 Parma, Italy.
Int J Mol Sci ; 25(2)2024 Jan 05.
Article em En | MEDLINE | ID: mdl-38255768
ABSTRACT
Even if malaria is rare in Europe, it is a medical emergency and programs for its control should ensure both an early diagnosis and a prompt treatment within 24-48 h from the onset of the symptoms. The increasing number of imported malaria cases as well as the risk of the reintroduction of autochthonous cases encouraged laboratories in non-endemic countries to adopt diagnostic methods/algorithms. Microscopy remains the gold standard, but with limitations. Rapid diagnostic tests have greatly expanded the ability to diagnose malaria for rapid results due to simplicity and low cost, but they lack sensitivity and specificity. PCR-based assays provide more relevant information but need well-trained technicians. As reported in the World Health Organization Global Technical Strategy for Malaria 2016-2030, the development of point-of-care testing is important for the improvement of diagnosis with beneficial consequences for prompt/accurate treatment and for preventing the spread of the disease. Despite their limitations, diagnostic methods contribute to the decline of malaria mortality. Recently, evidence suggested that artificial intelligence could be utilized for assisting pathologists in malaria diagnosis.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Malária Tipo de estudo: Diagnostic_studies / Screening_studies Limite: Humans Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Malária Tipo de estudo: Diagnostic_studies / Screening_studies Limite: Humans Idioma: En Ano de publicação: 2024 Tipo de documento: Article