Cooke Benton (baycrown8)

Resistance to antiepileptic drug treatment increases the risk of comorbidities and mortality due to a cardio-autonomic imbalance and left ventricular (LV) dysfunction. To assess the prevalence of LV dysfunction and cardio-autonomic imbalance in children with drug-resistant epilepsy (DRE). This cross-sectional study included 40 children with DRE and 40 healthy age- and sex-matched controls. LV function was evaluated by M-mode, two-dimensional, pulse-wave Doppler echocardiography, and tissue Doppler imaging (TDI). Cardio-autonomic function was assessed by 24 -h Holter monitoring of heart rate variability. All time domain measures were significantly lower in the epilepsy group than in the control group (all Ps<0.01). Additionally, the mean high frequency (HF) parameters were significantly lower (P = 0.035), whereas the mean low frequency (LF) parameters and the LF/HF ratio were significantly higher (P < 0.001) in the epilepsy group than in the control group. LV function did not differ between groups regarding all standard echocardiographic parameters. There was evidence of subclinical LVdysfunction by tissue doppler among the epileptic group, as evidenced by the elevated Myocardial Performance Index, isovolumetric relaxation time and mitral E/Em ratio. There was no significant correlation between the duration of epilepsy or seizure frequency with any cardiac abnormality. Children with DRE exhibited cardio-autonomic and subclinical LV dysfunction, independent of the duration of epilepsy, frequency, and seizure type. Children with DRE exhibited cardio-autonomic and subclinical LV dysfunction, independent of the duration of epilepsy, frequency, and seizure type.Segmentation of Intravascular Ultrasound (IVUS) images into Lumen and Media (interior and exterior) artery vessel walls is highly clinically relevant in the diagnosis and treatment of cardiovascular diseases such as atherosclerosis. When fused with position data, such segmentations also play a key role in reconstructing 3D representations of arteries. Automated segmentation in real-time is known to be a difficult image analysis problem, primarily due to artefacts commonly present in IVUS ultrasound images such as shadows, guide-wire effects, and side-branches. An additional challenge is the limited amount of expert labelled IVUS data, which limits the application of many well-performing deep learning models from other domains. To exploit the circular layered structure of the artery in B-Mode images, we propose a multi-class fully convolutional semantic segmentation network based on a minimal U-Net architecture augmented with learned translation dependence in the polar domain. The coordinate awareness in the multi-class segmentation allows the model to exploit relative spatial context about the interior and exterior vessel walls which are simply separable in polar coordinates. After training on 109 expert-labelled examples, our model significantly outperforms the state-of-the art in terms of mean Jaccard Measure (0.91 vs. 0.89) and Hausdorff distance (0.32 mm vs. 0.48 mm) on Media segmentation, and reaches equivalent performance on Lumen segmentation when evaluated on a standard publicly available dataset of 326 IVUS B-Mode images captured by 20 Mhz ultrasound probes. Using an order of magnitude fewer trainable parameters than the previous state-of-the-art, our model runs over 50 times faster and is able to execute in only 3 ms on a common GPU, achieving both leading accuracy and practical real-time performance.Genomic aberrations (GAs) in fibroblast growth factor receptors (FGFRs) are involved in the pathogenesis of intrahepatic cholangiocarcinoma (ICC), and clinical trials have shown efficacy of FGFR inhibitors in treating ICC patients with FGFR GAs such as FGFR2 rearrangement. To clarify the FGFRs GA profile and corresponding clinicopathological features in Chinese patients with ICC, a total of 257 cases were iden