19 research outputs found

    A tumor DNA complex aberration index is an independent predictor of survival in breast and ovarian cancer

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    Complex focal chromosomal rearrangements in cancer genomes, also called "firestorms", can be scored from DNA copy number data. The complex arm-wise aberration index (CAAI) is a score that captures DNA copy number alterations that appear as focal complex events in tumors, and has potential prognostic value in breast cancer. This study aimed to validate this DNA-based prognostic index in breast cancer and test for the first time its potential prognostic value in ovarian cancer. Copy number alteration (CNA) data from 1950 breast carcinomas (METABRIC cohort) and 508 high-grade serous ovarian carcinomas (TCGA dataset) were analyzed. Cases were classified as CAAI positive if at least one complex focal event was scored. Complex alterations were frequently localized on chromosome 8p (n = 159), 17q (n = 176) and 11q (n = 251). CAAI events on 11q were most frequent in estrogen receptor positive (ER+) cases and on 17q in estrogen receptor negative (ER) cases. We found only a modest correlation between CAAI and the overall rate of genomic instability (GII) and number of breakpoints (r = 0.27 and r = 0.42, p <0.001). Breast cancer specific survival (BCSS), overall survival (OS) and ovarian cancer progression free survival (PUS) were used as clinical end points in Cox proportional hazard model survival analyses. CAAI positive breast cancers (43%) had higher mortality: hazard ratio (HR) of 1.94 (95%CI, 1.62-2.32) for BCSS, and of 1.49 (95%CI, 1.30-1.71) for OS. Representations of the 70-gene and the 21-gene predictors were compared with CAAI in multivariable models and CAAI was independently significant with a Cox adjusted HR of 1.56 (95%CI, 1.23-1.99) for ER+ and 1.55 (95%CI, 1.11-2.18) for ER disease. None of the expression-based predictors were prognostic in the ER subset. We found that a model including CAM and the two expression-based prognostic signatures outperformed a model including the 21-gene and 70-gene signatures but excluding CAAL Inclusion of CAAI in the clinical prognostication tool PREDICT significantly improved its performance. CAAI positive ovarian cancers (52%) also had worse prognosis: HRs of 1.3 (95%CI, 1.1-1.7) for PFS and 1.3 (95%CI, 1.1-1.6) for OS. This study validates CAM as an independent predictor of survival in both ER+ and ER breast cancer and reveals a significant prognostic value for CAAI in high-grade serous ovarian cancer. (C) 2014 The Authors. Published by Elsevier B.V. on behalf of Federation of European Biochemical Societies. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).Publisher PDFPeer reviewe

    A tumor DNA complex aberration index is an independent predictor of survival in breast and ovarian cancer.

    Get PDF
    Complex focal chromosomal rearrangements in cancer genomes, also called "firestorms", can be scored from DNA copy number data. The complex arm-wise aberration index (CAAI) is a score that captures DNA copy number alterations that appear as focal complex events in tumors, and has potential prognostic value in breast cancer. This study aimed to validate this DNA-based prognostic index in breast cancer and test for the first time its potential prognostic value in ovarian cancer. Copy number alteration (CNA) data from 1950 breast carcinomas (METABRIC cohort) and 508 high-grade serous ovarian carcinomas (TCGA dataset) were analyzed. Cases were classified as CAAI positive if at least one complex focal event was scored. Complex alterations were frequently localized on chromosome 8p (n = 159), 17q (n = 176) and 11q (n = 251). CAAI events on 11q were most frequent in estrogen receptor positive (ER+) cases and on 17q in estrogen receptor negative (ER-) cases. We found only a modest correlation between CAAI and the overall rate of genomic instability (GII) and number of breakpoints (r = 0.27 and r = 0.42, p < 0.001). Breast cancer specific survival (BCSS), overall survival (OS) and ovarian cancer progression free survival (PFS) were used as clinical end points in Cox proportional hazard model survival analyses. CAAI positive breast cancers (43%) had higher mortality: hazard ratio (HR) of 1.94 (95%CI, 1.62-2.32) for BCSS, and of 1.49 (95%CI, 1.30-1.71) for OS. Representations of the 70-gene and the 21-gene predictors were compared with CAAI in multivariable models and CAAI was independently significant with a Cox adjusted HR of 1.56 (95%CI, 1.23-1.99) for ER+ and 1.55 (95%CI, 1.11-2.18) for ER- disease. None of the expression-based predictors were prognostic in the ER- subset. We found that a model including CAAI and the two expression-based prognostic signatures outperformed a model including the 21-gene and 70-gene signatures but excluding CAAI. Inclusion of CAAI in the clinical prognostication tool PREDICT significantly improved its performance. CAAI positive ovarian cancers (52%) also had worse prognosis: HRs of 1.3 (95%CI, 1.1-1.7) for PFS and 1.3 (95%CI, 1.1-1.6) for OS. This study validates CAAI as an independent predictor of survival in both ER+ and ER- breast cancer and reveals a significant prognostic value for CAAI in high-grade serous ovarian cancer

    IMPACT-Global Hip Fracture Audit: Nosocomial infection, risk prediction and prognostication, minimum reporting standards and global collaborative audit. Lessons from an international multicentre study of 7,090 patients conducted in 14 nations during the COVID-19 pandemic

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    Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

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    IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570

    Envisioning UBC Food System : Asset Map 2.0

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    One of the indicators of food insecurity is food price volatility (FAO, 2011). For example, in Barbados an apple crumble goes for Can8.05perpie(LaBaguetteBarbados,n.d.).InCuba,onegoesforCan8.05 per pie (La Baguette Barbados, n.d.). In Cuba, one goes for Can9.12 (Cafeteria El Biky, 2018), while in Haiti, the same pie can cost up to Can$10.50! (Epi D’or, 2013). One might describe these as the pie rates of the Caribbean. But how does one find the best place to grab a tasty slice of pie? Or monitor supply chain threats to said pie’s availability? In this report, we invite you to join our swashbuckling crew of eager students and future food systems professionals as we set sail in (re)search of an answer to this question by investigating one possible solution: a Food Asset Map. Food Asset Mapping is an approach to addressing food security which employs an asset-based model to community food infrastructure and gaps, primarily in urban settings (Soma et al., 2021). This report showcases our research directed at advancing a just, secure and sustainable food system on the UBC Vancouver Campus by exploring the UBC Food Asset Map (FAM), a virtual tool which displays a multiplicity of food-related organizations, businesses, and initiatives at UBC Vancouver. Our two client groups UBC Wellbeing and the Social Ecological Economic Development Studies (SEEDS) Sustainability Program who currently oversee the UBC FAM determined that it needed clarification on its direction, scope, and management. Our team was recruited to assess the UBC FAM’s current uses and effectiveness in reaching its goals, as well as to provide guidance on its future direction. This work was conducted by a group of undergraduate student researchers within the course LFS 450: Land, Food, and Community III: Leadership in Campus Food System Sustainability. We believe strongly in the importance of community involvement being integrated into our work. Community-Based Action Research (CBAR) methodology provided a framework by which we coordinated our work, by highlighting the importance of localizing and contextualizing research within a community (Nasrollahi, 2015). We applied CBAR principles of collaboration among stakeholder, researchers, and community members; the implementation of different research methods as a means of knowledge collection; and analyzing data and knowledge holistically. Our research data were collected from peer reviewed literature and an environmental scan, in addition to primary data from a survey, focus groups, and one interview with the student-run initiative Campus Nutrition. With support from our teaching team, client groups, and a treasure chest of UBC resources at our disposal, we set about our work, the whole UBC Campus our oyster. Our survey (n = 108 )was administered via Qualtrics to a target sample of UBC Vancouver undergraduate students with the aim of drawing insight into the UBC student community’s opinions, knowledge, and possible uses for the UBC FAM. The focus groups and interview occurred over zoom with the participation of six UBC food systems stakeholders to collect data on their professional applications and perceptions of the UBC FAM. The data were then coded manually to identify recurring themes and ideas including: sustainability, equity, and food security; community collaboration; possible overlap between resources, and more. Through gathering these data, our team assembled an itinerary of adjustments that could be made to enhance the UBC Food Asset Map’s current aims and possible future direction changes. This included using the Food Asset Map as a way for students to locate meals on campus and mapping non-physical assets and relationships among food systems stakeholders. Our ultimate conclusion, however, is that these scope changes hold high potential to be addressed rather by nurturing initiatives and collaborations on the UBC Campus. Finally, our report outlines recommendations of ways to leverage and expand upon these collaborations. We also highlight the areas needing further research where our own investigation was constrained by scope or data limitation, which includes past and future user analytics, applications in campus planning, and relationship mapping. Disclaimer: “UBC SEEDS provides students with the opportunity to share the findings of their studies, as well as their opinions, conclusions and recommendations with the UBC community. The reader should bear in mind that this is a student project/report and is not an official document of UBC. Furthermore readers should bear in mind that these reports may not reflect the current status of activities at UBC. We urge you to contact the research persons mentioned in a report or the SEEDS Coordinator about the current status of the subject matter of a project/report.”Land and Food Systems, Faculty ofUnreviewedUndergraduat

    Topsoil physico-chemical properties from the UKCEH Countryside Survey, Great Britain, 2020

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    This dataset consists of measures of topsoil (0-15cm) physico-chemical properties from soils sampled from 49 1km squares across Great Britain in 2020 as part of a rolling soil and vegetation monitoring program of 500 1km squares, repeated every 5 years. The properties included are: soil organic matter (loss on ignition (LOI)), derived carbon concentration, total soil organic carbon (SOC), nitrogen, Olsen-phosphorous, pH, electrical conductivity, soil bulk density of fine earth and fine earth volumetric water content. The UKCEH Countryside Survey is a unique study or 'audit' of the natural resources of the UK's countryside. The sample sites are chosen from a stratified random sample, based on a 15 by 15 km grid of GB. Surveys have been carried out in 1978, 1984, 1990, 1998 and 2007 by the UK Centre for Ecology & Hydrology (UKCEH) and predecessors, with repeated visits to the majority of squares. The countryside is sampled and surveyed using rigorous scientific methods, allowing us to compare new results with those from previous surveys. In this way we can detect the gradual and subtle changes that occur in the UK's countryside over time. In addition to soil data, vegetation species data are also gathered by the current phase of Countryside Survey
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