Dataset Information

Monitoring of Allogeneic Islet Grafts in Nonhuman Primates Using MRI.

ABSTRACT: Information regarding the longevity of transplanted pancreatic islet grafts could provide valuable information for treatment options. In our previous studies, we showed that isolated autologous pancreatic islets could be labeled with iron oxide nanoparticles and monitored after transplantation using MRI. Here, we report on in vivo monitoring of a secondary damage that occurs at the later stages because of allogeneic immune rejection.In the proof-of-principle studies, iron oxide-labeled autologous pancreatic islets were transplanted under the renal capsules of nonhuman primates. To demonstrate acute graft loss, the animals were injected with streptozotocin. Graft monitoring was performed by in vivo MRI. Next, iron oxide-labeled allogeneic islets were transplanted into the liver and monitored by MRI after withdrawal of immunosuppression.In autologous model, we observed a pronounced drop in graft volume after streptozotocin challenge as assessed by MRI. In allogeneic model of islet transplantation, there was an initial islet loss after the procedure followed by relative stabilization of the graft volume. After immunosuppression was discontinued, there was a noticeable drop in graft volume that gradually continued during the course of the study. Importantly, the loss of graft volume observed on MR preceded the raise in blood glucose.This study demonstrated that in vivo MRI was able to reveal graft volume loss before any changes in blood glucose that can be measured by standard methods. We believe that these results could provide means for clinicians to follow islet fate noninvasively and longitudinally using clinically relevant scanners.

SUBMITTER: Wang P

PROVIDER: S-EPMC4551574 | biostudies-literature | 2015 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Monitoring of Allogeneic Islet Grafts in Nonhuman Primates Using MRI.

Wang Ping P Schuetz Christian C Vallabhajosyula Prashanth P Medarova Zdravka Z Tena Aseda A Wei Lingling L Yamada Kazuhiko K Deng Shaoping S Markmann James F JF Sachs David H DH Moore Anna A

Transplantation 20150801 8

<h4>Background</h4>Information regarding the longevity of transplanted pancreatic islet grafts could provide valuable information for treatment options. In our previous studies, we showed that isolated autologous pancreatic islets could be labeled with iron oxide nanoparticles and monitored after transplantation using MRI. Here, we report on in vivo monitoring of a secondary damage that occurs at the later stages because of allogeneic immune rejection.<h4>Methods</h4>In the proof-of-principle st ...[more]

PMID: 25806407

Similar Datasets

Project description:Brain extraction or skull stripping of magnetic resonance images (MRI) is an essential step in neuroimaging studies, the accuracy of which can severely affect subsequent image processing procedures. Current automatic brain extraction methods demonstrate good results on human brains, but are often far from satisfactory on nonhuman primates, which are a necessary part of neuroscience research. To overcome the challenges of brain extraction in nonhuman primates, we propose a fully-automated brain extraction pipeline combining deep Bayesian convolutional neural network (CNN) and fully connected three-dimensional (3D) conditional random field (CRF). The deep Bayesian CNN, Bayesian SegNet, is used as the core segmentation engine. As a probabilistic network, it is not only able to perform accurate high-resolution pixel-wise brain segmentation, but also capable of measuring the model uncertainty by Monte Carlo sampling with dropout in the testing stage. Then, fully connected 3D CRF is used to refine the probability result from Bayesian SegNet in the whole 3D context of the brain volume. The proposed method was evaluated with a manually brain-extracted dataset comprising T1w images of 100 nonhuman primates. Our method outperforms six popular publicly available brain extraction packages and three well-established deep learning based methods with a mean Dice coefficient of 0.985 and a mean average symmetric surface distance of 0.220?mm. A better performance against all the compared methods was verified by statistical tests (all p-values?<?10-4, two-sided, Bonferroni corrected). The maximum uncertainty of the model on nonhuman primate brain extraction has a mean value of 0.116 across all the 100 subjects. The behavior of the uncertainty was also studied, which shows the uncertainty increases as the training set size decreases, the number of inconsistent labels in the training set increases, or the inconsistency between the training set and the testing set increases.

Dataset Information

Monitoring of Allogeneic Islet Grafts in Nonhuman Primates Using MRI.

Publications

Monitoring of Allogeneic Islet Grafts in Nonhuman Primates Using MRI.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets