Longitudinal study of humoral immunity against SARS-CoV-2 of health professionals in Brazil: the impact of booster dose and reinfection on antibody dynamics

Introduction

The pandemic caused by SARS-CoV-2 has had a major impact on health systems. Vaccines have been shown to be effective in improving the clinical outcome of COVID-19, but they are not able to fully prevent infection and reinfection, especially that caused by new variants.

Methods

Here, we tracked for 450 days the humoral immune response and reinfection in 52 healthcare workers from Brazil. Infection and reinfection were confirmed by RT-qPCR, while IgM and IgG antibody levels were monitored by rapid test.

Results

Of the 52 participants, 19 (36%) got reinfected during the follow-up period, all presenting mild symptoms. For all participants, IgM levels dropped sharply, with over 47% of them becoming seronegative by the 60th day. For IgG, 90% of the participants became seropositive within the first 30 days of follow-up. IgG antibodies also dropped after this period reaching the lowest level on day 270 (68.5 ± 72.3, p<0.0001). Booster dose and reinfection increased the levels of both antibodies, with the interaction between them resulting in an increase in IgG levels of 130.3 arbitrary units.

Conclusions

Overall, our data indicate that acquired humoral immunity declines over time and suggests that IgM and IgG antibody levels are not associated with the prevention of reinfection.

Comparison of diagnostic performance of RT-qPCR, RT-LAMP and IgM/IgG rapid tests for detection of SARS-CoV-2 among healthcare workers in Brazil

BACKGROUND

COVID-19 has become a major public health problem after the outbreak caused by SARS-CoV-2 virus. Great efforts to contain COVID-19 transmission have been applied worldwide. In this context, accurate and fast diagnosis is essential.

METHODS

In this prospective study, we evaluated the clinical performance of three different RNA-based molecular tests - RT-qPCR (Charité protocol), RT-qPCR (CDC (USA) protocol) and RT-LAMP - and one rapid test for detecting anti-SARS-CoV-2 IgM and IgG antibodies.

RESULTS

Our results demonstrate that RT-qPCR using the CDC (USA) protocol is the most accurate diagnostic test among those evaluated, while oro-nasopharyngeal swabs are the most appropriate biological sample. RT-LAMP was the RNA-based molecular test with lowest sensitivity while the serological test presented the lowest sensitivity among all evaluated tests, indicating that the latter test is not a good predictor of disease in the first days after symptoms onset. Additionally, we observed higher viral load in individuals who reported more than 3 symptoms at the baseline. Nevertheless, viral load had not impacted the probability of testing positive for SARS-CoV-2.

CONCLUSION

Our data indicates that RT-qPCR using the CDC (USA) protocol in oro-nasopharyngeal swabs samples should be the method of choice to diagnosis COVID-19.

Genotypic correlation and path analysis in early and super-early maize genotypes

The aims of this study were to assess the linear relationships between agronomic and nutritional traits and to identify promising traits for indirect selection in early and super-early maturing maize genotypes. Two trials were run in the 2009/2010 agricultural year, each consisting of a randomized block design with three replications. One trial was run on 36 early maturing maize genotypes and the other on 22 super-early maturing genotypes. Six agronomic traits, 11 protein-nutritional traits, and four energetic-nutritional traits were measured. Error normality assumptions were verified using the Kolmogorov-Smirnov test and residual variance homogeneity assumptions using the Levene test. Analysis of variance and the F-test were run for each of the 21 traits. Next, the genotype correlation coefficient matrix was estimated for the 21 traits and each trial. Ridge path analysis was based on values of k = 0.00 and k = 0.10 on the diagonal of X'X correlation matrix, taking the nutritional traits as principal variables and agronomic traits as explanatory variables. The number of days from sowing to male flowering, the number of days from sowing to female flowering, plant height, ear insertion height, ear weight and grain yield can be used in indirect selection as indicators of grain nutritional quality.

Multicollinearity in canonical correlation analysis in maize

The objective of this study was to evaluate the effects of multicollinearity under two methods of canonical correlation analysis (with and without elimination of variables) in maize (Zea mays L.) crop. Seventy-six maize genotypes were evaluated in three experiments, conducted in a randomized block design with three replications, during the 2009/2010 crop season. Eleven agronomic variables (number of days from sowing until female flowering, number of days from sowing until male flowering, plant height, ear insertion height, ear placement, number of plants, number of ears, ear index, ear weight, grain yield, and one thousand grain weight), 12 protein-nutritional variables (crude protein, lysine, methionine, cysteine, threonine, tryptophan, valine, isoleucine, leucine, phenylalanine, histidine, and arginine), and 6 energetic-nutritional variables (apparent metabolizable energy, apparent metabolizable energy corrected for nitrogen, ether extract, crude fiber, starch, and amylose) were measured. A phenotypic correlation matrix was first generated among the 29 variables for each of the experiments. A multicollinearity diagnosis was later performed within each group of variables using methodologies such as variance inflation factor and condition number. Canonical correlation analysis was then performed, with and without the elimination of variables, among groups of agronomic and protein-nutritional, and agronomic and energetic-nutritional variables. The canonical correlation analysis in the presence of multicollinearity (without elimination of variables) overestimates the variability of canonical coefficients. The elimination of variables is an efficient method to circumvent multicollinearity in canonical correlation analysis.