Qualitative American Heart Association plot of late gadolinium enhancement with mortality and ventricular arrhythmia prediction using artificial intelligence.

Background: The prognostic value of late gadolinium enhancement (LGE) in cardiac magnetic resonance (CMR) imaging is well-established. However, the direct relationship between image pixels and outcomes remains poorly understood. We hypothesised that leveraging artificial intelligence (AI) to analyse qualitative LGE images based on American Heart Association (AHA) guidelines could elucidate this relationship.

The development and psychometric properties of the Prison Fellowship- Well-being Index (PF WBI).

Purpose: This paper aims to describe the development and validation of the Prison Fellowship Well-being index (PF-WBI), a new quantitative tool for assessing prisoner and staff well-being within prison cultures.

Design/methodology/approach: The PF-WBI was developed through an iterative process of item creation, administration alongside established well-being measures and a series of data analyses. Data was collected from both staff and prisoners ( = 989) across four North Dakota prisons.

Divergent roles of RIPK3 and MLKL in high-fat diet-induced obesity and MAFLD in mice.

Cell death frequently occurs in the pathogenesis of obesity and metabolic dysfunction-associated fatty liver disease (MAFLD). However, the exact contribution of core cell death machinery to disease manifestations remains ill-defined. Here, we show via the direct comparison of mice genetically deficient in the essential necroptotic regulators, receptor-interacting protein kinase-3 (RIPK3) and mixed lineage kinase domain-like (MLKL), as well as mice lacking apoptotic caspase-8 in myeloid cells combined with RIPK3 loss, that RIPK3/caspase-8 signaling regulates macrophage inflammatory responses and drives adipose tissue inflammation and MAFLD upon high-fat diet feeding.

Automated cardiovascular MR myocardial scar quantification with unsupervised domain adaptation.

Quantification of myocardial scar from late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) images can be facilitated by automated artificial intelligence (AI)-based analysis. However, AI models are susceptible to domain shifts in which the model performance is degraded when applied to data with different characteristics than the original training data. In this study, CycleGAN models were trained to translate local hospital data to the appearance of a public LGE CMR dataset.