789 resultados para Witherspoon, Julian


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PURPOSE: The transcription factor CCAAT/enhancer binding protein-alpha (CEBPA) is crucial for normal myeloid differentiation. Mutations in the CEBPA gene are found in subsets of patients with acute myeloid leukemia (AML). Recently, three families were reported in whom several family members had germline CEBPA mutations and subsequently developed AML. Whereas familial AML is considered a rare event, the frequency of CEBPA germline mutations in AML is not known. PATIENTS AND METHODS: In this study, we screened 187 consecutive AML patients for CEBPA mutations at diagnosis. We detected 18 patients (9.6%) with CEBPA mutations. We then analyzed remission samples and constitutive DNA from these patients. RESULTS: We found that two (11.1%) of 18 AML patients with CEBPA mutations carried a germline N-terminal frameshift CEBPA mutation. Interestingly, additional members in the families of both of these patients have been affected by AML, and the germline CEBPA mutations were also observed in these patients. Additional somatic mutations in AML patients with germline CEBPA mutations in the two families comprised in-frame C-terminal CEBPA mutations in two patients, two nonsilent CEBPA point mutations in one patient, and monosomy 7 in one patient. CONCLUSION: This study shows, for the first time to our knowledge, that germline CEBPA mutations are frequently observed among AML patients with CEBPA mutations. Including the families with germline CEBPA mutations reported previously, additional somatic CEBPA mutations represent a frequent second event in AML with germline CEBPA mutations. Our data strongly indicate that germline CEBPA mutations predispose to AML and that additional somatic CEBPA mutations contribute to the development of the disease.

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The objective of this research is to develop sustainable wood-blend bioasphalt and characterize the atomic, molecular and bulk-scale behavior necessary to produce advanced asphalt paving mixtures. Bioasphalt was manufactured from Aspen, Basswood, Red Maple, Balsam, Maple, Pine, Beech and Magnolia wood via a 25 KWt fast-pyrolysis plant at 500 °C and refined into two distinct end forms - non-treated (5.54% moisture) and treated bioasphalt (1% moisture). Michigan petroleum-based asphalt, Performance Grade (PG) 58-28 was modified with 2, 5 and 10% of the bioasphalt by weight of base asphalt and characterized with the gas chromatography-mass spectroscopy (GC-MS), Fourier Transform Infra-red (FTIR) spectroscopy and the automated flocculation titrimetry techniques. The GC-MS method was used to characterize the Carbon-Hydrogen-Nitrogen (CHN) elemental ratio whiles the FTIR and the AFT were used to characterize the oxidative aging performance and the solubility parameters, respectively. For rheological characterization, the rotational viscosity, dynamic shear modulus and flexural bending methods are used in evaluating the low, intermediate and high temperature performance of the bio-modified asphalt materials. 54 5E3 (maximum of 3 million expected equivalent standard axle traffic loads) asphalt paving mixes were then prepared and characterized to investigate their laboratory permanent deformation, dynamic mix stiffness, moisture susceptibility, workability and constructability performance. From the research investigations, it was concluded that: 1) levo, 2, 6 dimethoxyphenol, 2 methoxy 4 vinylphenol, 2 methyl 1-2 cyclopentandione and 4-allyl-2, 6 dimetoxyphenol are the dominant chemical functional groups; 2) bioasphalt increases the viscosity and dynamic shear modulus of traditional asphalt binders; 3) Bio-modified petroleum asphalt can provide low-temperature cracking resistance benefits at -18 °C but is susceptible to cracking at -24 °C; 3) Carbonyl and sulphoxide oxidation in petroleum-based asphalt increases with increasing bioasphalt modifiers; 4) bioasphalt causes the asphaltene fractions in petroleum-based asphalt to precipitate out of the solvent maltene fractions; 5) there is no definite improvement or decline in the dynamic mix behavior of bio-modified mixes at low temperatures; 6) bio-modified asphalt mixes exhibit better rutting performance than traditional asphalt mixes; 7) bio-modified asphalt mixes have lower susceptibility to moisture damage; 8) more field compaction energy is needed to compact bio-modified mixes.

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BACKGROUND: As only a minority of alcoholics develop cirrhosis, polymorphic genes, whose products are involved in fibrosis development were suggested to confer individual susceptibility. We tested whether a functional promoter polymorphism in the gene encoding matrix metalloproteinase-3 (MMP-3; 1171 5A/6A) was associated liver cirrhosis in alcoholics. METHODS: Independent cohorts from the UK and Germany were studied. (i) UK cohort: 320 alcoholic cirrhotics and 183 heavy drinkers without liver damage and (ii) German cohort: 149 alcoholic cirrhotics, 220 alcoholic cirrhotics who underwent liver transplantation and 151 alcoholics without liver disease. Patients were genotyped for MMP-3 variants by restriction fragment length polymorphism, single strand confirmation polymorphism, and direct sequencing. In addition, MMP-3 transcript levels were correlated with MMP-3 genotype in normal liver tissues. RESULTS: Matrix metalloproteinase-3 genotype and allele distribution in all 1023 alcoholic patients were in Hardy-Weinberg equilibrium. No significant differences in MMP-3 genotype and allele frequencies were observed either between alcoholics with or without cirrhosis. There were no differences in hepatic mRNA transcription levels according to MMP-3 genotype. CONCLUSIONS: Matrix metalloproteinase-3 1171 promoter polymorphism plays no role in the genetic predisposition for liver cirrhosis in alcoholics. Stringently designed candidate gene association studies are required to exclude chance observations.

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OBJECTIVE: To study the inter-observer variation related to extraction of continuous and numerical rating scale data from trial reports for use in meta-analyses. DESIGN: Observer agreement study. DATA SOURCES: A random sample of 10 Cochrane reviews that presented a result as a standardised mean difference (SMD), the protocols for the reviews and the trial reports (n=45) were retrieved. DATA EXTRACTION: Five experienced methodologists and five PhD students independently extracted data from the trial reports for calculation of the first SMD result in each review. The observers did not have access to the reviews but to the protocols, where the relevant outcome was highlighted. The agreement was analysed at both trial and meta-analysis level, pairing the observers in all possible ways (45 pairs, yielding 2025 pairs of trials and 450 pairs of meta-analyses). Agreement was defined as SMDs that differed less than 0.1 in their point estimates or confidence intervals. RESULTS: The agreement was 53% at trial level and 31% at meta-analysis level. Including all pairs, the median disagreement was SMD=0.22 (interquartile range 0.07-0.61). The experts agreed somewhat more than the PhD students at trial level (61% v 46%), but not at meta-analysis level. Important reasons for disagreement were differences in selection of time points, scales, control groups, and type of calculations; whether to include a trial in the meta-analysis; and data extraction errors made by the observers. In 14 out of the 100 SMDs calculated at the meta-analysis level, individual observers reached different conclusions than the originally published review. CONCLUSIONS: Disagreements were common and often larger than the effect of commonly used treatments. Meta-analyses using SMDs are prone to observer variation and should be interpreted with caution. The reliability of meta-analyses might be improved by having more detailed review protocols, more than one observer, and statistical expertise.

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Making sense of rapidly evolving evidence on genetic associations is crucial to making genuine advances in human genomics and the eventual integration of this information in the practice of medicine and public health. Assessment of the strengths and weaknesses of this evidence, and hence the ability to synthesize it, has been limited by inadequate reporting of results. The STrengthening the REporting of Genetic Association studies (STREGA) initiative builds on the STrengthening the Reporting of OBservational Studies in Epidemiology (STROBE) Statement and provides additions to 12 of the 22 items on the STROBE checklist. The additions concern population stratification, genotyping errors, modelling haplotype variation, Hardy-Weinberg equilibrium, replication, selection of participants, rationale for choice of genes and variants, treatment effects in studying quantitative traits, statistical methods, relatedness, reporting of descriptive and outcome data and the volume of data issues that are important to consider in genetic association studies. The STREGA recommendations do not prescribe or dictate how a genetic association study should be designed, but seek to enhance the transparency of its reporting, regardless of choices made during design, conduct or analysis.

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Making sense of rapidly evolving evidence on genetic associations is crucial to making genuine advances in human genomics and the eventual integration of this information in the practice of medicine and public health. Assessment of the strengths and weaknesses of this evidence, and hence the ability to synthesize it, has been limited by inadequate reporting of results. The STrengthening the REporting of Genetic Association studies (STREGA) initiative builds on the STrengthening the Reporting of OBservational Studies in Epidemiology (STROBE) Statement and provides additions to 12 of the 22 items on the STROBE checklist. The additions concern population stratification, genotyping errors, modelling haplotype variation, Hardy-Weinberg equilibrium, replication, selection of participants, rationale for choice of genes and variants, treatment effects in studying quantitative traits, statistical methods, relatedness, reporting of descriptive and outcome data, and the volume of data issues that are important to consider in genetic association studies. The STREGA recommendations do not prescribe or dictate how a genetic association study should be designed but seek to enhance the transparency of its reporting, regardless of choices made during design, conduct, or analysis.

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Making sense of rapidly evolving evidence on genetic associations is crucial to making genuine advances in human genomics and the eventual integration of this information in the practice of medicine and public health. Assessment of the strengths and weaknesses of this evidence, and hence, the ability to synthesize it, has been limited by inadequate reporting of results. The STrengthening the REporting of Genetic Association (STREGA) studies initiative builds on the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement and provides additions to 12 of the 22 items on the STROBE checklist. The additions concern population stratification, genotyping errors, modeling haplotype variation, Hardy-Weinberg equilibrium, replication, selection of participants, rationale for choice of genes and variants, treatment effects in studying quantitative traits, statistical methods, relatedness, reporting of descriptive and outcome data, and the volume of data issues that are important to consider in genetic association studies. The STREGA recommendations do not prescribe or dictate how a genetic association study should be designed, but seek to enhance the transparency of its reporting, regardless of choices made during design, conduct, or analysis.

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Making sense of rapidly evolving evidence on genetic associations is crucial to making genuine advances in human genomics and the eventual integration of this information in the practice of medicine and public health. Assessment of the strengths and weaknesses of this evidence, and hence the ability to synthesize it, has been limited by inadequate reporting of results. The STrengthening the REporting of Genetic Association studies (STREGA) initiative builds on the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement and provides additions to 12 of the 22 items on the STROBE checklist. The additions concern population stratification, genotyping errors, modelling haplotype variation, Hardy-Weinberg equilibrium, replication, selection of participants, rationale for choice of genes and variants, treatment effects in studying quantitative traits, statistical methods, relatedness, reporting of descriptive and outcome data, and the volume of data issues that are important to consider in genetic association studies. The STREGA recommendations do not prescribe or dictate how a genetic association study should be designed but seek to enhance the transparency of its reporting, regardless of choices made during design, conduct, or analysis.

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Making sense of rapidly evolving evidence on genetic associations is crucial to making genuine advances in human genomics and the eventual integration of this information into the practice of medicine and public health. Assessment of the strengths and weaknesses of this evidence, and hence the ability to synthesize it, has been limited by inadequate reporting of results. The STrengthening the REporting of Genetic Association studies (STREGA) initiative builds on the STrengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement and provides additions to 12 of the 22 items on the STROBE checklist. The additions concern population stratification, genotyping errors, modeling haplotype variation, Hardy-Weinberg equilibrium, replication, selection of participants, rationale for choice of genes and variants, treatment effects in studying quantitative traits, statistical methods, relatedness, reporting of descriptive and outcome data, and issues of data volume that are important to consider in genetic association studies. The STREGA recommendations do not prescribe or dictate how a genetic association study should be designed but seek to enhance the transparency of its reporting, regardless of choices made during design, conduct, or analysis.

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Making sense of rapidly evolving evidence on genetic associations is crucial to making genuine advances in human genomics and the eventual integration of this information in the practice of medicine and public health. Assessment of the strengths and weaknesses of this evidence, and hence the ability to synthesize it, has been limited by inadequate reporting of results. The STrengthening the REporting of Genetic Association studies (STREGA) initiative builds on the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement and provides additions to 12 of the 22 items on the STROBE checklist. The additions concern population stratification, genotyping errors, modeling haplotype variation, Hardy-Weinberg equilibrium, replication, selection of participants, rationale for choice of genes and variants, treatment effects in studying quantitative traits, statistical methods, relatedness, reporting of descriptive and outcome data, and the volume of data issues that are important to consider in genetic association studies. The STREGA recommendations do not prescribe or dictate how a genetic association study should be designed but seek to enhance the transparency of its reporting, regardless of choices made during design, conduct, or analysis.

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Making sense of rapidly evolving evidence on genetic associations is crucial to making genuine advances in human genomics and the eventual integration of this information in the practice of medicine and public health. Assessment of the strengths and weaknesses of this evidence, and hence the ability to synthesize it, has been limited by inadequate reporting of results. The STrengthening the REporting of Genetic Association studies (STREGA) initiative builds on the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement and provides additions to 12 of the 22 items on the STROBE checklist. The additions concern population stratification, genotyping errors, modeling haplotype variation, Hardy-Weinberg equilibrium, replication, selection of participants, rationale for choice of genes and variants, treatment effects in studying quantitative traits, statistical methods, relatedness, reporting of descriptive and outcome data, and the volume of data issues that are important to consider in genetic association studies. The STREGA recommendations do not prescribe or dictate how a genetic association study should be designed but seek to enhance the transparency of its reporting, regardless of choices made during design, conduct, or analysis.