Chronic diabetic complications affect multiple organs causing widespread organ damage. Although there are some commonalities, the phenotype of such changes show tissue specific variation. Given this, we examined whether differences in circular RNA (circRNA) mediated gene regulatory mechanisms contribute to changes in gene expression at the basal level and in diabetes. CircRNAs are single-stranded RNA with covalently closed loop structures and act as miRNA sponges, factors of RNA splicing, scaffolding for proteins, regulators of transcription, and modulators of the expression of parental genes, among other roles. We examined heart and retinal tissue from Streptozotocin-induced diabetic mice with established diabetes related tissue damage and tissue from non-diabetic controls. A custom array analysis was performed and the data were analysed. Two major circRNA mediated processes were uniquely upregulated in diabetic heart tissue, namely, positive regulation of endothelial cell migration and regulation of mitochondria: mitochondrial electron transport. In the retina, circRNAs regulating extracellular matrix protein production and endothelial to mesenchymal transition (EndMT) were found to be upregulated. The current study identified regulatory and potential pathogenetic roles of specific circRNA in diabetic retinopathy and cardiomyopathy. Understanding such novel mechanisms, may in the future, be useful to develop RNA based treatment strategies.
The Coronavirus Disease of 2019 (COVID-19) pandemic has caused significant delays in the delivery of cancer treatments in Canada. As cancer treatment and imaging volumes return to normal, radiologists will encounter more cases of chemotherapy-induced toxicities. These toxicities have varied appearances on imaging, and can affect multiple organ systems. The purpose of this review is to offer a unified resource for general radiologists regarding the imaging appearances of chemotherapy-induced toxicities.
Purpose Bard by Google, a direct competitor to ChatGPT, was recently released. Understanding the relative performance of these different chatbots can provide important insight into their strengths and weaknesses as well as which roles they are most suited to fill. In this project, we aimed to compare the most recent version of ChatGPT, ChatGPT-4, and Bard by Google, in their ability to accurately respond to radiology board examination practice questions. Methods Text-based questions were collected from the 2017-2021 American College of Radiology’s Diagnostic Radiology In-Training (DXIT) examinations. ChatGPT-4 and Bard were queried, and their comparative accuracies, response lengths, and response times were documented. Subspecialty-specific performance was analyzed as well. Results 318 questions were included in our analysis. ChatGPT answered significantly more accurately than Bard (87.11% vs 70.44%, P < .0001). ChatGPT’s response length was significantly shorter than Bard’s (935.28 ± 440.88 characters vs 1437.52 ± 415.91 characters, P < .0001). ChatGPT’s response time was significantly longer than Bard’s (26.79 ± 3.27 seconds vs 7.55 ± 1.88 seconds, P < .0001). ChatGPT performed superiorly to Bard in neuroradiology, (100.00% vs 86.21%, P = .03), general & physics (85.39% vs 68.54%, P < .001), nuclear medicine (80.00% vs 56.67%, P < .01), pediatric radiology (93.75% vs 68.75%, P = .03), and ultrasound (100.00% vs 63.64%, P < .001). In the remaining subspecialties, there were no significant differences between ChatGPT and Bard’s performance. Conclusion ChatGPT displayed superior radiology knowledge compared to Bard. While both chatbots display reasonable radiology knowledge, they should be used with conscious knowledge of their limitations and fallibility. Both chatbots provided incorrect or illogical answer explanations and did not always address the educational content of the question.
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