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CCL3, CCL5, IL-15, IL-1Ra and VEGF compose a reliable algorithm to discriminate classes of adverse events following 17DD-YF primary vaccination according to cause-specific definitions

2021; Elsevier BV; Volume: 39; Issue: 31 Linguagem: Inglês

10.1016/j.vaccine.2021.05.101

1873-2518

Jordana Rodrigues Barbosa Fradico, Ana Carolina Campi‐Azevedo, Vanessa Peruhype-Magalhães, Jordana Grazziela Alves Coelho-dos-Reis, Elaine Spezialli Faria, Betânia Paiva Drumond, Izabela Maurício de Rezende, Janaína Fonseca Almeida, Roberta Barros da Silva, Josiane Dias Gusmão, Eva Lídia Arcoverde Medeiros, Regina C. M. Rodrigues, José Geraldo Leite Ribeiro, Maira Alves Pereira, Marcos Vinícius Ferreira Silva, Marília Lima Cruz Rocha, Talita Adelino, Felipe Campos de Melo Iani, Glauco Carvalho Pereira, Eder Gatti Fernandes, Maria Auxiliadora‐Martins, Valéria Valim, Janaína Fonseca Almeida Souza, Laurence Rodrigues do Amaral, Alessandro Pecego Martins Romano, Daniel Ramos, Sandra Maria Deotti Carvalho, Francieli Fontana Sutile Tardetti Fantinato, Rodrigo Fabiano do Carmo Said, Andréa Teixeira−Carvalho, Olindo Assis Martins‐Filho,

interferon and immune responses

In the present study, a range of serum biomarkers were quantified in suspected cases of adverse events following YF immunization (YEL-AEFI) to propose a reliable laboratorial algorithm to discriminate confirmed YEL-AEFI ("A1" class) from cases with other illnesses ("C" class). Our findings demonstrated that increased levels of CXCL8, CCL2, CXCL10, IL-1β, IL-6 and TNF-α were observed in YEL-AEFI ("A1" and "C" classes) as compared to primary vaccines without YEL-AEFI [PV(day 3–28)] and reference range (RR) controls. Notably, increased levels of CCL3, CCL4, CCL2, CCL5, IL-1β, IL-15, IL-1Ra and G-CSF were found in "A1" as compared to "C" class. Venn diagrams analysis allowed the pre-selection of biomarkers for further analysis of performance indices. Data demonstrated that CCL3, CCL5, IL-15 and IL-1Ra presented high global accuracy (AUC = 1.00) to discriminate "A1" from "C". Decision tree was proposed with a reliable algorithm to discriminate YEL-AEFI cases according to cause-specific definitions with outstanding overall accuracy (91%). CCL3, CCL5, IL-15 and IL-1Ra appears as root attributes to identify "A1" followed by VEGF as branch nodes to discriminate Wild Type YFV infection ("C(WT-YFV)") from cases with other illnesses ("C*"). Together, these results demonstrated the applicability of serum biomarker measurements as putative parameters towards the establishment of accurate laboratorial tools for complementary differential diagnosis of YEL-AEFI cases.