Notes

Mild Men and women: Mentor Chennupati Jagadish.
Reinfection with SARS-CoV-2 is a rare phenomenon. To date, there has been some cases reported from countries such as United States, Ecuador, Hong Kong, the Netherlands and Belgium. This case report presents the first case of reinfection from Saudi Arabia, and probably the first dental student to have been re-infected with COVID-19. Selleckchem U0126 A 24-year-old male dental student presents with reinfection after a period of three months since he was first infected with COVID-19. The signs and symptoms reported by the patient were similar in both instances, except that he developed fever only at the time of reinfection. The infection and reinfection were confirmed with a RT-PCR test reports. This report highlights how it is necessary to continue to observe all the prescriptions recently indicated in the literature in order to avoid new contagion for all health workers after healed from covid-19 or asymptomatic positive, since as seen sometimes the infection does not ensures complete immunity in 100% of cases.Super-resolvedq-space deep learning (SR-q-DL) has been developed to estimate high-resolution (HR) tissue microstructure maps from low-quality diffusion magnetic resonance imaging (dMRI) scans acquired with a reduced number of diffusion gradients and low spatial resolution, where deep networks are designed for the estimation. However, existing methods do not exploit HR information from other modalities, which are generally acquired together with dMRI and could provide additional useful information for HR tissue microstructure estimation. In this work, we extend SR-q-DL and propose multimodal SR-q-DL, where information in low-resolution (LR) dMRI is combined with HR information from another modality for HR tissue microstructure estimation. Because the HR modality may not be as sensitive to tissue microstructure as dMRI, direct concatenation of multimodal information does not necessarily lead to improved estimation performance. Since existing deep networks for HR tissue microstructure estimation are patch-based LR sparse representation with voxelwise multiplication, and the weighted LR sparse representation is used to compute HR tissue microstructure with another network component that allows resolution enhancement. All weights in the proposed network for multimodal SR-q-DL are jointly learned and the estimation is end-to-end. To evaluate the proposed method, we performed experiments on brain dMRI scans together with images of additional HR modalities. In the experiments, the proposed method was applied to the estimation of tissue microstructure measures for different datasets and advanced biophysical models, where the benefit of incorporating multimodal information using the proposed method is shown.Brain image analysis has advanced substantially in recent years with the proliferation of neuroimaging datasets acquired at different resolutions. While research on brain image super-resolution has undergone a rapid development in the recent years, brain graph super-resolution is still poorly investigated because of the complex nature of non-Euclidean graph data. In this paper, we propose the first-ever deep graph super-resolution (GSR) framework that attempts to automatically generate high-resolution (HR) brain graphs with N' nodes (i.e., anatomical regions of interest (ROIs)) from low-resolution (LR) graphs with N nodes where N less then N'. First, we formalize our GSR problem as a node feature embedding learning task. Once the HR nodes' embeddings are learned, the pairwise connectivity strength between brain ROIs can be derived through an aggregation rule based on a novel Graph U-Net architecture. While typically the Graph U-Net is a node-focused architecture where graph embedding depends mainly on node attributes, we propose a graph-focused architecture where the node feature embedding is based on the graph topology. Second, inspired by graph spectral theory, we break the symmetry of the U-Net architecture by super-resolving the low-resolution brain graph structure and node content with a GSR layer and two graph convolutional network layers to further learn the node embeddings in the HR graph. Third, to handle the domain shift between the ground-truth and the predicted HR brain graphs, we incorporate adversarial regularization to align their respective distributions. Our proposed AGSR-Net framework outperformed its variants for predicting high-resolution functional brain graphs from low-resolution ones. Our AGSR-Net code is available on GitHub at https//github.com/basiralab/AGSR-Net.
The aim of this study was to investigate the diagnostic abilities of both pericoronary adipose tissue (PCAT) CT attenuation and volume for the predication hemodynamic significance of coronary artery stenosis as evaluated by fractional flow reserve (FFR).

Patients with ≥ 30 % in at least 1 major epicardial coronary artery were retrospectively included. Furthermore, all eligible patients underwent coronary computed tomography angiography (CCTA) and invasive coronary angiography (ICA) as well as FFR within 1 month. PCAT CT attenuation and volume around ischemic and non-ischemic coronary stenosis were measured and compared. The diagnostic accuracy of PCAT CT attenuation and volume for the identification of hemodynamically significant stenosis was determined against the reference standard of FFR ≤ 0.80.

A total of 61 patients (mean age, 57.8 years ± 11.8) with 77 vessels were included. Average PCAT CT attenuation of all vessels was -70.3 ± 7.4 HU. PCAT CT attenuation in coronary arteries with hemodynamically disease, after adding of PCAT CT attenuation to CCTA, the diagnostic ability for the identification of ischemic coronary stenosis was significantly improved.
To evaluate the relationship between myocardial extracellular volume (ECV) fraction measured using dual-energy computed tomography with late iodine enhancement (LIE-DECT) and risk of heart failure (HF) in patients without coronary artery disease (CAD), and to evaluate the relationship between ECV and left ventricular structure and function.

Sixty consecutive HF patients without CAD and 60 consecutive participants without heart disease who underwent coronary CT angiography (CCTA) following LIE-DECT were included. ECV of the left ventricle was calculated from the iodine maps and hematocrit levels using the American Heart Association (AHA) 16-segment model. Cardiac structural and functional parameters were collected including left ventricular end-systolic volume (LVESV), left ventricular end-diastolic volume (LVEDV), left ventricular ejection fraction (LVEF), left atrial volume (LAV), interventricular septal thickness (IVST), and N-terminal pro-brain natriuretic peptide (NT-pro-BNP).

ECV in HF patients without CAD (31.
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