COVID-19 Mortality and Case-Fatality Rates in Sergipe State, Northeast Brazil, From April to June 2020

Information on how coronavirus disease 2019 (COVID-19) mortality is related to population characteristics in low- and middle-income countries is still limited. We described the deaths from COVID-19 in Sergipe state, Northeast Brazil, from April 2 to June 27, 2020. For this purpose, we conducted a study composed of (i) a case series study of all deaths due to COVID-19 and (ii) a population-based study to verify the behavior of the mortality and case-fatality rates (CFR) related to COVID-19. Data from 605 deaths due to COVID-19 were used to describe the characteristics of individuals with the disease, as well as the differences in gender, age, and comorbidities. Additionally, population data were extracted to estimate the mortality and CFR by population stratum. We also performed an adjusted CFR analysis including a time lag of 14 days between the onset of symptoms and reporting deaths. Of the 605 patients included in this study, 321 (53.1%) were males and the median age was 67.0 years. Most patients (n = 447, 73.9%) who died from COVID-19 had at least one pre-existing clinical condition. The mortality rate was 29.3 deaths per 100,000 inhabitants and the crude CRF was 2.6% (95% CI 2.4–2.8). CFR was higher in males (3.1%, 95% CI 2.8–3.4; p < 0.001) and people aged =60 years (14.2%, 95% CI 13.0–15.6; p = 0.042). About 25% of patients died during the first 24-h post-hospital admission. The adjusted CFR for a 14-day time lag was ~2-fold higher than the crude CFR over the study period.We dedicated this article to all health professionals who are facing COVID-19. This study was part of the EpiSERGIPE project. NM thanks the Portuguese Foundation for Science and Technology under the Horizon 2020 Program (PTDC/PSI-GER/280 76/2017)

Hybridization in human evolution: Insights from other organisms

During the late Pleistocene, isolated lineages of hominins exchanged genes thus influencing genomic variation in humans in both the past and present. However, the dynamics of this genetic exchange and associated phenotypic consequences through time remain poorly understood. Gene exchange across divergent lineages can result in myriad outcomes arising from these dynamics and the environmental conditions under which it occurs. Here we draw from our collective research across various organisms, illustrating some of the ways in which gene exchange can structure genomic/phenotypic diversity within/among species. We present a range of examples relevant to questions about the evolution of hominins. These examples are not meant to be exhaustive, but rather illustrative of the diverse evolutionary causes/consequences of hybridization, highlighting potential drivers of human evolution in the context of hybridization including: influences on adaptive evolution, climate change, developmental systems, sex-differences in behavior, Haldane’s rule and the large X-effect, and transgressive phenotypic variation.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151330/1/evan21787.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151330/2/evan21787_am.pd

Framingham coronary heart disease risk score can be predicted from structural brain images in elderly subjects

Recent literature has presented evidence that cardiovascular risk factors (CVRF) play an important role on cognitive performance in elderly individuals, both those who are asymptomatic and those who suffer from symptoms of neurodegenerative disorders. Findings from studies applying neuroimaging methods have increasingly reinforced such notion. Studies addressing the impact of CVRF on brain anatomy changes have gained increasing importance, as recent papers have reported gray matter loss predominantly in regions traditionally affected in Alzheimer’s disease (AD) and vascular dementia in the presence of a high degree of cardiovascular risk. In the present paper, we explore the association between CVRF and brain changes using pattern recognition techniques applied to structural MRI and the Framingham score (a composite measure of cardiovascular risk largely used in epidemiological studies) in a sample of healthy elderly individuals. We aim to answer the following questions: is it possible to decode (i.e., to learn information regarding cardiovascular risk from structural brain images) enabling individual predictions? Among clinical measures comprising the Framingham score, are there particular risk factors that stand as more predictable from patterns of brain changes? Our main findings are threefold: (i) we verified that structural changes in spatially distributed patterns in the brain enable statistically significant prediction of Framingham scores. This result is still significant when controlling for the presence of the APOE 4 allele (an important genetic risk factor for both AD and cardiovascular disease). (ii) When considering each risk factor singly, we found different levels of correlation between real and predicted factors; however, single factors were not significantly predictable from brain images when considering APOE4 allele presence as covariate. (iii) We found important gender differences, and the possible causes of that finding are discussed