Publications by authors named "Tayna E Lima"
Article Synopsis
- The study emphasizes the urgent need to identify risk factors for COVID-19 severity to improve patient care and allocate resources effectively.
- It highlights that current disease severity classification relies on clinical parameters and blood tests that lack global standardization, leading to conflicting results.
- A machine learning model was developed using data from 72 patients in Brazil, identifying five key lab biomarkers that accurately predict severe COVID-19, potentially enhancing clinical decision-making.
Objectives: In this preliminary study we investigated cellular and humoral immune responses to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antigens in blood samples from 14 recovered coronavirus disease 2019 (COVID-19) patients and compared them to those in samples from 12 uninfected/unvaccinated volunteers.
Methods: Cellular immunity was assessed by intracellular detection of IFN-γ in CD3+ T lymphocytes after stimulation with SARS-CoV-2 spike (S1), nucleocapsid (NC), or receptor-binding domain (RBD) recombinant proteins or overlapping peptide pools covering the sequence of SARS-CoV-2 spike, membrane and nucleocapsid regions. The humoral response was examined by ELISAs and/or chemiluminescence assays for the presence of serum IgG antibodies directed to SARS-CoV-2 proteins.