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Sonstige beteiligte Institutionen
- Johns Hopkins School of Medicine (14)
- Department of Biomedical Imaging, National Cerebral and Cardiovascular Research Center, Suita, Japan (2)
- Division of Medical Technology and Science, Department of Medical Physics and Engineering, Course of Health Science, Osaka University Graduate School of Medicine, Suita Japan (2)
- Institut for Molecular Biology and CMBI, Department of Genomics, Stem Cell Biology and Regenerative Medicine, Leopold-Franzens-University Innsbruck, Innsbruck, Austria (2)
- Johns Hopkins School of Medicine, The Russell H Morgan Department of Radiology and Radiological Science, Baltimore, MD, USA (2)
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EU-Project number / Contract (GA) number
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Even as medical data sets become more publicly accessible, most are restricted to specific medical conditions. Thus, data collection for machine learning approaches remains challenging, and synthetic data augmentation, such as generative adversarial networks (GAN), may overcome this hurdle. In the present quality control study, deep convolutional GAN (DCGAN)-based human brain magnetic resonance (MR) images were validated by blinded radiologists. In total, 96 T1-weighted brain images from 30 healthy individuals and 33 patients with cerebrovascular accident were included. A training data set was generated from the T1-weighted images and DCGAN was applied to generate additional artificial brain images. The likelihood that images were DCGAN-created versus acquired was evaluated by 5 radiologists (2 neuroradiologists [NRs], vs 3 non-neuroradiologists [NNRs]) in a binary fashion to identify real vs created images. Images were selected randomly from the data set (variation of created images, 40%-60%). None of the investigated images was rated as unknown. Of the created images, the NRs rated 45% and 71% as real magnetic resonance imaging images (NNRs, 24%, 40%, and 44%). In contradistinction, 44% and 70% of the real images were rated as generated images by NRs (NNRs, 10%, 17%, and 27%). The accuracy for the NRs was 0.55 and 0.30 (NNRs, 0.83, 0.72, and 0.64). DCGAN-created brain MR images are similar enough to acquired MR images so as to be indistinguishable in some cases. Such an artificial intelligence algorithm may contribute to synthetic data augmentation for "data-hungry" technologies, such as supervised machine learning approaches, in various clinical applications.
Purpose: We aim to provide an overview of the conventional single photon emission computed tomography (SPECT) and emerging positron emission tomography (PET) catecholamine analogue tracers for assessing myocardial nerve integrity, in particular focusing on \(^{18}\)F-labeled tracers.
Results: Increasingly, the cardiac sympathetic nervous system (SNS) is being studied by non-invasive molecular imaging approaches. Forming the backbone of myocardial SNS imaging, the norepinephrine (NE) transporter at the sympathetic nerve terminal plays a crucial role for visualizing denervated myocardium: in particular, the single-photon-emitting NE analogue \(^{123}\)I-meta-Iodobenzylguanidine (\(^{123}\)I-mIBG) has demonstrated favorable results in the identification of patients at a high risk for cardiac death. However, cardiac neuronal PET agents offer several advantages inlcuding improved spatio-temporal resolution and intrinsic quantifiability. Compared to their \(^{11}\)C-labeled counterparts with a short half-life (20.4 min), novel \(^{18}\)F-labeled PET imaging agents to assess myocardial nerve integrity have the potential to revolutionize the field of SNS molecular imaging: The longer half-life of \(^{18}\)F (109.8 min) allows for more flexibility in the study design and delivery from central cyclotron facilities to smaller hospitals may lead to further cost reduction. A great deal of progress has been made by the first in-human studies of such \(^{18}\)F-labeled SNS imaging agents. Moreover, dedicated animal platforms open avenues for further insights into the handling of radiolabeled catecholamine analogues at the sympathetic nerve terminal. Conclusions: \(^{18}\)F-labeled imaging agents demonstrate key properties for mapping cardiac sympathetic nerve integrity and might outperform current SPECT-based or \(^{11}\)C-labeled tracers in the long run.
BACKGROUND:
Recent developments in cellular reprogramming technology enable the production of virtually unlimited numbers of human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CM). Although hiPSC-CM share various characteristic hallmarks with endogenous cardiomyocytes, it remains a question as to what extent metabolic characteristics are equivalent to mature mammalian cardiomyocytes. Here we set out to functionally characterize the metabolic status of hiPSC-CM in vitro by employing a radionuclide tracer uptake assay.
MATERIAL AND METHODS:
Cardiac differentiation of hiPSC was induced using a combination of well-orchestrated extrinsic stimuli such as WNT activation (by CHIR99021) and BMP signalling followed by WNT inhibition and lactate based cardiomyocyte enrichment. For characterization of metabolic substrates, dual tracer uptake studies were performed with \(^{18}\)F‑2‑fluoro‑2‑deoxy‑d‑glucose (\(^{18}\)F-FDG) and \(^{125}\)I‑β‑methyl‑iodophenyl‑pentadecanoic acid (\(^{125}\)I-BMIPP) as transport markers of glucose and fatty acids, respectively.
RESULTS:
After cardiac differentiation of hiPSCs, in vitro tracer uptake assays confirmed metabolic substrate shift from glucose to fatty acids that was comparable to those observed in native isolated human cardiomyocytes. Immunostaining further confirmed expression of fatty acid transport and binding proteins on hiPSC-CM.
CONCLUSIONS:
During in vitro cardiac maturation, we observed a metabolic shift to fatty acids, which are known as a main energy source of mammalian hearts, suggesting hi-PSC-CM as a potential functional phenotype to investigate alteration of cardiac metabolism in cardiac diseases. Results also highlight the use of available clinical nuclear medicine tracers as functional assays in stem cell research for improved generation of autologous differentiated cells for numerous biomedical applications.