35 research outputs found

    Left Atrial Transverse Diameter on Computed Tomography Angiography Can Accurately Diagnose Left Atrial Enlargement in Patients With Atrial Fibrillation

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    PurposeLeft atrial (LA) enlargement is associated with increased risk for adverse cardiovascular events. We assessed the accuracy of LA transverse and antero-posterior (AP) diameters obtained from chest computed tomography (CT) angiography in patients with atrial fibrillation.Materials and methodsNongated contrast-enhanced 64-slice multidetector CT angiography (slice thickness of 0.625 to 1.25 mm) was used to measure the volume and transverse and AP diameters of the LA in 222 subjects. The internal contours of the LA and LA appendage were outlined in 1 of every 5 axial images, and the LA area was multiplied by 5 times the slice thickness. Maximum transverse and AP diameters of the LA were measured, excluding the appendage. Receiver operating characteristic curves were fitted to assess the accuracy of the diameters. A Wald test was used to compare the area under the curves.ResultsThe mean age of patients was 60.0±10.6 years, and 71% were male. Median LA volume was 55.9±24.4 mL/m. LA enlargement was present in 83% of the patients. Transverse and AP LA diameters were accurate estimators of the LA enlargement. The transverse diameter demonstrated higher accuracy than the AP diameter, with area under the curves of 0.89 (0.84 to 0.94) and 0.81 (0.73 to 0.89), respectively (P<0.05). A transverse LA diameter of 7.3 cm had a sensitivity and specificity of 85% for detection of LA enlargement. At the same sensitivity level, an AP diameter of 4.3 cm had a specificity of 60.5%.ConclusionsTransverse LA diameter can accurately detect LA enlargement in patients with atrial fibrillation. This parameter can be used for detection of patients with possible LA enlargement on chest CT angiography

    AI is a viable alternative to high throughput screening: a 318-target study

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    : High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

    Strategies to improve recruitment to randomised trials

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    Background: Recruiting participants to trials can be extremely difficult. Identifying strategies that improve trial recruitment would benefit both trialists and health research. Objectives: To quantify the effects of strategies for improving recruitment of participants to randomised trials. A secondary objective is to assess the evidence for the effect of the research setting (e.g. primary care versus secondary care) on recruitment. Search methods: We searched the Cochrane Methodology Review Group Specialised Register (CMR) in the Cochrane Library (July 2012, searched 11 February 2015); MEDLINE and MEDLINE In Process (OVID) (1946 to 10 February 2015); Embase (OVID) (1996 to 2015 Week 06); Science Citation Index & Social Science Citation Index (ISI) (2009 to 11 February 2015) and ERIC (EBSCO) (2009 to 11 February 2015). Selection criteria: Randomised and quasi-randomised trials of methods to increase recruitment to randomised trials. This includes non-healthcare studies and studies recruiting to hypothetical trials. We excluded studies aiming to increase response rates to questionnaires or trial retention and those evaluating incentives and disincentives for clinicians to recruit participants. Data collection and analysis: We extracted data on: the method evaluated; country in which the study was carried out; nature of the population; nature of the study setting; nature of the study to be recruited into; randomisation or quasi-randomisation method; and numbers and proportions in each intervention group. We used a risk difference to estimate the absolute improvement and the 95% confidence interval (CI) to describe the effect in individual trials. We assessed heterogeneity between trial results. We used GRADE to judge the certainty we had in the evidence coming from each comparison
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