11 research outputs found

    The Gaia mission

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    Gaia is a cornerstone mission in the science programme of the EuropeanSpace Agency (ESA). The spacecraft construction was approved in 2006, following a study in which the original interferometric concept was changed to a direct-imaging approach. Both the spacecraft and the payload were built by European industry. The involvement of the scientific community focusses on data processing for which the international Gaia Data Processing and Analysis Consortium (DPAC) was selected in 2007. Gaia was launched on 19 December 2013 and arrived at its operating point, the second Lagrange point of the Sun-Earth-Moon system, a few weeks later. The commissioning of the spacecraft and payload was completed on 19 July 2014. The nominal five-year mission started with four weeks of special, ecliptic-pole scanning and subsequently transferred into full-sky scanning mode. We recall the scientific goals of Gaia and give a description of the as-built spacecraft that is currently (mid-2016) being operated to achieve these goals. We pay special attention to the payload module, the performance of which is closely related to the scientific performance of the mission. We provide a summary of the commissioning activities and findings, followed by a description of the routine operational mode. We summarise scientific performance estimates on the basis of in-orbit operations. Several intermediate Gaia data releases are planned and the data can be retrieved from the Gaia Archive, which is available through the Gaia home page. http://www.cosmos.esa.int/gai

    Perioperative changes in cell‑free DNA for patients undergoing surgery for colon cancer

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    BACKGROUND: Various conditions with cellular decay are associated with elevated cell-free DNA (cfDNA). This study aimed to investigate if perioperatively measured cfDNA levels were associated with the surgical approach, complications, or recurrence. METHODS: Plasma was obtained from patients who underwent surgery for colon cancer at admission and at the time of discharge. Quantitative measurement of cfDNA was performed by amplifying two amplicons of 102 base pairs (bp) and 132 bp of Beta-2-Microglobulin (B2M) and Peptidyl-Prolyl cis–trans Isomerase A (PPIA), respectively. RESULTS: cfDNA was measured in 48 patients who underwent surgery for colonic cancer. Sixteen patients had recurrence during the follow-up period, fifteen developed a postoperative complication, and seventeen patients developed neither, acting as the control group. Postoperative cfDNA levels were significantly elevated from baseline samples, across all groups, with a median preoperatively B2M level of 48.3 alleles per mL and postoperatively of 220 alleles per mL and a median preoperatively level PPIA of 26.9 alleles per mL and postoperatively of 111.6 alleles per mL (p < 0.001 for B2M and p < 0.001 for PPIA). Postoperative levels of PPIA, but not B2M, were significantly higher in patients experiencing complications than in the control group (p = 0.036). However, a tendency towards an association between the surgical approach and the changes in cfDNA levels was found for PPIA (p = 0.058), and B2M (p = 0.087). CONCLUSIONS: Plasma cfDNA was increased after surgery in all patients with colon cancer. Postoperative PPIA levels were significantly higher in patients experiencing surgical complications but not in B2M levels

    Receptor–ligand molecular docking

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    The risk of COVID-19 death is much greater and age dependent with type I IFN autoantibodies

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    International audienceSignificance There is growing evidence that preexisting autoantibodies neutralizing type I interferons (IFNs) are strong determinants of life-threatening COVID-19 pneumonia. It is important to estimate their quantitative impact on COVID-19 mortality upon SARS-CoV-2 infection, by age and sex, as both the prevalence of these autoantibodies and the risk of COVID-19 death increase with age and are higher in men. Using an unvaccinated sample of 1,261 deceased patients and 34,159 individuals from the general population, we found that autoantibodies against type I IFNs strongly increased the SARS-CoV-2 infection fatality rate at all ages, in both men and women. Autoantibodies against type I IFNs are strong and common predictors of life-threatening COVID-19. Testing for these autoantibodies should be considered in the general population

    The risk of COVID-19 death is much greater and age dependent with type I IFN autoantibodies

    No full text
    International audienceSignificance There is growing evidence that preexisting autoantibodies neutralizing type I interferons (IFNs) are strong determinants of life-threatening COVID-19 pneumonia. It is important to estimate their quantitative impact on COVID-19 mortality upon SARS-CoV-2 infection, by age and sex, as both the prevalence of these autoantibodies and the risk of COVID-19 death increase with age and are higher in men. Using an unvaccinated sample of 1,261 deceased patients and 34,159 individuals from the general population, we found that autoantibodies against type I IFNs strongly increased the SARS-CoV-2 infection fatality rate at all ages, in both men and women. Autoantibodies against type I IFNs are strong and common predictors of life-threatening COVID-19. Testing for these autoantibodies should be considered in the general population

    ISARIC-COVID-19 dataset: A Prospective, Standardized, Global Dataset of Patients Hospitalized with COVID-19

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    The International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC) COVID-19 dataset is one of the largest international databases of prospectively collected clinical data on people hospitalized with COVID-19. This dataset was compiled during the COVID-19 pandemic by a network of hospitals that collect data using the ISARIC-World Health Organization Clinical Characterization Protocol and data tools. The database includes data from more than 705,000 patients, collected in more than 60 countries and 1,500 centres worldwide. Patient data are available from acute hospital admissions with COVID-19 and outpatient follow-ups. The data include signs and symptoms, pre-existing comorbidities, vital signs, chronic and acute treatments, complications, dates of hospitalization and discharge, mortality, viral strains, vaccination status, and other data. Here, we present the dataset characteristics, explain its architecture and how to gain access, and provide tools to facilitate its use

    The value of open-source clinical science in pandemic response: lessons from ISARIC

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