List of publications for the week of January 6, 2022
Estimation of the mechanical properties of a transversely isotropic material from shear wave fields via artificial neural networks
(2022) Journal of the Mechanical Behavior of Biomedical Materials, 126, art. no. 105046, .
Hou, Z.a , Guertler, C.A.a , Okamoto, R.J.a , Chen, H.b c , Garbow, J.R.d , Kamilov, U.S.e , Bayly, P.V.a
a Department of Mechanical Engineering and Materials Science, Washington University in St. Louis, St. Louis, MO 63130, United States
b Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO 63130, United States
c Department of Radiation Oncology, Washington University School of Medicine, Saint Louis, MO 63108, United States
d Biomedical Magnetic Resonance Laboratory, Washington University School of Medicine, 4525 Scott Avenue, CB 8227, St. Louis, MO 63110, United States
e Department of Electrical and Systems Engineering and Computer Science and Engineering, Washington University in St. Louis, St. Louis, MO 63130, United States
Abstract
Artificial neural networks (ANN), established tools in machine learning, are applied to the problem of estimating parameters of a transversely isotropic (TI) material model using data from magnetic resonance elastography (MRE) and diffusion tensor imaging (DTI). We use neural networks to estimate parameters from experimental measurements of ultrasound-induced shear waves after training on analogous data from simulations of a computer model with similar loading, geometry, and boundary conditions. Strain ratios and shear-wave speeds (from MRE) and fiber direction (the direction of maximum diffusivity from diffusion tensor imaging (DTI)) are used as inputs to neural networks trained to estimate the parameters of a TI material (baseline shear modulus μ, shear anisotropy φ, and tensile anisotropy ζ). Ensembles of neural networks are applied to obtain distributions of parameter estimates. The robustness of this approach is assessed by quantifying the sensitivity of property estimates to assumptions in modeling (such as assumed loss factor) and choices in fitting (such as the size of the neural network). This study demonstrates the successful application of simulation-trained neural networks to estimate anisotropic material parameters from complementary MRE and DTI imaging data. © 2021 Elsevier Ltd
Author Keywords
Anisotropy; Artificial neural network; Focused ultrasound; Machine learning; MR elastography
Funding details
National Science FoundationNSFCMMI-17274212
National Institutes of HealthNIHR01 EB027577
Document Type: Article
Publication Stage: Final
Source: Scopus
Biodistribution of Biomimetic Drug Carriers, Mononuclear Cells, and Extracellular Vesicles, in Nonhuman Primates
(2021) Advanced Biology, .
Haney, M.J.a b , Yuan, H.c , Shipley, S.T.d , Wu, Z.c , Zhao, Y.a b , Pate, K.e , Frank, J.E.c , Massoud, N.d , Stewart, P.W.f , Perlmutter, J.S.g , Batrakova, E.V.a b
a Center for NanotechFnology in Drug Delivery, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States
b Eshelman School of Pharmacy, University of North Carolina at Chapel, Hill, Chapel Hill, NC 27599, United States
c Department of Radiology, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, United States
d Division of Comparative Medicine, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States
e Division of Comparative Pathology, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, United States
f Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
g School of Medicine, Washington University in St. Louis, St. Louis, MO, United States
Abstract
Discovery of novel drug delivery systems to the brain remains a key task for successful treatment of neurodegenerative disorders. Herein, the biodistribution of immunocyte-based carriers, peripheral blood mononuclear cells (PBMCs), and monocyte-derived EVs are investigated in adult rhesus macaques using longitudinal PET/MRI imaging. 64Cu-labeled drug carriers are introduced via different routes of administration: intraperitoneal (IP), intravenous (IV), or intrathecal (IT) injection. Whole body PET/MRI (or PET/CT) images are acquired at 1, 24, and 48 h post injection of 64Cu-labeled drug carriers, and standardized uptake values (SUVmean and SUVmax) in the main organs are estimated. The brain retention for both types of carriers increases based on route of administration: IP < IV < IT. Importantly, a single IT injection of PBMCs produces higher brain retention compared to IT injection of EVs. In contrast, EVs show superior brain accumulation compared to the cells when administered via IP and IV routes, respectively. Finally, a comprehensive chemistry panel of blood samples demonstrates no cytotoxic effects of either carrier. Overall, living cells and EVs have a great potential to be used for drug delivery to the brain. When identifying the ideal drug carrier, the route of administration could make big differences in CNS drug delivery. © 2021 The Authors. Advanced Biology published by Wiley-VCH GmbH
Author Keywords
brain bioavailability; drug delivery system; extracellular vesicles; monocytes; nonhuman primates
Funding details
17846, MJFF‐009726
National Institutes of HealthNIH1R01NS112019, 1RO1 NS102412
Eshelman Institute for Innovation, University of North Carolina at Chapel HillEII UNC 38‐124
Document Type: Article
Publication Stage: Article in Press
Source: Scopus