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

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

Development of a correlated finite element dynamic model of a complete aero engine

SIGLELD:8019.3153(PNR--90081). / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Assessing trends in biodiversity over space and time using the example of British breeding birds

Partitioning biodiversity change spatially and temporally is required for effective management, both to determine whether action is required and whether it should be applied at a regional level or targeted more locally. As biodiversity is a multifaceted concept, comparative analyses of different indices, focussing on different components of biodiversity change (evenness vs. abundance), give better information than a single headline index. We model changes in the spatial and temporal distribution of British breeding birds using generalized additive models applied to count data collected between 1994 and 2011. Abundance estimates, accounting for differences in detectability, are then used in community-specific (farmland and woodland) biodiversity indices. Temporal trends in biodiversity, and change points in those trends, are assessed at different spatial scales. The geometric mean of relative abundance, a headline indicator of biodiversity change, is assessed together with a goodness-of-fit evenness measure focussing separately on the rare and common species in the communities. Our analysis reveals predominantly declining trends in biodiversity indices for farmland and woodland bird communities in southern and eastern England, perhaps signalling environmental deterioration in this part of the country. Conversely, our results also show generally more positive trends in the north of Britain, consistent with north-south gradient expectations from the effects of climate change. We also reveal predominantly positive changes in evenness for the common species and negative changes in evenness for the rarer species in the communities, consistent with previously documented homogenization in bird communities. Synthesis and applications. Bird populations are seen as good indicators of ecosystem health, and trends for different communities can be indicative of wider biodiversity changes within their respective habitats. However, temporal trends in biodiversity at the national level may miss opposing trends occurring at different locations within the nation. We develop methods that allow assessment of how temporal trends vary spatially and whether these trends differ for the rare and common species in the respective communities. Our methods may be used to test hypotheses about the processes that generate the trends. Bird populations are seen as good indicators of ecosystem health, and trends for different communities can be indicative of wider biodiversity changes within their respective habitats. However, temporal trends in biodiversity at the national level may miss opposing trends occurring at different locations within the nation. We develop methods that allow assessment of how temporal trends vary spatially and whether these trends differ for the rare and common species in the respective communities. Our methods may be used to test hypotheses about the processes that generate the trends

Assessing trends in biodiversity over space and time using the example of British breeding birds

Partitioning biodiversity change spatially and temporally is required for effective management, both to determine whether action is required and whether it should be applied at a regional level or targeted more locally. As biodiversity is a multifaceted concept, comparative analyses of different indices, focussing on different components of biodiversity change (evenness vs. abundance), give better information than a single headline index. We model changes in the spatial and temporal distribution of British breeding birds using generalized additive models applied to count data collected between 1994 and 2011. Abundance estimates, accounting for differences in detectability, are then used in community-specific (farmland and woodland) biodiversity indices. Temporal trends in biodiversity, and change points in those trends, are assessed at different spatial scales. The geometric mean of relative abundance, a headline indicator of biodiversity change, is assessed together with a goodness-of-fit evenness measure focussing separately on the rare and common species in the communities. Our analysis reveals predominantly declining trends in biodiversity indices for farmland and woodland bird communities in southern and eastern England, perhaps signalling environmental deterioration in this part of the country. Conversely, our results also show generally more positive trends in the north of Britain, consistent with north-south gradient expectations from the effects of climate change. We also reveal predominantly positive changes in evenness for the common species and negative changes in evenness for the rarer species in the communities, consistent with previously documented homogenization in bird communities. Synthesis and applications. Bird populations are seen as good indicators of ecosystem health, and trends for different communities can be indicative of wider biodiversity changes within their respective habitats. However, temporal trends in biodiversity at the national level may miss opposing trends occurring at different locations within the nation. We develop methods that allow assessment of how temporal trends vary spatially and whether these trends differ for the rare and common species in the respective communities. Our methods may be used to test hypotheses about the processes that generate the trends. Bird populations are seen as good indicators of ecosystem health, and trends for different communities can be indicative of wider biodiversity changes within their respective habitats. However, temporal trends in biodiversity at the national level may miss opposing trends occurring at different locations within the nation. We develop methods that allow assessment of how temporal trends vary spatially and whether these trends differ for the rare and common species in the respective communities. Our methods may be used to test hypotheses about the processes that generate the trends

How should regional biodiversity be monitored?

We consider quantification of biodiversity in the context of targets set by the Convention on Biological Diversity. Implicit in such targets is a requirement to monitor biodiversity at a regional level. Few monitoring schemes are designed with these targets in mind. Monitored sites are typically not selected to be representative of a wider region, and measures of biodiversity are often biased by a failure to account for varying detectability among species and across time. Precision is often not adequately quantified. We review methods for quantifying the biodiversity of regions, consider issues that should be addressed in designing and evaluating a regional monitoring scheme, and offer a practical guide to what types of survey are appropriate for addressing different objectives for biodiversity monitoring

Efficacy and Safety of Topical Hypericin Photodynamic Therapy for Early-Stage Cutaneous T-Cell Lymphoma (Mycosis Fungoides): The FLASH Phase 3 Randomized Clinical Trial

Importance: Given that mycosis fungoides-cutaneous T-cell lymphoma (MF/CTCL) is chronic, there is a need for additional therapies with minimal short- and long-term adverse effects. Topical synthetic hypericin ointment, 0.25%, activated with visible light is a novel, nonmutagenic photodynamic therapy (PDT). Objectives: To determine the efficacy and safety of topical synthetic hypericin ointment, 0.25%, activated with visible light as a nonmutagenic PDT in early-stage MF/CTCL. Design, Settings, and Participants: This was a multicenter, placebo-controlled, double-blinded, phase 3 randomized clinical trial (FLASH study) conducted from December 2015 to November 2020 at 39 academic and community-based US medical centers. Participants were adults (≥18 years) with early-stage (IA-IIA) MF/CTCL. Interventions: In cycle 1, patients were randomized 2:1 to receive hypericin or placebo to 3 index lesions twice weekly for 6 weeks. In cycle 2, all patients received the active drug for 6 weeks to index lesions. In cycle 3 (optional), both index and additional lesions received active drug for 6 weeks. Main Outcomes and Measures: The primary end point was index lesion response rate (ILRR), defined as 50% or greater improvement in modified Composite Assessment of Index Lesion Severity (mCAILS) score from baseline after 6 weeks of therapy for cycle 1. For cycles 2 and 3, open label response rates were secondary end points. Adverse events (AEs) were assessed at each treatment visit, after each cycle, and then monthly for 6 months. Data analyses were performed on December 21, 2020. Results: The study population comprised 169 patients (mean [SD] age, 58.4 [16.0] years; 96 [57.8%] men; 120 [72.3%] White individuals) with early-stage MF/CTCL. After 6 weeks of treatment, hypericin PDT was more effective than placebo (cycle 1 ILRR, 16% vs 4%; P =.04). The ILRR increased to 40% in patients who received 2 cycles of hypericin PDT (P <.001 vs cycle 1 hypericin) and to 49% after 3 cycles (P <.001 vs cycle 1 hypericin). Significant clinical responses were observed in both patch and plaque type lesions and were similar regardless of age, sex, race, stage IA vs IB, time since diagnosis, and number of prior therapies. The most common treatment-related AEs were mild local skin (13.5%-17.3% across cycles 1-3 vs 10.5% for placebo in cycle 1) and application-site reactions (3.2%-6.9% across cycles 1-3 vs 4% for placebo in cycle 1). No drug-related serious AEs occurred. Conclusion and Relevance: The findings of this randomized clinical trial indicate that synthetic hypericin PDT is effective in early-stage patch and plaque MF/CTCL and has a favorable safety profile. Trial Registration: ClinicalTrials.gov Identifier: NCT02448381. © 2022 American Medical Association. All rights reserved.12 month embargo; published online: 20 July 2022This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Planning and Conducting a Pharmacogenetics Association Study

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/169261/1/cpt2270.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/169261/2/cpt2270_am.pd