Dukebroch3854

4 %. In the prospective part of the study overall PPA of the eazyplex® kit was 66.7 % but increased to 100 % when only Ct values ≤ 28 were considered. There were no false positive results. The median time to positivity was 12.5 min for the N gene and 16.75 min for ORF8. Analytical sensitivity was 3.75 TCID

/mL. 10

virus copies/mL were reproducibly detected.

The eazyplex® SARS-CoV-2 is a rapid assay that accurately identifies samples with high viral loads. It may be useful for near-patient testing outside of a molecular diagnostic laboratory.

The eazyplex® SARS-CoV-2 is a rapid assay that accurately identifies samples with high viral loads. It may be useful for near-patient testing outside of a molecular diagnostic laboratory.

Cytomegalovirus (CMV) nucleic acid amplification testing is important for CMV infection diagnosis and management. CMV DNA is found in plasma and various other fluids, including urine. If CMV can be reliably detected in urine, it may be considered a non-invasive alternative to blood tests. The cobas 6800 system (Roche Diagnostics, Mannheim, Germany) is a Food and Drug Administration-approved testing platform for measuring CMV DNA in plasma.

To evaluate the analytical performance of the cobas 6800 system and compare the clinical feasibility of CMV detection in plasma and urine samples.

Imprecision, linearity, limit of quantitation (LOQ), and cross-reactivity of the cobas 6800 system were assessed, and reference interval verification was performed. Plasma CMV DNA quantification was compared to CMV DNA values in urine samples obtained from 129 pediatric patients (<18 years of age) from March 2020 to May 2020 at a tertiary hospital.

The assay precision was within the acceptable range. Linearity was observed within the tested concentration range (2.36-6.33 log IU/mL) with a coefficient of determination of 0.9972. The LOQ was 34.5 IU/mL. The assay did not show cross-reactivity with 15 other viruses. Plasma and urine detection results were stratified into three categories negative, <LOQ, and positive to analyze the degree of agreement with the results. The quadratic weighted kappa value was 0.623 (P = 0.000), showing substantial concurrence.

The cobas 6800 system offers good sensitivity, precision, and linearity and is suitable for monitoring CMV viral loads in the plasma and urine samples.

The cobas 6800 system offers good sensitivity, precision, and linearity and is suitable for monitoring CMV viral loads in the plasma and urine samples.False positive reduction plays a key role in computer-aided detection systems for pulmonary nodule detection in computed tomography (CT) scans. However, this remains a challenge owing to the heterogeneity and similarity of anisotropic pulmonary nodules. In this study, a novel attention-embedded complementary-stream convolutional neural network (AECS-CNN) is proposed to obtain more representative features of nodules for false positive reduction. The proposed network comprises three function blocks 1) attention-guided multi-scale feature extraction, 2) complementary-stream block with an attention module for feature integration, and 3) classification block. The inputs of the network are multi-scale 3D CT volumes due to variations in nodule sizes. Subsequently, a gradual multi-scale feature extraction block with an attention module was applied to acquire more contextual information regarding the nodules. A subsequent complementary-stream integration block with an attention module was utilized to learn the significantly complementary features. Finally, the candidates were classified using a fully connected layer block. OSI-930 An exhaustive experiment on the LUNA16 challenge dataset was conducted to verify the effectiveness and performance of the proposed network. The AECS-CNN achieved a sensitivity of 0.92 with 4 false positives per scan. The results indicate that the attention mechanism can improve the network performance in false positive reduction, the proposed AECS-CNN can learn more representative features, and the attention module can guide the network to learn the discriminated feature channels and the crucial information embedded in the data, thereby effectively enhancing the performance of the detection system.

Recently, an augmented reality (AR) solution allows the physician to place the ablation catheter at the designated lesion site more accurately during cardiac electrophysiology studies. The improvement in navigation accuracy may positively affect ventricular tachycardia (VT) ablation termination, however assessment of this in the clinic would be difficult. Novel personalized virtual heart technology enables non-invasive identification of optimal lesion targets for infarct-related VT. This study aims to evaluate the potential impact of such catheter navigation accuracy improvement in virtual VT ablations.

2 MRI-based virtual hearts with 2 in silico induced VTs (VT 1, VT 2) were included. VTs were terminated with virtual "ground truth" endocardial ablation lesions. 106 navigation error values that were previously assessed in a clinical study evaluating the improvement of ablation catheter navigation accuracy guided with AR (53 with, 53 without) were used to displace the "ground truth" ablation targets. The corresponding ablations were simulated based on these errors and VT termination for each simulation was assessed.

In 54 VT 1 ablation simulations, smaller error with AR significantly resulted in more VT termination (25) compared to the error without AR (16) (P<0.01). In 52 VT 2 ablation simulations, no significant difference was observed from error with (11) and without AR (13) (P=0.58). The substrate characteristic may impact the effect of improved accuracy to an improved VT termination.

Virtual heart shows that the increased catheter navigation accuracy provided by AR guidance can affect the VT termination.

Virtual heart shows that the increased catheter navigation accuracy provided by AR guidance can affect the VT termination.Ontology-based phenotype profiles have been utilised for the purpose of differential diagnosis of rare genetic diseases, and for decision support in specific disease domains. Particularly, semantic similarity facilitates diagnostic hypothesis generation through comparison with disease phenotype profiles. However, the approach has not been applied for differential diagnosis of common diseases, or generalised clinical diagnostics from uncurated text-derived phenotypes. In this work, we describe the development of an approach for deriving patient phenotype profiles from clinical narrative text, and apply this to text associated with MIMIC-III patient visits. We then explore the use of semantic similarity with those text-derived phenotypes to classify primary patient diagnosis, comparing the use of patient-patient similarity and patient-disease similarity using phenotype-disease profiles previously mined from literature. We also consider a combined approach, in which literature-derived phenotypes are extended with the content of text-derived phenotypes we mined from 500 patients.