Dataset Information

MRI/SPECT/Fluorescent Tri-Modal Probe for Evaluating the Homing and Therapeutic Efficacy of Transplanted Mesenchymal Stem Cells in a Rat Ischemic Stroke Model.

ABSTRACT: Quantitatively tracking engraftment of intracerebrally or intravenously transplanted stem cells and evaluating their concomitant therapeutic efficacy for stroke has been a challenge in the field of stem cell therapy. In this study, first, an MRI/SPECT/fluorescent tri-modal probe (125I-fSiO4@SPIOs) is synthesized for quantitatively tracking mesenchymal stem cells (MSCs) transplanted intracerebrally or intravenously into stroke rats, and then the therapeutic efficacy of MSCs delivered by both routes and the possible mechanism of the therapy are evaluated. It is demonstrated that (125)I-fSiO4@SPIOs have high efficiency for labeling MSCs without affecting their viability, differentiation, and proliferation capacity, and found that 35% of intracerebrally injected MSCs migrate along the corpus callosum to the lesion area, while 90% of intravenously injected MSCs remain trapped in the lung at 14 days after MSC transplantation. However, neurobehavioral outcomes are significantly improved in both transplantation groups, which are accompanied by increases of vascular endothelial growth factor, basic fibroblast growth factor, and tissue inhibitor of metalloproteinases-3 in blood, lung, and brain tissue (p < 0.05). The study demonstrates that 125I-fSiO4@SPIOs are robust probe for long-term tracking of MSCs in the treatment of ischemic brain and MSCs delivered via both routes improve neurobehavioral outcomes in ischemic rats.

SUBMITTER: Tang Y

PROVIDER: S-EPMC4539160 | biostudies-literature | 2015 Feb

REPOSITORIES: biostudies-literature

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Publications

MRI/SPECT/Fluorescent Tri-Modal Probe for Evaluating the Homing and Therapeutic Efficacy of Transplanted Mesenchymal Stem Cells in a Rat Ischemic Stroke Model.

Tang Yaohui Y Zhang Chunfu C Wang Jixian J Lin Xiaojie X Zhang Lu L Yang Yi Y Wang Yongting Y Zhang Zhijun Z Bulte Jeff W M JW Yang Guo-Yuan GY

Advanced functional materials 20150201 7

Quantitatively tracking engraftment of intracerebrally or intravenously transplanted stem cells and evaluating their concomitant therapeutic efficacy for stroke has been a challenge in the field of stem cell therapy. In this study, first, an MRI/SPECT/fluorescent tri-modal probe (<sup>125</sup>I-fSiO4@SPIOs) is synthesized for quantitatively tracking mesenchymal stem cells (MSCs) transplanted intracerebrally or intravenously into stroke rats, and then the therapeutic efficacy of MSCs delivered b ...[more]

PMID: 26290659

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Project description:ObjectiveIncreased risk of stroke is highly associated with psychiatric disorders. We aimed to conduct the machine learning model based on multi-modal magnetic resonance imaging (MRI) radiomics predicting the prognosis of ischemic stroke.MethodsThis study retrospectively analyzed 148 patients with acute ischemic stroke due to anterior circulation artery occlusion. Based on the modified Rankin Scale (mRS) score, patients were divided into good (mRS ≤ 2) and poor (mRS > 2) outcome groups. Segmentation of the infarct region was performed by manually outlining a mask of the lesion on diffusion-weighted images (DWI) using MRIcron software. The apparent diffusion coefficient (ADC), fluid decay inversion recoverage (FLAIR), susceptibility weighted imaging (SWI) and T1-weighted (T1w) images were aligned to the DWI images and the radiomic features within the lesion area were extracted for each image modality. The calculations were done using pyradiomics software and a total of 4,744 stroke-related imaging features were automatically calculated. Next, feature selection based on recursive feature elimination was used for each modality and three radiomic features were extracted from each modality plus one feature from the lesion mask, for a total of 16 radiomic features. At last, five machine learning (ML) models were trained and tested to predict stroke prognosis, calculate the received operating characteristic (ROC) curves and other parameters, evaluate the performance of the models and validate their predictive efficacy by five-fold cross-validation.ResultsSixteen radiomic features were selected to construct the ML models for prognostic classification. By five-fold cross-validation, light gradient boosting machine (LightGBM) model-based muti-modal MRI radiomic features performed best in binary prognostic classification with accuracy of 0.831, sensitivity of 0.739, specificity of 0.902, F1-score of 0.788 and an area under the curve (AUC) of 0.902.ConclusionThe ML models based on muti-modal MRI radiomics are of high value for predicting clinical outcomes in acute stroke patients.

Dataset Information

MRI/SPECT/Fluorescent Tri-Modal Probe for Evaluating the Homing and Therapeutic Efficacy of Transplanted Mesenchymal Stem Cells in a Rat Ischemic Stroke Model.

Publications

MRI/SPECT/Fluorescent Tri-Modal Probe for Evaluating the Homing and Therapeutic Efficacy of Transplanted Mesenchymal Stem Cells in a Rat Ischemic Stroke Model.

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