RESUMO
ObjectiveSerum antibody levels have been linked to immune protection in SARS-CoV-2 infection. After exposure to the virus and/or vaccination there is an increase in serum antibody titers followed by progressive non-linear waning of antibody levels. Our aim was to find out if this waning of antibody titers would adjust to a mathematical model. MethodsWe studied serum anti-RBD (receptor binding domain) IgG antibody titers over a ten-month period in a cohort of 54 health-care workers who were either never infected with SARS-CoV-2 (naive, nHCWs group, n = 27) or previously infected (experienced, eHCWs group, n = 27) with the virus after the second dose of the BNT162b2 mRNA vaccine. We have selected a risk threshold of 1000 UA/ml anti-RBD Ab titer for symptomatic infection based on the upper titer threshold for those volunteers who suffered infection prior to the omicron outbreak. Two mathematical models, exponential and potential, were used to quantify antibody waning kinetics and the relative quality of the goodness of fit to the data between both models was compared using the Akaike Information Criterion. ResultsWe found that the waning of anti-RBD IgG antibody levels adjusted significantly to both exponential and potential models in all participants from both the naive and experienced groups. Moreover, the waning slopes were significantly more pronounced for the naive when compared to the experienced health-care workers. In the nHCWs group, titers would descend below this 1000-units threshold at a median of 210.6 days (IQ range: 74,2). However, for the eHCWs group, the risk threshold would be reached at 440.0 days (IQ range: 135,2) post-vaccination. ConclusionsThe goodness of the fit of the anti-RBD IgG antibody waning would allow us to predict when the antibody titers would fall below an established threshold in both naive and previously infected subjects.
RESUMO
OBJECTIVES: The main goal of this study was to accurately detect azole resistance in species of the Aspergillus fumigatus complex by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). METHODS: Identification of isolates (n = 868) was done with MALDI-TOF MS using both commercial and in-house libraries. To determine azole susceptibility, the EUCAST E.Def. 9.3.2 method was applied as the reference standard. Identification of resistant isolates was confirmed by DNA sequence analysis. Protein spectra obtained by MALDI-TOF MS were analysed to differentiate species within the A. fumigatus complex and to detect azole-resistant A. fumigatus sensu stricto isolates. RESULTS: Correct discrimination of A. fumigatus sensu stricto from cryptic species was accomplished in 100% of the cases applying principal component analysis (PCA) to protein spectra generated by MALDI-TOF MS. Furthermore, a specific peak (4586 m/z) was found to be present only in cryptic species. The application of partial least squares (PLS) discriminant analysis allowed 98.43% (±0.038) discrimination between susceptible and azole-resistant A. fumigatus sensu stricto isolates. Finally, based on PLS and SVM, A. fumigatus sensu stricto isolates with different cyp51A gene mutations were correctly clustered in 91.5% of the cases. CONCLUSIONS: MALDI-TOF MS combined with peak analysis is a novel tool that allows the differentiation of A. fumigatus sensu stricto from other species within the A. fumigatus complex, as well as the detection of azole-resistant A. fumigatus sensu stricto. Although further studies are still needed, the results reported here show the great potential of MALDI-TOF and machine learning for the rapid detection of azole-resistant Aspergillus fumigatus isolates from clinical origins.
