Hand-carried ultrasound performed at bedside in cardiology inpatient setting – a comparative study with comprehensive echocardiography

BACKGROUND: Hand-carried ultrasound (HCU) devices have been demonstrated to improve the diagnosis of cardiac diseases over physical examination, and have the potential to broaden the versatility in ultrasound application. The role of these devices in the assessment of hospitalized patients is not completely established. In this study we sought to perform a direct comparison between bedside evaluation using HCU and comprehensive echocardiography (CE), in cardiology inpatient setting. METHODS: We studied 44 consecutive patients (mean age 54 ± 18 years, 25 men) who underwent bedside echocardiography using HCU and CE. HCU was performed by a cardiologist with level-2 training in the performance and interpretation of echocardiography, using two-dimensional imaging, color Doppler, and simple calliper measurements. CE was performed by an experienced echocardiographer (level-3 training) and considered as the gold standard. RESULTS: There were no significant differences in cardiac chamber dimensions and left ventricular ejection fraction determined by the two techniques. The agreement between HCU and CE for the detection of segmental wall motion abnormalities was 83% (Kappa = 0.58). There was good agreement for detecting significant mitral valve regurgitation (Kappa = 0.85), aortic regurgitation (kappa = 0.89), and tricuspid regurgitation (Kappa = 0.74). A complete evaluation of patients with stenotic and prosthetic dysfunctional valves, as well as pulmonary hypertension, was not possible using HCU due to its technical limitations in determining hemodynamic parameters. CONCLUSION: Bedside evaluation using HCU is helpful for assessing cardiac chamber dimensions, left ventricular global and segmental function, and significant valvular regurgitation. However, it has limitations regarding hemodynamic assessment, an important issue in the cardiology inpatient setting

Segregation distortion of wild-type alleles at the Machado-Joseph disease locus: a study in normal families from the Azores islands (Portugal)

Machado-Joseph disease (MJD) is caused by an expansion of a triplet repeat with a CAG motif at the ATXN3 gene. The putative segregation ratio distortion (SRD) of alleles can play an important role in the non-Mendelian behaviour of triplet repeat loci. To study the stability and infer the segregation patterns of wild-type MJD alleles, the size of the (CAG)(n) tract was analysed in 102 normal sibships, representing 428 meioses. No mutational events were detected during the transmission of alleles. Segregation analysis showed that the smaller alleles were preferentially transmitted (56.9%). Considering maternal meioses alone, such preference was still detected (55.7%) but without statistical significance. A positive correlation was observed for the difference in length between the two alleles constituting the transmitters' genotype (D) and the frequency of transmission of the smaller alleles. The results suggest that small D values are not enough to modify the probability of allele transmission. When transmissions involving genotypes with D <or= 2 were excluded, SRD in favour of the smaller allele became significant for both maternal and paternal transmissions. Therefore, the genotypic composition of the transmitters in a sample to be analysed should influence the ability to detect SRD, acting as a confounding factor.This work was supported by ‘‘Projecto Regional Integrado— DMJ (PRI-DMJ)’’ (funded by Regional Government of the Azores), ‘‘Construyendo una Bio-Región Europea—Biopolis’’ (05/MAC/2.3/ C14, funded by PIC Interreg III B, Azores—Madeira—Canarias) and MANSEEBMO (MI.2.1/004/2005, funded by ‘‘Direcção Regional da Ciência e Tecnologia’’). CB (SFRH/BD/21875/2005) is a recipient of a Ph.D. grant, and RM (SFRH/BPD/32473/2006) and CS (SFRH/BPD/ 20944/2004) are postdoctoral fellows from ‘‘Fundação para a Ciência e a Tecnologia’’ (FCT)

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space. While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes, vast areas of the tropics remain understudied. In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity, but it remains among the least known forests in America and is often underrepresented in biodiversity databases. To worsen this situation, human-induced modifications may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge, it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

SARS-CoV-2 introductions and early dynamics of the epidemic in Portugal

Genomic surveillance of SARS-CoV-2 in Portugal was rapidly implemented by the National Institute of Health in the early stages of the COVID-19 epidemic, in collaboration with more than 50 laboratories distributed nationwide. Methods By applying recent phylodynamic models that allow integration of individual-based travel history, we reconstructed and characterized the spatio-temporal dynamics of SARSCoV-2 introductions and early dissemination in Portugal. Results We detected at least 277 independent SARS-CoV-2 introductions, mostly from European countries (namely the United Kingdom, Spain, France, Italy, and Switzerland), which were consistent with the countries with the highest connectivity with Portugal. Although most introductions were estimated to have occurred during early March 2020, it is likely that SARS-CoV-2 was silently circulating in Portugal throughout February, before the first cases were confirmed. Conclusions Here we conclude that the earlier implementation of measures could have minimized the number of introductions and subsequent virus expansion in Portugal. This study lays the foundation for genomic epidemiology of SARS-CoV-2 in Portugal, and highlights the need for systematic and geographically-representative genomic surveillance.We gratefully acknowledge to Sara Hill and Nuno Faria (University of Oxford) and Joshua Quick and Nick Loman (University of Birmingham) for kindly providing us with the initial sets of Artic Network primers for NGS; Rafael Mamede (MRamirez team, IMM, Lisbon) for developing and sharing a bioinformatics script for sequence curation (https://github.com/rfm-targa/BioinfUtils); Philippe Lemey (KU Leuven) for providing guidance on the implementation of the phylodynamic models; Joshua L. Cherry (National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health) for providing guidance with the subsampling strategies; and all authors, originating and submitting laboratories who have contributed genome data on GISAID (https://www.gisaid.org/) on which part of this research is based. The opinions expressed in this article are those of the authors and do not reflect the view of the National Institutes of Health, the Department of Health and Human Services, or the United States government. This study is co-funded by Fundação para a Ciência e Tecnologia and Agência de Investigação Clínica e Inovação Biomédica (234_596874175) on behalf of the Research 4 COVID-19 call. Some infrastructural resources used in this study come from the GenomePT project (POCI-01-0145-FEDER-022184), supported by COMPETE 2020 - Operational Programme for Competitiveness and Internationalisation (POCI), Lisboa Portugal Regional Operational Programme (Lisboa2020), Algarve Portugal Regional Operational Programme (CRESC Algarve2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF), and by Fundação para a Ciência e a Tecnologia (FCT).info:eu-repo/semantics/publishedVersio