Fig 1.
Map of Minas Gerais state in Brazil with the locations of the cities of Manhuaçu and Governador Valadares and associated rural villages, Córrego do Bernardo, Córrego do Melquíades and Chonim.
(http://pt.wikipedia.org/wiki/Predefini%C3%A7%C3%A3o:Mapa_de_localiza%C3%A7%C3%A3o/Minas_Gerais)
Table 1.
Characterization of the study groups.
Table 2.
The results of the CCA-ICT* are not affected by infection with intestinal protozoa.
Fig 2.
Performance of the CCA-ICT for diagnosing S. mansoni infection and receiver operating characteristic (ROC) curve analysis using two Kato-Katz thick smears (2KK test) as the reference.
A: Photograph of the different reactions possible with the CCA-ICT: negative, trace, weak (+), moderate (++) and strong (+++). B: Distribution of the CCA-ICT results for “2KK-NEG non-endemic area” (n = 41) and “2KK POS endemic area” (n = 48). C: The ROC curve and the area under the curve (AUC) for the performance of the CCA-ICT. The ROC curve established the optimal cutoff point as the trace score. Likelihood ratio positive (LR+); likelihood ratio negative (LR-); Positive Predictive Value (PPV); Negative Predictive Value (NPV).
Fig 3.
Performance comparisons between the 2KK test and the CCA-ICT with the cutoff set to trace.
A: Distribution of the CCA-ICT scores of “2KK-NEG non-endemic area” (n = 41), “2KK NEG endemic area” (n = 41) and “2KK POS endemic area” (n = 48) when the trace CCA-ICT score was considered negative. B: Association between the CCA-ICT scores, the individual mean egg per gram (epg) values (plotted on a logarithmic scale) and classes of intensity of infection. According to the 2KK test, the percentage for each class of infection intensity was 43.8% light, 39.6% moderate and 16.6% heavy. C: Correlation between epg values and CCA-ICT scores showing a positive association with the CCA-ICT scores.
Fig 4.
CCA-ICT performance with the cutoff set to negative.
A: Two-graph receiver operating characteristic (TG-ROC) curve showing the cutoff for the CCA-ICT set to the trace score. B: as for A but with the cutoff set to the negative score. C: ROC curve and the AUC for the performance of CCA-ICT with the cutoff set to the negative score using “2KK-NEG non-endemic area” (n = 41) and “2KK POS endemic area” (n = 48) as the reference groups. D: Distribution of the CCA-ICT scores of “2KK-NEG non-endemic area” (n = 41), “2KK NEG endemic area” (n = 41) and “2KK POS endemic area” (n = 48) when the cutoff was set to the negative CCA-ICT score. The area under the curve (AUC); Likelihood ratio positive (LR+); Likelihood ratio negative (LR-); Positive Predictive Value (PPV); Negative Predictive Value (NPV)
Fig 5.
Association between the CCA-ICT, SEA- ELISA and SWAP-ELISA data.
The ROC curves and AUC for the SEA-ELISA and SWAP-ELISA ELISA constructed for “2KK-NEG non-endemic area” (n = 41) and “2KK POS endemic area” (n = 48) are presented in A and B, respectively. The 2KK test was employed as the reference. C: Distribution of the CCA-ICT scores among those individuals categorized as “2KK SEA SWAP NEG” (n = 18) and “2KK SEA SWAP POS” (n = 38) when the trace CCA-ICT score was considered positive. The area under the curve (AUC); Likelihood ratio positive (LR+); Likelihood ratio negative (LR-); Positive Predictive Value (PPV); Negative Predictive Value (NPV).