Revised transthyretin Ile 122 allele frequency in African-Americans

The transthyretin (TTR) Ile 122 variant is associated with cardiac amyloidosis in individuals of African descent. To determine the prevalence of the allele encoding TTR Ile 122 in African-Americans, we have used PCR and restriction analysis to test DNA from African-Americans from various geographic areas, and found an allele frequency of 66/3376 (0.020), which is higher than the value we previously reported in a much smaller pilot study. Our data indicate that this TTR variant is present at equal frequency in African-Americans throughout the U.S., and suggest that this mutation may be a common, often unrecognized cause of cardiac disease in African-Americans. Received: 23 January 1996     

The discordance between the presence of identifiable p24 antigen in the blood and the isolation of HIV from the blood is not influenced by methodological factors

To clarify the biological, clinical and prognostic relevance of HIV isolation from plasma and explain the relation to p24 antigenemia, we studied these two markers in 67 anti-HIV positive subjects in different stages of the disease. The results suggest that discordances between these two parameters are not dependent on methodological problems and may be attributed to yet unexplained biological phenomena. Some hypotheses to explain the observed discrepancies are suggested.     

Using heterogeneous data to identify signatures of dengue outbreaks at fine spatio-temporal scales across Brazil

Dengue virus remains a significant public health challenge in Brazil, and seasonal preparation efforts are hindered by variable intra- and interseasonal dynamics. Here, we present a framework for characterizing weekly dengue activity at the Brazilian mesoregion level from 2010–2016 as time series properties that are relevant to forecasting efforts, focusing on outbreak shape, seasonal timing, and pairwise correlations in magnitude and onset. In addition, we use a combination of 18 satellite remote sensing imagery, weather, clinical, mobility, and census data streams and regression methods to identify a parsimonious set of covariates that explain each time series property. The models explained 54% of the variation in outbreak shape, 38% of seasonal onset, 34% of pairwise correlation in outbreak timing, and 11% of pairwise correlation in outbreak magnitude. Regions that have experienced longer periods of drought sensitivity, as captured by the “normalized burn ratio,” experienced less intense outbreaks, while regions with regular fluctuations in relative humidity had less regular seasonal outbreaks. Both the pairwise correlations in outbreak timing and outbreak trend between mesoresgions were best predicted by distance. Our analysis also revealed the presence of distinct geographic clusters where dengue properties tend to be spatially correlated. Forecasting models aimed at predicting the dynamics of dengue activity need to identify the most salient variables capable of contributing to accurate predictions. Our findings show that successful models may need to leverage distinct variables in different locations and be catered to a specific task, such as predicting outbreak magnitude or timing characteristics, to be useful. This advocates in favor of “adaptive models” rather than “one-size-fits-all” models. The results of this study can be applied to improving spatial hierarchical or target-focused forecasting models of dengue activity across Brazil.