Medeirosterkelsen4862

The coronavirus disease 2019 pandemic has exposed disproportionate health inequities among underserved populations, including refugees. Public safety net healthcare systems play a critical role in facilitating access to care for refugees and informing coordinated public health prevention and mitigation efforts during a pandemic.

This study aimed to evaluate the prevalence ratios of severe acute respiratory syndrome coronavirus 2 infection between refugee women and nonrefugee parturient patients admitted to the hospital for delivery. Here, we suspected that the burden of infection was disproportionately distributed across refugee communities that may act as sentinels for community outbreaks.

A cross-sectional study was conducted examining parturient women admitted to the maternity unit between May 6, 2020, and July 22, 2020, when universal testing for severe acute respiratory syndrome coronavirus 2 was first employed. Risk factors for severe acute respiratory syndrome 2 positivity were ascertained, disag utility of universal screening in mounting a rapid response to an evolving pandemic and how we can better serve refugee communities. Focused response may help achieve more equitable care related to severe acute respiratory syndrome 2 among vulnerable communities. The identification of such populations may help mitigate the spread of the disease and facilitate a timely, culturally, and linguistically enhanced public health response.

The severe acute respiratory syndrome coronavirus 2 outbreak has disproportionately affected refugee populations. This study highlighted the utility of universal screening in mounting a rapid response to an evolving pandemic and how we can better serve refugee communities. Focused response may help achieve more equitable care related to severe acute respiratory syndrome 2 among vulnerable communities. The identification of such populations may help mitigate the spread of the disease and facilitate a timely, culturally, and linguistically enhanced public health response.

Seasonal human coronaviruses (hCoVs) broadly circulate in humans. Their epidemiology and effect on the spread of emerging coronaviruses has been neglected thus far. We aimed to elucidate the epidemiology and burden of disease of seasonal hCoVs OC43, NL63, and 229E in patients in primary care and hospitals in Belgium between 2015 and 2020.

We retrospectively analysed data from the national influenza surveillance networks in Belgium during the winter seasons of 2015-20. LJI308 Respiratory specimens were collected through the severe acute respiratory infection (SARI) and the influenza-like illness networks from patients with acute respiratory illness with onset within the previous 10 days, with measured or reported fever of 38°C or greater, cough, or dyspnoea; and for patients admitted to hospital for at least one night. Potential risk factors were recorded and patients who were admitted to hospital were followed up for the occurrence of complications or death for the length of their hospital stay. All samples were[This corrects the article DOI 10.1016/S2666-5247(21)00082-3.].[This corrects the article DOI 10.1016/S2666-5247(21)00084-7.].Exome and genome sequencing have proven to be effective tools for the diagnosis of neurodevelopmental disorders (NDDs), but large fractions of NDDs cannot be attributed to currently detectable genetic variation. This is likely, at least in part, a result of the fact that many genetic variants are difficult or impossible to detect through typical short-read sequencing approaches. Here, we describe a genomic analysis using Pacific Biosciences circular consensus sequencing (CCS) reads, which are both long (>10 kb) and accurate (>99% bp accuracy). We used CCS on six proband-parent trios with NDDs that were unexplained despite extensive testing, including genome sequencing with short reads. We identified variants and created de novo assemblies in each trio, with global metrics indicating these datasets are more accurate and comprehensive than those provided by short-read data. In one proband, we identified a likely pathogenic (LP), de novo L1-mediated insertion in CDKL5 that results in duplication of exon 3, leading to a frameshift. In a second proband, we identified multiple large de novo structural variants, including insertion-translocations affecting DGKB and MLLT3, which we show disrupt MLLT3 transcript levels. We consider this extensive structural variation likely pathogenic. The breadth and quality of variant detection, coupled to finding variants of clinical and research interest in two of six probands with unexplained NDDs, support the hypothesis that long-read genome sequencing can substantially improve rare disease genetic discovery rates.Transcriptome prediction methods such as PrediXcan and FUSION have become popular in complex trait mapping. Most transcriptome prediction models have been trained in European populations using methods that make parametric linear assumptions like the elastic net (EN). To potentially further optimize imputation performance of gene expression across global populations, we built transcriptome prediction models using both linear and non-linear machine learning (ML) algorithms and evaluated their performance in comparison to EN. We trained models using genotype and blood monocyte transcriptome data from the Multi-Ethnic Study of Atherosclerosis (MESA) comprising individuals of African, Hispanic, and European ancestries and tested them using genotype and whole-blood transcriptome data from the Modeling the Epidemiology Transition Study (METS) comprising individuals of African ancestries. We show that the prediction performance is highest when the training and the testing population share similar ancestries regardless of the prediction algorithm used. While EN generally outperformed random forest (RF), support vector regression (SVR), and K nearest neighbor (KNN), we found that RF outperformed EN for some genes, particularly between disparate ancestries, suggesting potential robustness and reduced variability of RF imputation performance across global populations. When applied to a high-density lipoprotein (HDL) phenotype, we show including RF prediction models in PrediXcan revealed potential gene associations missed by EN models. Therefore, by integrating other ML modeling into PrediXcan and diversifying our training populations to include more global ancestries, we may uncover new genes associated with complex traits.