Canine visceral leishmaniasis diagnosis by UV spectroscopy of blood serum and machine learning algorithms.

Autor: Coelho ML; Faculty of Veterinary Medicine and Animal Husbandry (FAMEZ), Universidade Federal de Mato Grosso do Sul - UFMS, Campo Grande 79070-900, Brazil., França T; SISFOTON-UFMS, Optics and Photonics Group, Institute of Physics, Universidade Federal de Mato Grosso do Sul - UFMS, Campo Grande 79070-900, Brazil., Fontoura Mateus NL; Faculty of Veterinary Medicine and Animal Husbandry (FAMEZ), Universidade Federal de Mato Grosso do Sul - UFMS, Campo Grande 79070-900, Brazil., da Costa Lima Junior MS; Department of Immunology, Aggeu Magalhães Institute, FIOCRUZ, Recife, PE 50740-465, Brazil., Cena C; SISFOTON-UFMS, Optics and Photonics Group, Institute of Physics, Universidade Federal de Mato Grosso do Sul - UFMS, Campo Grande 79070-900, Brazil. Electronic address: cicero.cena@ufms.br., do Nascimento Ramos CA; Faculty of Veterinary Medicine and Animal Husbandry (FAMEZ), Universidade Federal de Mato Grosso do Sul - UFMS, Campo Grande 79070-900, Brazil. Electronic address: carlos.nascimento@ufms.br.
Jazyk: angličtina
Zdroj: Photodiagnosis and photodynamic therapy [Photodiagnosis Photodyn Ther] 2023 Jun; Vol. 42, pp. 103575. Date of Electronic Publication: 2023 Apr 18.
DOI: 10.1016/j.pdpdt.2023.103575
Abstrakt: Visceral leishmaniasis (VL) is a zoonotic disease caused by the protozoan Leishmania infantum, and dogs are considered the main urban hosts for future disease transmission. The first and most effective control against the spread of disease relies on identifying infected animals, followed by their treatment or sacrifice, to reduce the protozoan reservoirs. Despite the availability of various diagnostic tests for VL in dogs the development of a quick and accurate diagnosis is essential from a public health and ethical point of view. Here we analyze the use of UV-Vis spectroscopy as an alternative diagnostic method for VL diagnosis by using the antigen-antibody interaction in canine blood serum and machine learning algorithms. The main UV spectra in the 220 to 280 nm range exhibit nine electronic absorption bands, but no significative difference could be identified between the positive and negative group spectra. Finally, UV pre-proceed spectra by SNV (standard normal variate) were submitted to principal component analysis followed by Linear SVM algorithm, the prediction model was tested in a leave-one-out cross-validation and external validation test reaching an overall accuracy of 75%.
Competing Interests: Declaration of Competing Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
(Copyright © 2023 Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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