33 research outputs found

    COVID-19 symptoms at hospital admission vary with age and sex: results from the ISARIC prospective multinational observational study

    Get PDF
    Background: The ISARIC prospective multinational observational study is the largest cohort of hospitalized patients with COVID-19. We present relationships of age, sex, and nationality to presenting symptoms. Methods: International, prospective observational study of 60 109 hospitalized symptomatic patients with laboratory-confirmed COVID-19 recruited from 43 countries between 30 January and 3 August 2020. Logistic regression was performed to evaluate relationships of age and sex to published COVID-19 case definitions and the most commonly reported symptoms. Results: ‘Typical’ symptoms of fever (69%), cough (68%) and shortness of breath (66%) were the most commonly reported. 92% of patients experienced at least one of these. Prevalence of typical symptoms was greatest in 30- to 60-year-olds (respectively 80, 79, 69%; at least one 95%). They were reported less frequently in children (≀ 18 years: 69, 48, 23; 85%), older adults (≄ 70 years: 61, 62, 65; 90%), and women (66, 66, 64; 90%; vs. men 71, 70, 67; 93%, each P < 0.001). The most common atypical presentations under 60 years of age were nausea and vomiting and abdominal pain, and over 60 years was confusion. Regression models showed significant differences in symptoms with sex, age and country. Interpretation: This international collaboration has allowed us to report reliable symptom data from the largest cohort of patients admitted to hospital with COVID-19. Adults over 60 and children admitted to hospital with COVID-19 are less likely to present with typical symptoms. Nausea and vomiting are common atypical presentations under 30 years. Confusion is a frequent atypical presentation of COVID-19 in adults over 60 years. Women are less likely to experience typical symptoms than men

    Soluble stroma‐related biomarkers of pancreatic cancer

    Get PDF
    Abstract The clinical management of pancreatic ductal adenocarcinoma (PDAC) is hampered by the lack of reliable biomarkers. This study investigated the value of soluble stroma‐related molecules as PDAC biomarkers. In the first exploratory phase, 12 out of 38 molecules were associated with PDAC in a cohort of 25 PDAC patients and 16 healthy subjects. A second confirmatory phase on an independent cohort of 131 PDAC patients, 30 chronic pancreatitis patients, and 131 healthy subjects confirmed the PDAC association for MMP7, CCN2, IGFBP2, TSP2, sICAM1, TIMP1, and PLG. Multivariable logistic regression model identified biomarker panels discriminating respectively PDAC versus healthy subjects (MMP7 + CA19.9, AUC = 0.99, 99% CI = 0.98–1.00) (CCN2 + CA19.9, AUC = 0.96, 99% CI = 0.92–0.99) and PDAC versus chronic pancreatitis (CCN2 + PLG+FN+Col4 + CA19.9, AUC = 0.94, 99% CI = 0.88–0.99). Five molecules were associated with PanIN development in two GEM models of PDAC (PdxCre/LSL‐KrasG12D and PdxCre/LSL‐KrasG12D/+/LSL‐Trp53R172H/+), suggesting their potential for detecting early disease. These markers were also elevated in patient‐derived orthotopic PDAC xenografts and associated with response to chemotherapy. The identified stroma‐related soluble biomarkers represent potential tools for PDAC diagnosis and for monitoring treatment response of PDAC patients

    A founder MLH1 mutation in Lynch syndrome families from Piedmont, Italy, is associated with an increased risk of pancreatic tumours and diverse immunohistochemical patterns

    Get PDF
    The MLH1 c.2252_2253delAA mutation was found in 11 unrelated families from a restricted area southwest of Turin among 140 families with mutations in the mismatch repair genes. The mutation is located in the highly conserved C-terminal region, responsible for dimerization with the PMS2 protein. Twenty-five tumour tissues from 61 individuals with the c.2252_2253delAA mutation were tested for microsatellite instability(MSI) and protein expression.We compared the clinical features of these families versus the rest of our cohort and screened for a founder effect. All but one tumours showed the MSI-high mutator phenotype. Normal, focal and lack of MLH1 staining were observed in 16, 36 and 48 % of tumours, respectively. PMS2 expression was always lost. The mutation co-segregated with Lynch syndrome-related cancers in all informative families. All families but one fulfilled Amsterdam criteria, a frequency higher than in other MLH1 mutants. This was even more evident for AC II (72.7 vs. 57.5 %). Moreover, all families had at least one colon cancer diagnosed before 50 years and one case with multiple Lynch syndrome-related tumours. Interestingly, a statistically significant (p = 0.0057) higher frequency of pancreatic tumourswas observed compared to familieswith other MLH1 mutations: 8.2 % of affected individuals versus 1.6 %. Haplotype analysis demonstrated a common ancestral origin of the mutation, which originated about 1,550 years ago. The mutation is currently classified as having an uncertain clinical significance. Clinical features, tissue analysis and co-segregation with disease strongly support the hypothesis that the MLH1 c.2252_2253delAA mutation has a pathogenic effec

    Representing the Process of Inflammation as Key Events in Adverse Outcome Pathways

    No full text
    Inflammation is an important biological process involved in many target organ toxicities. However, there has been little consensus on how to represent inflammatory processes using the adverse outcome pathway (AOP) framework. In particular, there were concerns that inflammation was not being represented in a way that it would be recognized as a highly connected, central node within the global AOP network. The consideration of salient features common to the inflammatory process across tissues was used as a basis to propose 3 hub key events (KEs) for use in AOP network development. Each event, “tissue resident cell activation”, “increased pro-inflammatory mediators”, and “leukocyte recruitment/activation,” is viewed as a hallmark of inflammation, independent of tissue, and can be independently measured. Using these proposed hub KEs, it was possible to link together a series of AOPs that previously had no shared KEs. Significant challenges remain with regard to accurate prediction of inflammation-related toxicological outcomes even if a broader and more connected network of inflammation-centered AOPs is developed. Nonetheless, the current proposal addresses one of the major hurdles associated with representation of inflammation in AOPs and may aid fit-for-purpose evaluations of other AOPs operating in a network context.JRC.F.3-Chemicals Safety and Alternative Method

    Representing the Process of Inflammation as Key Events in Adverse Outcome Pathways.

    Get PDF
    Inflammation is an important biological process involved in many target organ toxicities. However, there has been little consensus on how to represent inflammatory processes using the adverse outcome pathway (AOP) framework. In particular, there were concerns that inflammation was not being represented in a way that it would be recognized as a highly connected, central node within the global AOP network. The consideration of salient features common to the inflammatory process across tissues was used as a basis to propose 3 hub key events (KEs) for use in AOP network development. Each event, "tissue resident cell activation", "increased pro-inflammatory mediators", and "leukocyte recruitment/activation," is viewed as a hallmark of inflammation, independent of tissue, and can be independently measured. Using these proposed hub KEs, it was possible to link together a series of AOPs that previously had no shared KEs. Significant challenges remain with regard to accurate prediction of inflammation-related toxicological outcomes even if a broader and more connected network of inflammation-centered AOPs is developed. Nonetheless, the current proposal addresses one of the major hurdles associated with representation of inflammation in AOPs and may aid fit-for-purpose evaluations of other AOPs operating in a network context
    corecore