1,895 research outputs found

    Pancreatic cancer-associated diabetes mellitus: an open field for proteomic applications.

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    Background: Diabetes mellitus is associated with pancreatic cancer in more than 80% of the cases. Clinical, epidemiological, and experimental data indicate that pancreatic cancer causes diabetes mellitus by releasing soluble mediators which interfere with both beta-cell function and liver and muscle glucose metabolism. Methods: We analysed, by matrix-assisted laser desorption ionization time of flight (MALDI-TOF), a series of pancreatic cancer cell lines conditioned media, pancreatic cancer patients' peripheral and portal sera, comparing them with controls and chronic pancreatitis patients' sera. Results: MALDI-TOF analysis of pancreatic cancer cells conditioned media and patients' sera indicated a low molecular weight peptide to be the putative pancreatic cancer-associated diabetogenic factor. The sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) analysis of tumor samples from diabetic and non-diabetic patients revealed the presence of a 1500 Da peptide only in diabetic patients. The amino acid sequence of this peptide corresponded to the N-terminal of an S-100 calcium binding protein, which was therefore suggested to be the pancreatic cancer-associated diabetogenic factor. Conclusions: We identified a tumor-derived peptide of 14 amino acids sharing a 100% homology with an S-100 calcium binding protein, which is probably the pancreatic cancer-associated diabetogenic facto

    Pancreatic cancer-derived S-100A8 N-terminal peptide: a diabetes cause?

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    BACKGROUND: Our aim was to identify the pancreatic cancer diabetogenic peptide. METHODS: Pancreatic tumor samples from patients with (n=15) or without (n=7) diabetes were compared with 6 non-neoplastic pancreas samples using SDS-PAGE. RESULTS: A band measuring approximately 1500 Da was detected in tumors from diabetics, but not in neoplastic samples from non-diabetics or samples from non-neoplastic subjects. Sequence analysis revealed a 14 amino acid peptide (1589.88 Da), corresponding to the N-terminal of the S100A8. At 50 nmol/L and 2 mmol/L, this peptide significantly reduced glucose consumption and lactate production by cultured C(2)C(12) myoblasts. The 14 amino acid peptide caused a lack of myotubular differentiation, the presence of polynucleated cells and caspase-3 activation. CONCLUSIONS: The 14 amino acid peptide from S100A8 impairs the catabolism of glucose by myoblasts in vitro and may cause hyperglycemia in vivo. Its identification in biological fluids might be helpful in diagnosing pancreatic cancer in patients with recent onset diabetes mellitus

    The valuation of European financial firms

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    We extend the recent literature concerning accounting based valuation models to investigate financial firms from six European countries with substantial financial sectors: France, Germany, Italy, Netherlands, Switzerland and the UK. Not only are these crucial industries worthy of study in their own right, but unusual accounting practices, and inter-country differences in those accounting practices, provide valuable insights into the accounting-value relationship. Our sample consists of 7,714 financial firm/years observations from 1,140 companies drawn from 1989-2000. Sub-samples include 1,309 firm/years for banks, 650 for insurance companies, 1,705 for real estate firms, and 3,239 for investment companies. In most countries we find that the valuation models work as well or better in explaining cross-sectional variations in the market-to-book ratio for financial firms as they do for industrial and commercial firms in the same countries, although Switzerland is an exception to this generalization. As expected, the results are sensitive to industrial differences, accounting regulation and accounting practices. In particular, marking assets to market value reduces the relevance of earnings figures and increases that of equity

    Seasonality and immunity to laboratory-confirmed seasonal coronaviruses (HCoV-NL63, HCoV-OC43, and HCoV-229E): results from the Flu Watch cohort study [version 1; peer review: 2 approved with reservations]

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    Background: There is currently a pandemic caused by the novel coronavirus SARS-CoV-2. The intensity and duration of this first wave in the UK may be dependent on whether SARS-CoV-2 transmits more effectively in the winter than the summer and the UK Government response is partially built upon the assumption that those infected will develop immunity to reinfection in the short term. In this paper we examine evidence for seasonality and immunity to laboratory-confirmed seasonal coronavirus (HCoV) from a prospective cohort study in England. Methods: In this analysis of the Flu Watch cohort, we examine seasonal trends for PCR-confirmed coronavirus infections (HCoV-NL63, HCoV-OC43, and HCoV-229E) in all participants during winter seasons (2006-2007, 2007-2008, 2008-2009) and during the first wave of the 2009 H1N1 influenza pandemic (May-Sep 2009). We also included data from the pandemic and ‘post-pandemic’ winter seasons (2009-2010 and 2010-2011) to identify individuals with two confirmed HCoV infections and examine evidence for immunity against homologous reinfection. Results: We tested 1,104 swabs taken during respiratory illness and detected HCoV in 199 during the first four seasons. The rate of confirmed HCoV infection across all seasons was 390 (95% CI 338-448) per 100,000 person-weeks; highest in the Nov-Mar 2008/9 season at 674 (95%CI 537-835). The highest rate was in February at 759 (95% CI 580-975). Data collected during May-Sep 2009 showed there was small amounts of ongoing transmission, with four cases detected during this period. Eight participants had two confirmed infections, of which none had the same strain twice

    Learning Aligned-Spatial Graph Convolutional Networks for Graph Classification

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    In this paper, we develop a novel Aligned-Spatial Graph Convolutional Network (ASGCN) model to learn effective features for graph classification. Our idea is to transform arbitrary-sized graphs into fixed-sized aligned grid structures, and define a new spatial graph convolution operation associated with the grid structures. We show that the proposed ASGCN model not only reduces the problems of information loss and imprecise information representation arising in existing spatially-based Graph Convolutional Network (GCN) models, but also bridges the theoretical gap between traditional Convolutional Neural Network (CNN) models and spatially-based GCN models. Moreover, the proposed ASGCN model can adaptively discriminate the importance between specified vertices during the process of spatial graph convolution, explaining the effectiveness of the proposed model. Experiments on standard graph datasets demonstrate the effectiveness of the proposed model

    Household transmission of seasonal coronavirus infections: Results from the Flu Watch cohort study [version 1; peer review: 1 approved, 2 approved with reservations]

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    Background: In the context of the current coronavirus disease 2019 (COVID-19) pandemic, understanding household transmission of seasonal coronaviruses may inform pandemic control. We aimed to investigate what proportion of seasonal coronavirus transmission occurred within households, measure the risk of transmission in households, and describe the impact of household-related factors of risk of transmission. Methods: Using data from three winter seasons of the UK Flu Watch cohort study, we measured the proportion of symptomatic infections acquired outside and within the home, the household transmission risk and the household secondary attack risk for PCR-confirmed seasonal coronaviruses. We present transmission risk stratified by demographic features of households. Results: We estimated that the proportion of cases acquired outside the home, weighted by age and region, was 90.7% (95% CI 84.6- 94.5, n=173/195) and within the home was 9.3% (5.5-15.4, 22/195). Following a symptomatic coronavirus index case, 14.9% (9.8 - 22.1, 20/134) of households experienced symptomatic transmission to at least one other household member. Onward transmission risk ranged from 11.90% (4.84-26.36, 5/42) to 19.44% (9.21-36.49, 7/36) by strain. The overall household secondary attack risk for symptomatic cases was 8.00% (5.31-11.88, 22/275), ranging across strains from 5.10 (2.11-11.84, 5/98) to 10.14 (4.82- 20.11, 7/69). Median clinical onset serial interval was 7 days (IQR= 6-9.5). Households including older adults, 3+ children, current smokers, contacts with chronic health conditions, and those in relatively deprived areas had the highest transmission risks. Child index cases and male index cases demonstrated the highest transmission risks. Conclusion: Most seasonal coronaviruses appear to be acquired outside the household, with relatively modest risk of onward transmission within households. Transmission risk following an index case appears to vary by demographic household features, with potential overlap between those demonstrating the highest point estimates for seasonal coronavirus transmission risk and COVID-19 susceptibility and poor illness outcomes
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