Guercetti, JulianAlorda, MarcSappia, LucianoGalve, RogerDuran-Corbera, MacarenaPulido, DanielBerardi, GinevraRoyo, MiriamLacorna, AliciaMuñoz Gutiérrez, JoséPadilla, EduardoCastañeda, SilviaSendra, ElenaHorcajada Gallego, Juan PabloGutiérrez Gálvez, AgustínMarco Colás, SantiagoSalvador, J. PabloMarco, M. Pilar2025-05-022025-05-022025-02-212575-9108https://hdl.handle.net/2445/220776A multiplexed microarray chip (Immuno-μSARS2) aiming at providing information on the prognosis of the COVID-19 has been developed. The diagnostic technology records information related to the profile of the immunological response of patients infected by the SARS-CoV-2 virus. The diagnostic technology delivers information on the avidity of the sera against 28 different peptide epitopes and 7 proteins printed on a 25 mm2 area of a glass slide. The peptide epitopes (12–15 mer) derived from structural proteins (Spike and Nucleocapsid) have been rationally designed, synthesized, and used to develop Immuno-μSARS2 as a multiplexed and high-throughput fluorescent microarray platform. The analysis of 755 human serum samples (321 from PCR+ patients; 288 from PCR– patients; 115 from prepandemic individuals and classified as hospitalized, admitted to intensive-care unit (ICU), and exitus) from three independent cohorts has shown that the chips perform with a 98% specificity and 91% sensitivity identifying RT-PCR+ patients. Computational analysis utilized to correlate the immunological signatures of the samples analyzed indicate significant prediction rates against exitus conditions with 82% accuracy, ICU admissions with 80% accuracy, and 73% accuracy over hospitalization requirement compared to asymptomatic patients’ fingerprints. The miniaturized microarray chip allows simultaneous determination of 96 samples (24 samples/slide) in 90 min and requires only 10 μL of sera. The diagnostic approach presented for the first time here could have a great value in assisting clinicians in decision-making based on the information provided by the Immuno-μSARS2 regarding progression of the disease and could be easily implemented in diagnostics of other infectious diseases.14 p.application/pdfengcc-by (c) Guercetti, Julian et al., 2025http://creativecommons.org/licenses/by/3.0/es/COVID-19Aprenentatge automàticPèptidsCOVID-19Machine learningPeptidesinfo:eu-repo/semantics/article7574652025-05-02info:eu-repo/semantics/openAccess