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Estudo comparativo de anticorpos IgG e IgE antileishmania como marcadores de infecção e doença em área endêmica de leishmaniose visceral na ilha de São Luis, MA no período de maio de 1999 a maio de 2000. A casuística foi composta de 1.016 menores de 16 anos. Destes, foram randomizados 85 para dosagem de IgE antileishmania pelo ELISA. A prevalência de soropositividade por IgG foi de 17,1% (174 menores) e 0,4% da população evoluiu para leishmaniose visceral doença. A positividade para IgE antileishmania, foi de 43,5% dos 85 indivíduos estudados. Dos 7 casos de calazar no passado analisados, observou-se que todos apresentavam positividade para IgG e 4 (57,14%) permaneciam positivos para IgE, mesmo 7 anos após a cura. Os 3 menores com leishmaniose visceral doença, apresentaram positividade para IgG e IgE antileishmania. A detecção de IgE antileishmania demonstrou ser marcador de infecção para Leishmania chagasi em área endêmica, não sendo entretanto, marcador de doença.

Abstract Automated database search engines are one of the fundamental engines of high-throughput proteomics enabling daily identifications of hundreds of thousands of peptides and proteins from tandem mass (MS/MS) spectrometry data. Nevertheless, this automation also makes it humanly impossible to manually validate the vast lists of resulting identifications from such high-throughput searches. This challenge is usually addressed by using a Target-Decoy Approach (TDA) to impose an empirical False Discovery Rate (FDR) at a pre-determined threshold x% with the expectation that at most x% of the returned identifications would be false positives. But despite the fundamental importance of FDR estimates in ensuring the utility of large lists of identifications, there is surprisingly little consensus on exactly how TDA should be applied to minimize the chances of biased FDR estimates. In fact, since less rigorous TDA/FDR estimates tend to result in more identifications (at higher 'true' FDR), there is often little incentive to enforce strict TDA/FDR procedures in studies where the major metric of success is the size of the list of identifications and there are no follow up studies imposing hard cost constraints on the number of reported false positives. Here we address the problem of the accuracy of TDA estimates of empirical FDR. Using MS/MS spectra from samples where we were able to define a factual FDR estimator of 'true' FDR we evaluate several popular variants of the TDA procedure in a variety of database search contexts. We show that the fraction of false identifications can sometimes be over 10× higher than reported and may be unavoidably high for certain types of searches. In addition, we further report that the two-pass search strategy seems the most promising database search strategy. While unavoidably constrained by the particulars of any specific evaluation dataset, our observations support a series of recommendations towards maximizing the number of resulting identifications while controlling database searches with robust and reproducible TDA estimation of empirical FDR.