204 research outputs found
sj-docx-1-tae-10.1177_20420188241229540 – Supplemental material for Role of epicardial adipose tissue in diabetic cardiomyopathy through the lens of cardiovascular magnetic resonance imaging – a narrative review
No full textSupplemental material, sj-docx-1-tae-10.1177_20420188241229540 for Role of epicardial adipose tissue in diabetic cardiomyopathy through the lens of cardiovascular magnetic resonance imaging – a narrative review by Sindhoora Kotha, Sven Plein, John P. Greenwood and Eylem Levelt in Therapeutic Advances in Endocrinology and Metabolism</p
The role of rest and stress cardiac energy metabolism and ectopic lipid deposition in diabetic cardiomyopathy
No full textType 2 diabetes mellitus (T2DM) is associated with an increased risk of heart failure and cardiovascular mortality even in the absence of coronary artery disease. Although the reasons for this are not clear, impaired cardiac high energy phosphate metabolism, coronary microvascular dysfunction and ectopic lipid deposition have emerged among the candidate mechanisms. Cardiac magnetic resonance imaging (CMR) and magnetic resonance spectroscopy (MRS) are powerful tools to characterise these conditions. The findings here suggest that, in diabetes, coronary microvascular dysfunction may potentially exacerbate derangement of cardiac energetics under conditions of increased workload. The relationships amongst ectopic adiposity (myocardial, epicardial and hepatic), myocardial metabolic changes and left ventricular remodelling in T2DM were determined using proton magnetic resonance spectroscopy (1H-MRS), cardiac computer tomography (CT) and multi-parametric liver MR. It was found that cardiac steatosis independently predicted left ventricular concentric remodelling and impairment of systolic strain in patients with T2DM. This work also demonstrated that ectopic adiposity is linked to insulin resistance, liver fibrosis and inflammation, and cardiac contractile dysfunction in diabetes, and that the coexistence of obesity and T2DM leads to higher epicardial fat volumes and significant non-alcoholic fatty liver disease compared to T2DM alone. These findings suggest that, since cardiac steatosis and ectopic adiposity are modifiable, strategies aimed at reducing myocardial triglyceride levels may reverse concentric remodelling and improve contractile function in the diabetic heart. The work on field strength effects on cardiac 31P-MRS has shown that 7T is feasible and reliable with reduced error and increased signal to noise compared to 3T. In summary, the work in this thesis demonstrates the powerful role of CMR and MRS in elucidating the detrimental effects that originate from the metabolic abnormalities in the diabetic heart
Reply: Ectopic Fat Deposition and Diabetes Mellitus.
No full textReply to Drs. Gaborit and Dutour re: "Ectopic Fat Deposition and Diabetes Mellitus" Journal of the American College of Cardiology 68 (23) pp.2594-2595
Advanced spectroscopic imaging techniques for X-nuclei cardiac magnetic resonance imaging
No full textChanges to cardiac metabolism are an important component of heart failure. Understanding these metabolic changes is therefore critical to developing treatments for the failing heart, particularly pharmaceuticals, or other interventions, which aim to restore normal metabolism. A combination of hyperpolarized 13C and 31P magnetic resonance spectroscopic imaging (MRSI) enables quantification of both the substrate usage (13C) and energy output (31P) of cardiac metabolism, allowing a full picture of the metabolic state of the heart to be built up.
Both hyperpolarized 13C and 31P MRSI present significant experimental challenges, which I will address, through the use of advanced acquisition and reconstruction techniques, in this thesis.
The main challenges associated with hyperpolarized 13C MRSI are the short transverse relaxation time of the metabolites, necessitating very rapid readouts, the short half-life of hyperpolarization, which requires the full scan to complete in approximately two minutes, and the bolus injection of hyperpolarized material to the patient, which makes the signal intensity highly dynamic. To address these three challenges, I implemented a hybrid multiple/single-shot spiral sequence, with a rapid readout, which enabled the usual trade-off, inherent to multiple-shot spiral readouts, of temporal and spatial resolution (when selecting the number of shots), to be made at reconstruction, rather than acquisition time.
The principal challenge of 31P MRSI however, is achieving adequate SNR. The low SNR of 31P spectroscopy necessitates significant averaging during the acquisition, which extends scan time and precludes high spatial resolution imaging within a reasonable scan time. In this work I implement two compartmentalized reconstruction techniques, SLAM and SLIM, and apply them at both 3 and 7T, before investigating acquisition parameters, which lead to an improvement in SNR and repeatability over the existing technique used in OCMR.
Together these technological advances have extended our ability to quantify metabolism, which will, through their clinical application, enhance our understanding of heart failure
Oog voor klimaat en luchtkwaliteit
No full textGeoscience and Remote SensingCivil Engineering and Geoscience
Are language production problems apparent in adults who no longer meet diagnostic criteria for attention-deficit/hyperactivity disorder?
No full textIn this study, we examined sentence production in a sample of adults (N = 21) who had had attention-deficit/hyperactivity disorder (ADHD) as children, but as adults no longer met DSM-IV diagnostic criteria (APA, 2000). This “remitted” group was assessed on a sentence production task. On each trial, participants saw two objects and a verb. Their task was to construct a sentence using the objects as arguments of the verb. Results showed more ungrammatical and disfluent utterances with one particular type of verb (i.e., participle). In a second set of analyses, we compared the remitted group to both control participants and a “persistent” group, who had ADHD as children and as adults. Results showed that remitters were more likely to produce ungrammatical utterances and to make repair disfluencies compared to controls, and they patterned more similarly to ADHD participants. Conclusions focus on language output in remitted ADHD, and the role of executive functions in language production
Predicting myocardial infarction through retinal scans and minimal personal information
No full textIn ophthalmologic practice, retinal images are routinely obtained to diagnose and monitor primary eye diseases and systemic conditions affecting the eye, such as diabetic retinopathy. Recent studies have shown that biomarkers on retinal images, for example, retinal blood vessel density or tortuosity, are associated with cardiac function and may identify patients at risk of coronary artery disease. In this work we investigate the use of retinal images, alongside relevant patient metadata, to estimate left ventricular mass and left ventricular end-diastolic volume, and subsequently, predict incident myocardial infarction. We trained a multichannel variational autoencoder and a deep regressor model to estimate left ventricular mass (4.4 (–32.30, 41.1) g) and left ventricular end-diastolic volume (3.02 (–53.45, 59.49) ml) and predict risk of myocardial infarction (AUC = 0.80 ± 0.02, sensitivity = 0.74 ± 0.02, specificity = 0.71 ± 0.03) using just the retinal images and demographic data. Our results indicate that one could identify patients at high risk of future myocardial infarction from retinal imaging available in every optician and eye clinic
Aerosol Absorption from Global Satellite Measurements in the Ultra-Violet: From Qualitative Aerosol Index to Quantitative Aerosol Absorptive Properties
No full textAtmospheric aerosols are solid or liquid particles suspended in the air. The majority of them are produced by natural processes, including sea salt from oceans, mineral dust from (semi-)arid regions, carbon containing particles from wildfires, and sulfates and ash from volcanic activities. Anthropogenic aerosols are produced by industrial activities, power generation, transportation, agriculture, and human-induced biomass burning events. Depending on the meteorological conditions, aerosol particles can stay in the atmosphere for several hours to several months and can be transported over long distances, causing adverse effects on human health, visibility and climate.This thesis focuses on the aerosol optical properties, particularly the light absorption of the aerosol particles that has significant effects on the Earth’s climate system. This thesis starts with a general introduction of atmospheric aerosols, including its sources, categories, physical properties and measurement techniques (Chapter 1). Next, the Ultra-Violet Aerosol Index (UVAI) is introduced, which is calculated from satellite measurements of the radiance at two wavelengths in the UV. UVAI contains information of aerosol absorption, and it has a very long andalmost continuous data record starting in 1978. Direct use of UVAI is challenging because it is not a geophysical quantity, but a numerical index. The objective of this thesis is to derive quantitative properties on aerosol absorption from the UVAI (e.g. single scattering albedo, absorption aerosol optical depth) that can be directly used in aerosol radiative transfer assessments. Two types of methods have been developed, i.e. physically-based methods and statistically-based methods. The first compares the observed UVAI to the one simulated by radiative transfer models. The second uses Machine Learning algorithms trained by existing data sets.The physically-based methods have been applied to quantify aerosol absorption of several large scale wildfires (Chapter 2 and 3). An important challenge of these method is that assumptions have to be made on the aerosol micro-physical properties, leading to significant uncertainties in the results, whereas theMachine Learning-based methods can avoid this kind of assumptions. Chapter 3 investigates the feasibility to quantify aerosol absorption from UVAI using a Machine Learning algorithm. Despite the higher computational efficiency and better results, the application of such data-driven methods is still restricted by the limited data on the aerosol vertical distribution. Therefore, in Chapter4, a database of aerosol height is created from a chemistry transport model. This database is applied in Chapter 5, where a Deep Neural Network method is used to derive the quantitative aerosol absorptive properties from the OMI/Aura UVAI for the period from 2006 to 2019. In comparison to ground-based observations, the results of the Deep Neural Network agree better than satellite retrievals and also better than chemistry transport model simulations.This thesis demonstrates the feasibility of deriving quantitative aerosol absorptive properties from the satellite retrieved UVAI.We use traditional radiative transfer simulations meanwhile investigating the new possibilities of data-driven methods in aerosol remote sensing. Although the retrieval results are encouraging, there remain limitations and challenges which need to be addressed. These are discussed in Chapter 6 with corresponding suggestions and prospects. Despite the challenges, it is expected that a synthetic database of global aerosol absorption can be derived fromUVAI observations provided by multiple satellite products. Such a data set will make great contributions to quantify the effect of absorbing aerosols on the climate system.Atmospheric Remote Sensin
Satellite-derived NO<sub>x</sub> emissions over East Asia
No full textNitrogen oxides (NOx) are important air pollutants and play a crucial role in climate change. NOx emissions are important for chemical transport models to simulate and forecast air quality. Up-to-date emission information also helps policymakers to mitigate air pollution. In this thesis, we have focused on providing better NOx emission estimates with the DECSO (Daily Emission estimates Constrained by Satellite Observations) inversion algorithm applied to satellite observations. DECSO is a fast algorithm, which enables daily emissions estimates as soon as the satellite observations are available. Satellite-derived emissions reveal more specific information on the location and strength of sources than concentration observations. The monthly and yearly variability in emissions are well captured. This is demonstrated by our monitoring of the effect of air quality regulations on emissions during events like the 2014 Youth Olympic Games. Near the Chinese coast ship tracks, which are otherwise hidden under the outflow of air pollution from the mainland, are revealed in our NOx emissions derived with DECSO applied to OMI satellite observations. Trends of shipping emissions for a 10-year period (2007 to 2016) over Chinese seas are presented for the first time.Atmospheric Remote Sensin
Global Mapping of Atmospheric Composition from Space: Retrieving Aerosol Height and Tropospheric NO2 from OMI
No full textThe main objective of this thesis is to design a new aerosol layer height retrieval in order to improve the operational NO2 retrieval, both in the troposphere, from space-borne instruments for highly polluted events and under cloud-free conditions. This thesis focuses on the exploitation of the OMI satellite measurements acquired in the visible wavelength range (405-490 nm). In addition, we develop numerical methods and tools (e.g. machine learning) in order to support the operational processing of big data amounts from the forthcoming new-generation satellite instruments for air quality and climate research.Atmospheric Remote Sensin
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