Zika virus (ZIKV) is a flavivirus transmitted by Aedes Aegypti mosquito, which led to a high incidence of cases in Brazil between 2015 and 2016. Although 80% of the cases are asymptomatic, the later association of the disease with neurological complications such as Guillain-Barré syndrome in adults and microcephaly in neonates of infected pregnant women, led to initiatives in the development of diagnostic methods and vaccines, and studies of viral infection process in human and mosquito cells. In parallel, metabolomic strategies associated with mass spectrometry and artificial intelligence have advanced in the research of new therapeutic and diagnostic targets, with more precise and sensitive methodologies. These tools supported advances in ZIKV research, bringing innovation to the elucidation of viral infection biomarkers, understanding of virus mechanisms of action, diagnostic methods and evidences of ZIKV oncolytic potential. The Zika virus tropism for different cell types, including neural and gonads cells, associated with its ability to induce glioblastoma cell death have suggested the possibility of using ZIKV for the treatment of other cancers, such as prostate and ovary cancers. Using in vitro models, the objective is to investigate the therapeutic potential and the metabolic alterations induced by ZIKV through metabolomic studies using mass spectrometry and artificial intelligence. (AU)
News published in Agência FAPESP Newsletter about the scholarship:
DIAS-AUDIBERT, FLAVIA LUISA;
NAVARRO, LUIZ CLAUDIO;
DE OLIVEIRA, DIOGO NOIN;
MELO, CARLOS FERNANDO ODIR RODRIGUES;
GUERREIRO, TATIANE MELINA;
ROSA, FLAVIA TRONCON;
PETENUCI, DIEGO LIMA;
WATANABE, MARIA ANGELICA EHARA;
VELLOSO, LICIO AUGUSTO;
ROCHA, ANDERSON REZENDE;
CATHARINO, RODRIGO RAMOS.
Combining Machine Learning and Metabolomics to Identify Weight Gain Biomarkers.
FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY,
JAN 24 2020.
Web of Science Citations: 0.
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