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Disruptive chemical doping in a ferritin-based iron oxide nanoparticle to decrease r2 and enhance detection with T1-weighted MRI.

ABSTRACT: Inorganic doping was used to create flexible, paramagnetic nanoparticle contrast agents for in vivo molecular magnetic resonance imaging (MRI) with low transverse relaxivity (r2). Most nanoparticle contrast agents formed from superparamagnetic metal oxides are developed with high r2. While sensitive, they can have limited in vivo detection due to a number of constraints with T2 or T2*-weighted imaging. T1-weighted imaging is often preferred for molecular MRI, but most T1-shortening agents are small chelates with low metal payload or are nanoparticles that also shorten T2 and limit the range of concentrations detectable with T1-weighting. Here we used tungsten and iron deposition to form doped iron oxide crystals inside the apoferritin cavity to form a WFe nanoparticle with a disordered crystal and un-coupled atomic magnetic moments. The atomic magnetic moments were thus localized, resulting in a principally paramagnetic nanoparticle. The WFe nanoparticles had no coercivity or saturation magnetization at 5 K and sweeping up to ± 20,000 Oe, while native ferritin had a coercivity of 3000 Oe and saturation at ± 20,000 Oe. This tungsten-iron crystal paramagnetism resulted in an increased WFe particle longitudinal relaxivity (r1) of 4870 mm(-1) s(-1) and a reduced transverse relaxivity (r2) of 9076 mm(-1) s(-1) compared with native ferritin. The accumulation of the particles was detected with T1-weighted MRI in concentrations from 20 to 400 nm in vivo, both injected in the rat brain and targeted to the rat kidney glomerulus. The WFe apoferritin nanoparticles were not cytotoxic up to 700 nm particle concentrations, making them potentially important for targeted molecular MRI.

SUBMITTER: Clavijo Jordan MV

PROVIDER: S-EPMC7093841 | biostudies-literature | 2014 Sep-Oct

REPOSITORIES: biostudies-literature

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Disruptive chemical doping in a ferritin-based iron oxide nanoparticle to decrease r2 and enhance detection with T1-weighted MRI.

Clavijo Jordan M Veronica MV Beeman Scott C SC Baldelomar Edwin J EJ Bennett Kevin M KM

Contrast media & molecular imaging 20140425 5

Inorganic doping was used to create flexible, paramagnetic nanoparticle contrast agents for in vivo molecular magnetic resonance imaging (MRI) with low transverse relaxivity (r2). Most nanoparticle contrast agents formed from superparamagnetic metal oxides are developed with high r2. While sensitive, they can have limited in vivo detection due to a number of constraints with T2 or T2*-weighted imaging. T1-weighted imaging is often preferred for molecular MRI, but most T1-shortening agents are sm ...[more]

PMID: 24764110

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Project description:BackgroundParkinson's disease (PD) is the second most common neurodegenerative disease. An objective diagnosis method is urgently needed in clinical practice. In this study, deep learning and radiomics techniques were studied to automatically diagnose PD from healthy controls (HCs).Methods155 PD patients and 154 HCs were randomly divided into a training set (246 patients) and a testing set (63 patients). The brain subregions identification and segmentation were automatically performed with a VB-net, and radiomics features of billateral thalamus, caudatum, putamen and pallidum were extracted. Five independent machine learning classifiers [Support Vector Machine (SVM), Stochastic gradient descent (SGD), random forest (RF), quadratic discriminant analysis (QDA) and decision tree (DT)] were trained on the training set, and validated on the testing. Delong test was used to compare the performance of different models.ResultsOur VB-net could automatically identify and segment the brain into 109 regions. 2,264 radiomics features were automatically extracted from the billateral thalamus, caudatum, putamen or pallidum of each patient. After four step of features dimensionality reduction, Delong tests showed that the SVM model based on combined features had the best performance, with AUCs of 0.988 (95% CI: 0.979 ~ 0.998, specificity = 91.1%, sensitivity =100%, accuracy = 89.4% and precision = 88.2%) and 0.976 (95% CI: 0.942 ~ 1.000, specificity = 100%, sensitivity = 87.1%, accuracy = 93.5% and precision = 88.6%) in the training set and testing set, respectively. Decision curve analysis showed that the clinical benefit of the line graph model was high.ConclusionThe SVM model based on combined features could be used to diagnose PD with high accuracy. Our fully automatic model could rapidly process the MRI data and distinguish PD and HCs in one minute. It greatly improved the diagnostic efficiency and has a great potential value in clinical practice to help the early diagnosis of PD.

Dataset Information

Disruptive chemical doping in a ferritin-based iron oxide nanoparticle to decrease r2 and enhance detection with T1-weighted MRI.

Publications

Disruptive chemical doping in a ferritin-based iron oxide nanoparticle to decrease r2 and enhance detection with T1-weighted MRI.

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