MICCAI-CDMRI 2023 QuantConn Challenge Findings on Achieving Robust Quantitative Connectivity through Harmonized Preprocessing of Diffusion MRI.

White matter alterations are increasingly implicated in neurological diseases and their progression. International-scale studies use diffusion-weighted magnetic resonance imaging (DW-MRI) to qualitatively identify changes in white matter microstructure and connectivity. Yet, quantitative analysis of DW-MRI data is hindered by inconsistencies stemming from varying acquisition protocols.

High-quality genome assembly and annotation of five bacteria isolated from the Abu Dhabi sabkha-shore region.

Objectives: Sabkhas represent polyextreme environments characterized by elevated salinity levels, intense ultraviolet (UV) radiation exposure, and extreme temperature fluctuations. In this study, we present the complete genomes of five bacterial isolates isolated from the sabkha-shore region and investigate their genomic organization and gene annotations. A better understanding of the bacterial genomic organization and genetic adaptations of these bacteria holds promise for engineering microbes with tailored functionalities for diverse industrial and agricultural applications, including bioremediation and promotion of plant growth under salinity stress conditions.

Step-Up Management in Acute Pancreatitis: A Tertiary Care Center's Experience From Southern India.

Background The clinical spectrum of acute pancreatitis (AP) ranges from mild disease to severe form associated with multiorgan failure, prolonged hospital stay, high morbidity, and mortality. Acute necrotizing pancreatitis (ANP) is a severe form of AP. This study evaluates AP's outcomes after applying principles of the  approach in a tertiary healthcare center in south India.

MONAI Label: A framework for AI-assisted interactive labeling of 3D medical images.

The lack of annotated datasets is a major bottleneck for training new task-specific supervised machine learning models, considering that manual annotation is extremely expensive and time-consuming. To address this problem, we present MONAI Label, a free and open-source framework that facilitates the development of applications based on artificial intelligence (AI) models that aim at reducing the time required to annotate radiology datasets. Through MONAI Label, researchers can develop AI annotation applications focusing on their domain of expertise.