Citations

RBFOX1 is an RNA-binding protein that regulates alternative splicing and RNA processing in the neurons, skeletal muscle, and heart. We intended to define the role of RBFOX1 in regulating calcium homeostasis to maintain normal cardiac function. We generated cardiomyocyte-specific Rbfox1 gene-deletion mice (cKO). The cardiomyocyte-specific deletion of RBFOX1 was confirmed by Western blotting and immunohistochemistry. The cKO mice showed mild hypertrophy and depressed cardiac function under homeostatic conditions, which did not deteriorate with age. Pressure overload by trans-aortic constriction (TAC) caused exaggerated cardiac hypertrophy and accelerated heart failure in cKO compared with wild-type mice. We performed Western blotting to assess the expression of important Ca2+-handling proteins, which showed alterations in the phosphorylation of PLN and CAMKII and decreased expression of SERCA2. We measured the Ca2+ dynamics and noted significantly delayed Ca2+ reuptake into the sarcoplasmic reticulum. Importantly, the decrease in SERCA2 expression was not due to reduced mRNA expression or altered splicing. To assess the possibility of the post-transcriptional regulation of SERCA2 expression by RBFOX1, we performed RNA immunoprecipitation (RIP), which showed the binding of RBFOX1 protein to Serca2 mRNA, which was confirmed in luciferase assays with the Serca2a 3′-untranslated region fused to luciferase. Finally, we performed a puromycin incorporation experiment, which showed that RBFOX1 enhances SERCA2 protein translation. Our results show that RBFOX1 plays a crucial role in regulating the expression of Ca2+-handling genes to maintain normal cardiac function. We show an important post-transcriptional role of RBFOX1 in regulating SERCA2 expression.

Artificial intelligence (AI) is rapidly moving from an experimental phase to an implementation phase in many fields, including medicine. The combination of improved availability of large datasets, increasing computing power, and advances in learning algorithms has created major performance breakthroughs in the development of AI applications. In the last 5 years, AI techniques known as deep learning have delivered rapidly improving performance in image recognition, caption generation, and speech recognition. Radiology, in particular, is a prime candidate for early adoption of these techniques. It is anticipated that the implementation of AI in radiology over the next decade will significantly improve the quality, value, and depth of radiology's contribution to patient care and population health, and will revolutionize radiologists' workflows. The Canadian Association of Radiologists (CAR) is the national voice of radiology committed to promoting the highest standards in patient-centered imaging, lifelong learning, and research. The CAR has created an AI working group with the mandate to discuss and deliberate on practice, policy, and patient care issues related to the introduction and implementation of AI in imaging. This white paper provides recommendations for the CAR derived from deliberations between members of the AI working group. This white paper on AI in radiology will inform CAR members and policymakers on key terminology, educational needs of members, research and development, partnerships, potential clinical applications, implementation, structure and governance, role of radiologists, and potential impact of AI on radiology in Canada.