30 research outputs found

    Abstracts of presentations on plant protection issues at the xth international congress of virology: August 11-16,1996 Binyanei haOoma, Jerusalem, Israel Part 2 Plenary Lectures

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    Whole-genome sequencing reveals host factors underlying critical COVID-19

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    Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2–4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

    Whole-genome sequencing reveals host factors underlying critical COVID-19

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    Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

    A dissection method for determining the gut contents of calanoid copepods

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    Volume: 116Start Page: 129End Page: 13

    A randomized controlled crossover trial evaluating differential responses to antihypertensive drugs (used as mono- or dual therapy) on the basis of ethnicity: The comparIsoN oF Optimal Hypertension RegiMens; part of the Ancestry Informative Markers in HYpertension program—AIM-HY INFORM trial

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    BACKGROUND: Ethnicity, along with a variety of genetic and environmental factors, is thought to influence the efficacy of antihypertensive therapies. Current UK guidelines use a "black versus white" approach; in doing so, they ignore the United Kingdom's largest ethnic minority: Asians from South Asia.STUDY DESIGN: The primary purpose of the AIM-HY INFORM trial is to identify potential differences in response to antihypertensive drugs used as mono- or dual therapy on the basis of self-defined ethnicity. A multicenter, prospective, open-label, randomized study with 2 parallel, independent trial arms (mono- and dual therapy), AIM-HY INFORM plans to enroll a total of 1,320 patients from across the United Kingdom. Those receiving monotherapy (n = 660) will enter a 3-treatment (amlodipine 10 mg od; lisinopril 20 mg od; chlorthalidone 25 mg od), 3-period crossover, lasting 24 weeks, whereas those receiving dual therapy (n = 660) will enter a 4-treatment (amlodipine 5 mg od and lisinopril 20 mg od; amlodipine 5 mg od and chlorthalidone 25 mg od; lisinopril 20 mg od and chlorthalidone 25 mg od; amiloride 10 mg od and chlorthalidone 25 mg od), 4-period crossover, lasting 32 weeks. Equal numbers of 3 ethnic groups (white, black/black British, and Asian/Asian British) will ultimately be recruited to each of the trial arms (ie, 220 participants per ethnic group per arm). Seated, automated, unattended, office, systolic blood pressure measured 8 weeks after each treatment period begins will serve as the primary outcome measure.CONCLUSION: AIM-HY INFORM is a prospective, open-label, randomized trial which aims to evaluate first- and second-line antihypertensive therapies for multiethnic populations
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