Impact of kidney disease in patients undergoing catheter directed interventions for intermediate to high-risk pulmonary embolism.

Background: Catheter-directed interventions (CDIs) for pulmonary embolism (PE) continue to evolve. However, due to the paucity of data, their use has been limited in patients with underlying kidney disease.

Methods: The National Readmission Database (2016-2020) was utilized to identify intermediate to high-risk PE (IHR-PE) patients requiring CDI (thrombectomy, thrombolysis, and ultrasound-assisted thrombolysis).

Ethyl 4-[(2-hy-droxy-eth-yl)amino]-2-(4-meth-oxyphen-yl)-1-methyl-5-oxo-2,5-di-hydro-1-pyrrole-3-carboxyl-ate.

In the title compound, CHNO the pyrrolidine ring is almost planar and subtends a dihedral angle of 85.77 (7)° with the pendant phenyl ring. An intra-molecular N-H⋯O hydrogen bond generates an (6) loop.

Methyl 2-[()-5-methyl-2-oxoindolin-3-yl-idene]hydrazinecarbodi-thio-ate.

The title di-thio-carbazate imine, CHNOS, was obtained from the condensation reaction of -methyl-dithio-carbazate (SMDTC) and 5-methyl-isatin. It shows a configuration about the imine C=N bond, which is associated with an intra-molecular N-H⋯O hydrogen bond that closes an (6) ring. In the crystal, inversion dimers linked by pairwise N-H⋯O hydrogen bonds generate (8) loops.

Ethyl (2,3,4)-1-ethyl-2-(furan-2-yl)-4-hy-droxy-5-oxopyrrolidine-3-carboxyl-ate.

The title racemic oxopyrrolidine compound, CHNO, contains three stereogenic centres and crystallizes with two mol-ecules in the asymmetric unit. The five-membered pyrrolidine rings in both mol-ecules exhibit envelope conformations. The -ethyl group of one of the mol-ecules is disordered over two sets of sites in a 0.

GraFMRI: A graph-based fusion framework for robust multi-modal MRI reconstruction.

Purpose: This study introduces GraFMRI, a novel framework designed to address the challenges of reconstructing high-quality MRI images from undersampled k-space data. Traditional methods often suffer from noise amplification and loss of structural detail, leading to suboptimal image quality. GraFMRI leverages Graph Neural Networks (GNNs) to transform multi-modal MRI data (T1, T2, PD) into a graph-based representation, enabling the model to capture intricate spatial relationships and inter-modality dependencies.