MiR-219a-5p Enriched Extracellular Vesicles Induce OPC Differentiation and EAE Improvement More Efficiently Than Liposomes and Polymeric Nanoparticles

Remyelination is a key aspect in multiple sclerosis pathology and a special effort is being made to promote it. However, there is still no available treatment to regenerate myelin and several strategies are being scrutinized. Myelination is naturally performed by oligodendrocytes and microRNAs have been postulated as a promising tool to induce oligodendrocyte precursor cell differentiation and therefore remyelination. Herein, DSPC liposomes and PLGA nanoparticles were studied for miR-219a-5p encapsulation, release and remyelination promotion. In parallel, they were compared with biologically engineered extracellular vesicles overexpressing miR-219a-5p. Interestingly, extracellular vesicles showed the highest oligodendrocyte precursor cell differentiation levels and were more effective than liposomes and polymeric nanoparticles crossing the blood–brain barrier. Finally, extracellular vesicles were able to improve EAE animal model clinical evolution. Our results indicate that the use of extracellular vesicles as miR-219a-5p delivery system can be a feasible and promising strategy to induce remyelination in multiple sclerosis patients.This work was supported by Carlos III Institute, (PI17/00189 and DTS15/00069), by Fondo Europeo de Desarrollo Regional—FEDER, by the Gipuzkoa Regional Council (DFG 15/006), by grant from the Basque Government (RIS3/DTS/2018222025), by the Department of Industry of the Basque Country (ELKARTEK 16/014), and the Spanish State Research Agency (SAF2017-87670-R) and Maria de Maeztu Units of Excellence Program Grant MDM-2017-0720). I.O.-Q., A.A. and L.I. were supported by the Department of Education of the Basque Government. IOQ and LAN were supported by EMBO short Term Fellowship Programme. LAN was supported by a Canadian graduate scholarship from the Canadian Institutes of Health Research (CGS-D CIHR).PRC was supported by Ikerbasque, the Basque Foundation for Science

Diagnosing and mapping pulmonary emphysema on X-ray projection images

To assess whether grating-based X-ray dark-field imaging can increase the sensitivity of X-ray projection images in the diagnosis of pulmonary emphysema and allow for a more accurate assessment of emphysema distribution. Lungs from three mice with pulmonary emphysema and three healthy mice were imaged ex vivo using a laser-driven compact synchrotron X-ray source. Median signal intensities of transmission (T), dark-field (V) and a combined parameter (normalized scatter) were compared between emphysema and control group. To determine the diagnostic value of each parameter in differentiating between healthy and emphysematous lung tissue, a receiver-operating-characteristic (ROC) curve analysis was performed both on a per-pixel and a per-individual basis. Parametric maps of emphysema distribution were generated using transmission, dark-field and normalized scatter signal and correlated with histopathology. Transmission values relative to water were higher for emphysematous lungs than for control lungs (1.11 vs. 1.06, p<0.001). There was no difference in median dark-field signal intensities between both groups (0.66 vs. 0.66). Median normalized scatter was significantly lower in the emphysematous lungs compared to controls (4.9 vs. 10.8, p<0.001), and was the best parameter for differentiation of healthy vs. emphysematous lung tissue. In a per-pixel analysis, the area under the ROC curve (AUC) for the normalized scatter value was significantly higher than for transmission (0.86 vs. 0.78, p<0.001) and dark-field value (0.86 vs. 0.52, p<0.001) alone. Normalized scatter showed very high sensitivity for a wide range of specificity values (94% sensitivity at 75% specificity). Using the normalized scatter signal to display the regional distribution of emphysema provides color-coded parametric maps, which show the best correlation with histopathology. In a murine model, the complementary information provided by X-ray transmission and dark-field images adds incremental diagnostic value in detecting pulmonary emphysema and visualizing its regional distribution as compared to conventional X-ray projections

Computed Tomography Imaging of Primary Lung Cancer in Mice Using a Liposomal-Iodinated Contrast Agent

To investigate the utility of a liposomal-iodinated nanoparticle contrast agent and computed tomography (CT) imaging for characterization of primary nodules in genetically engineered mouse models of non-small cell lung cancer.Primary lung cancers with mutations in K-ras alone (Kras(LA1)) or in combination with p53 (LSL-Kras(G12D);p53(FL/FL)) were generated. A liposomal-iodine contrast agent containing 120 mg Iodine/mL was administered systemically at a dose of 16 µl/gm body weight. Longitudinal micro-CT imaging with cardio-respiratory gating was performed pre-contrast and at 0 hr, day 3, and day 7 post-contrast administration. CT-derived nodule sizes were used to assess tumor growth. Signal attenuation was measured in individual nodules to study dynamic enhancement of lung nodules.A good correlation was seen between volume and diameter-based assessment of nodules (R(2)>0.8) for both lung cancer models. The LSL-Kras(G12D);p53(FL/FL) model showed rapid growth as demonstrated by systemically higher volume changes compared to the lung nodules in Kras(LA1) mice (p<0.05). Early phase imaging using the nanoparticle contrast agent enabled visualization of nodule blood supply. Delayed-phase imaging demonstrated significant differential signal enhancement in the lung nodules of LSL-Kras(G12D);p53(FL/FL) mice compared to nodules in Kras(LA1) mice (p<0.05) indicating higher uptake and accumulation of the nanoparticle contrast agent in rapidly growing nodules.The nanoparticle iodinated contrast agent enabled visualization of blood supply to the nodules during the early-phase imaging. Delayed-phase imaging enabled characterization of slow growing and rapidly growing nodules based on signal enhancement. The use of this agent could facilitate early detection and diagnosis of pulmonary lesions as well as have implications on treatment response and monitoring

Multiparametric Imaging and MR Image Texture Analysis in Brain Tumors

Discrimination of tumor from radiation injured (RI) tissues and differentiation of tumor types using noninvasive imaging is essential for guiding surgical and radiotherapy treatments are some of the challenges that clinicians face in the course of treatment of brain tumors. The first objective in this thesis was to develop a method to discriminate between glioblastoma tumor recurrences and radiation injury using multiparametric characterization of the tissue incorporating conventional magnetic resonance imaging signal intensities and diffusion tensor imaging parameters. Our results show significant correlations in the RI that was missing in the tumor regions. These correlations may aid in differentiating between tumor recurrence and RI. The second objective of was to investigate whether texture based image analysis of routine MR images would provide quantitative information that could be used to differentiate between glioblastoma and metastasis. Our results demonstrate that first-order texture feature of standard deviation and second-order texture features of entropy, inertia, homogeneity, and energy show significant differences between the two groups. The third objective was to investigate whether quantitative measurements of tumor size and appearance on MRI scans acquired prior to helical tomotherapy (HT) type whole brain radiotherapy with simultaneous infield boost treatment could be used to differentiate responder and non-responder patient groups. Our results demonstrated that smaller size lesions may respond better to this type of radiation therapy. Measures of appearance provided limited added value over measures of size for response prediction. Quantitative measurements of rim enhancement and core necrosis performed separately did not provide additional predictive value

Radiomics in Cardiovascular Disease Imaging: from Pixels to the Heart of the Problem

Purpose of Review This review of the literature aims to present potential applications of radiomics in cardiovascular radiology and, in particular, in cardiac imaging. Recent Findings Radiomics and machine learning represent a technological innovation which may be used to extract and analyze quantitative features from medical images. They aid in detecting hidden pattern in medical data, possibly leading to new insights in pathophysiology of different medical conditions. In the recent literature, radiomics and machine learning have been investigated for numerous potential applications in cardiovascular imaging. They have been proposed to improve image acquisition and reconstruction, for anatomical structure automated segmentation or automated characterization of cardiologic diseases. Summary The number of applications for radiomics and machine learning is continuing to rise, even though methodological and implementation issues still limit their use in daily practice. In the long term, they may have a positive impact in patient management

Unified adaptive framework for contrast enhancement of blood vessels

Information about blood vessel structures influences a lot of diseases in the medical realm. Therefore, for proper localization of blood vessels, its contrast should be enhanced properly. Since the blood vessels from all the medical angio-images have almost similar properties, a unified approach for the contrast enhancement of blood vessel structures is very useful. This paper aims to enhance the contrast of the blood vessels as well as the overall contrast of all the medical angio-images. In the proposed method, initially, the vessel probability map is extracted using hessian eigenanalysis. From the map, vessel edges and textures are derived and summed at every pixel location to frame a unique fractional differential function. The resulting fractional value from the function gives out the most optimal fractional order that can be adjusted to improve the contrast of blood vessels by convolving the image using Grunwald-Letnikov (G-L) fractional differential kernel. The vessel enhanced image is Gaussian fitted and contrast stretched to get overall contrast enhancement. This method of enhancement, when applied to medical angio-images such as the retinal fundus, Computerised Tomography (CT), Coronary Angiography (CA) and Digital Subtraction Angiography (DSA), has shown improved performance validated by the performance metrics

Cardiovascular Magnetic Resonance Myocardial Perfusion Mapping for the Assessment of Coronary Artery Disease

Pixelwise myocardial perfusion mapping is a novel cardiovascular magnetic resonance (CMR) technique enables quantitative measurement of myocardial blood flow (MBF) at a pixel level. This could improve the accuracy of detection of obstructive coronary artery disease (CAD) and may also have a role in the diagnosis and assessment of coronary microvascular dysfunction (CMD). In this thesis, I explore the use of this novel technique in cohorts of clinical patients and controls with suspected CAD or CMD. Firstly, I demonstrate that stress MBF measured using perfusion mapping is accurate for the detection of CAD using invasive fractional flow reserve (FFR) as the reference standard, and that global stress MBF can be used as a marker of CMD using invasive index of microcirculatory resistance (IMR) as the reference standard. One limitation of adenosine stress testing is the confirmation of adequate hyperaemia with lack of gold standard non-invasive marker. Here, I demonstrate that regional stress MBF can be utilised as a non-invasive marker of adequate stress response. Another limitation of stress MBF is the relatively poor performance for the detection of multivessel disease. In a cohort of patients with confirmed two- and three-vessel disease I demonstrate that perfusion mapping is superior to visual analysis for the correct identification of disease severity. Perfusion mapping provides a host of options for quantitative image analysis. I show that the most reliable method for detection of coronary disease at a patient level is the presence of reduced MBF in two adjacent myocardial segments. In summary, in this thesis I performed a series of studies investigating the clinical utilisation of CMR perfusion mapping that can be translated to clinical practice to enhance the performance of stress perfusion CMR

Clinical quantitative cardiac imaging for the assessment of myocardial ischaemia

Cardiac imaging has a pivotal role in the prevention, diagnosis and treatment of ischaemic heart disease. SPECT is most commonly used for clinical myocardial perfusion imaging, whereas PET is the clinical reference standard for the quantification of myocardial perfusion. MRI does not involve exposure to ionizing radiation, similar to echocardiography, which can be performed at the bedside. CT perfusion imaging is not frequently used but CT offers coronary angiography data, and invasive catheter-based methods can measure coronary flow and pressure. Technical improvements to the quantification of pathophysiological parameters of myocardial ischaemia can be achieved. Clinical consensus recommendations on the appropriateness of each technique were derived following a European quantitative cardiac imaging meeting and using a real-time Delphi process. SPECT using new detectors allows the quantification of myocardial blood flow and is now also suited to patients with a high BMI. PET is well suited to patients with multivessel disease to confirm or exclude balanced ischaemia. MRI allows the evaluation of patients with complex disease who would benefit from imaging of function and fibrosis in addition to perfusion. Echocardiography remains the preferred technique for assessing ischaemia in bedside situations, whereas CT has the greatest value for combined quantification of stenosis and characterization of atherosclerosis in relation to myocardial ischaemia. In patients with a high probability of needing invasive treatment, invasive coronary flow and pressure measurement is well suited to guide treatment decisions. In this Consensus Statement, we summarize the strengths and weaknesses as well as the future technological potential of each imaging modality