Lívia Rosa-Fernandes

This paper applies fractal analysis to MALDI-TOF MS data for glioblastoma diagnosis, showing fractals can improve machine learning models but still underperform compared to traditional feature extraction.

This study uses MALDI-TOF MS and machine learning to identify serum biomarkers for astrocytoma diagnosis, achieving 94.5% accuracy in distinguishing GBM from healthy controls.

The paper infers evolutionary relationships among Leishmaniinae parasites using protein expression profiles, finding results congruent with established DNA-based phylogeny.

This study analyzes the proteomes of three Leishmania species under different growth phases and temperature stress to identify molecular changes during parasite-host interactions.

This study investigates the role of ATE1-mediated N-terminal arginylation in glioblastoma, finding it activates ER-stress and UPR pathways, promotes autophagy, and supports tumor growth.

The paper analyzes RNAseq data from ocular cells of infants exposed to Zika virus prenatally, revealing gene expression changes in both symptomatic and asymptomatic cases.

This paper describes an optimized impression cytology method to obtain viable ocular cells from babies with Congenital Zika Syndrome, enabling transcriptomic and proteomic analyses.