999 resultados para Little, George


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The dynamics of HIV-1 RNA during structured treatment interruptions (STIs) are well established, but little is known about viral proteins like p24. We studied 65 participants of an STI trial. Before the trial, continuous highly active antiretroviral therapy (HAART) had suppressed their viral load to <50 copies/mL during 6 months. They then interrupted HAART during weeks 1 through 2, 11 through 12, 21 through 22, 31 through 32, and 41 through 52. The p24 was measured by boosted enzyme-linked immunosorbent assay of plasma pretreated by efficient virus disruption and heat denaturation. At time point 0, p24 was measurable in 22 patients (34%), who had maintained a viral load <50 copies/mL for 25.4 months (median, range: 6.2-38.9 months) under HAART. Viral rebounds during 2-week STIs led to a mean p24 increase of only 0.08 to 0.19 log10 (ie, 20%-60%). Pre-HAART viral load and p24 at time 0 independently predicted p24 rebounds during the 4 2-week STIs. The p24 at time 0 and HIV-1 RNA rebound during weeks 41 through 52 independently determined the concomitant p24 rebound. An increase of p24 but not viral load during the first 8 weeks of the long STI correlated significantly with concomitant CD4(+) T cell loss. Persisting p24 despite successful HAART may reflect virus replication in reservoirs not represented by plasma viral load and has implications for the concept of therapeutic vaccination.

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The study of the micro-fauna of Montana formations has been almost entirely neglected. Because the petroleum industry of this state has not felt the necessity for using micro-paleontology in its sub-surface correlations, the science has been but little used. The Montana Power Company has had an examination made of some of its well cuttings by a competent micro-paleontologist who found some foraminifera in Mesozoic sediments. However, no investiga­tions have been made to determine the presence and character of the micro-fauna of the Paleozoic formations of Montana.

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The objective of this study was to describe the all-cause mortality of participants in the Swiss Hepatitis C Cohort compared to the Swiss general population. Patients with hepatitis C virus (HCV) infection attending secondary and tertiary care centres in Switzerland. One thousand six hundred and forty-five patients with HCV infection were followed up for a mean of over 2 years. We calculated all-cause standardized mortality ratios (SMR) and 95% confidence intervals (CI) using age, sex and calendar year-specific Swiss all-cause mortality rates. Multivariable Poisson regression was used to model the variability of SMR by cirrhotic status, HCV genotype, infection with hepatitis B virus or HIV, injection drug use and alcohol intake. Sixty-one deaths were recorded out of 1645 participants. The crude all-cause SMR was 4.5 (95% CI: 3.5-5.8). Patients co-infected with HIV had a crude SMR of 20 (95% CI: 11.1-36.1). The SMR of 1.1 (95% CI: 0.63-2.03) for patients who were not cirrhotic, not infected with HBV or HIV, did not inject drugs, were not heavy alcohol consumers (

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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.