RESUMO

Syphilis is a sexually transmitted infection (STI) caused by the spiral bacterium Treponema pallidum. Diagnosis is based on epidemiology, clinical and serology, but serodiagnosis is challenging because distinct clinical forms of the infection may influence serological performance. Several recombinant Treponema pallidum-proteins have already been tested for syphilis diagnosis and they are critical to achieve high accuracy in serological testing. A total of 647 samples were included in the study: 180 T. pallidum-positive samples, 191 T. pallidum-negative samples and 276 sera from individuals infected with unrelated diseases. The diagnostic potential was validated by analysis of ROC curves. For the indirect ELISA, TpN17 (100%) and TmpA (99%) showed excellent AUC values. Sensitivity values were 97.2% for TpN17 and 90.6% for TmpA, while specificity was 100% for both molecules. According to the clinical phase, TmpA ranged from 84% to 97%, with the highest value for secondary syphilis. TpN17 was 100% sensitive for the primary and secondary stages and 93.2% for recent latent syphilis. All clinical phases achieved 100% specificity. Accuracy values showed that TmpA (> 95%) and TpN17 (> 98%) presented high diagnostic accuracy for all clinical stages of syphilis. Cross-reactivity was only observed in one sample positive for Chagas disease (1.5%), when TpN17 was evaluated. On the other hand, TmpA showed reactivity for two samples positive for Chagas disease (3.1%), one sample positive for HBV (1.25%), two samples positive for HIV (9.5%) and one sample positive for HTLV (1.6%). The TmpA antigen's performance was evaluated in multiple studies for syphilis diagnosis, corroborating our findings. However, TpN17 sensitivity values have ranged in other studies. According to clinical stages of the infection, our findings obtained close performance values.

RESUMO

Droughts threaten water resources, agriculture, socio-economic activities and the population at the global and regional level, so identifying areas with homogeneous drought behaviors is an important consideration in improving the management of water resources. The objective of this study is to identify homogenous zones over Paraíba State in relation to the state, duration and severity of droughts that have occurred over the last 20 years (1998-2017) using hierarchical cluster analysis based on both gauge-measured and Tropical Rainfall Measuring Mission (TRMM) estimated rainfall data (TMPA 3B42). The drought series were calculated using the Standardized Precipitation Index (SPI) based on eight time scales and were grouped according to drought state, duration and severity time series. The integrated results of state, duration and severity of droughts indicate that there is a basis for dividing Paraíba State into two major regions (a) Zone I, formed by Mata Paraibana and Agreste Paraibano, and (b) Zone II, composed by Borborema and Sertão Paraibano. This division is evident when assessing short-term droughts, but in the case of long-term droughts, Paraíba State has a high similarity in terms of drought state, duration, and severity. Factors such as proximity to the ocean, active climatic systems, and the local relief configuration were identified as influencing the drought regime. Finally, it is concluded that TMPA rainfall estimates represent a valuable source of data to regionalize and identify drought patterns over this part of Brazil and that other studies of this type should be carried out to monitor these phenomena based on other satellite-based rainfall data, including the Global Precipitation Mission (GPM).

Assuntos

Secas , Recursos Hídricos , Brasil , Análise por Conglomerados

RESUMO

Satellite precipitation products (SPPs) provide alternative precipitation data for regions with sparse rain gauge measurements. However, SPPs are subject to different types of error that need correction. Most SPP bias correction methods use the statistical properties of the rain gauge data to adjust the corresponding SPP data. The statistical adjustment does not make it possible to correct the pixels of SPP data for which there is no rain gauge data. The solution proposed in this article is to correct the daily SPP data for the Guiana Shield using a novel two set approach, without taking into account the daily gauge data of the pixel to be corrected, but the daily gauge data from surrounding pixels. In this case, a spatial analysis must be involved. The first step defines hydroclimatic areas using a spatial classification that considers precipitation data with the same temporal distributions. The second step uses the Quantile Mapping bias correction method to correct the daily SPP data contained within each hydroclimatic area. We validate the results by comparing the corrected SPP data and daily rain gauge measurements using relative RMSE and relative bias statistical errors. The results show that analysis scale variation reduces rBIAS and rRMSE significantly. The spatial classification avoids mixing rainfall data with different temporal characteristics in each hydroclimatic area, and the defined bias correction parameters are more realistic and appropriate. This study demonstrates that hydroclimatic classification is relevant for implementing bias correction methods at the local scale.