MRI overview for fat quantification in non-alcoholic fatty liver disease in the clinical and research settings

The general purpose of this master’s thesis is to describe the MRI techniques used in scanning and post processing for quantifying liver fat percentages for the purpose of diagnosis and research. At the onset we will look at epidemiological data regarding nonalcoholic fatty liver disease, which is often called by the name of hepatic steatosis. Based on the prevalence of this disease it is worthwhile to fully understand non-invasive (MRI) analysis, and its use in the clinical and research setting. Following an introductory section regarding the basis of magnetic resonance imaging, we will take a more in-depth look at current methods utilized for liver fat quantification. Due to the massive population of those of suffer from this disease worldwide it is prudent to analyze current methods, as well as the implications that such research has and will have on the pharmaceutical approach to treating this disease. The purpose of this thesis is to elucidate the MRI techniques utilized for liver fat quantification and provide a comprehensive view of how these techniques are used for diagnosis in the clinical setting, and longitudinal studies in the research setting to measure liver fat levels and how they react to various treatment approaches

Spectral tracing of deuterium for imaging glucose metabolism.

Cells and tissues often display pronounced spatial and dynamical metabolic heterogeneity. Common glucose-imaging techniques report glucose uptake or catabolism activity, yet do not trace the functional utilization of glucose-derived anabolic products. Here we report a microscopy technique for the optical imaging, via the spectral tracing of deuterium (STRIDE), of diverse macromolecules derived from glucose. Based on stimulated Raman-scattering imaging, STRIDE visualizes the metabolic dynamics of newly synthesized macromolecules, such as DNA, protein, lipids and glycogen, via the enrichment and distinct spectra of carbon-deuterium bonds transferred from the deuterated glucose precursor. STRIDE can also use spectral differences derived from different glucose isotopologues to visualize temporally separated glucose populations using a pulse-chase protocol. We also show that STRIDE can be used to image glucose metabolism in many mouse tissues, including tumours, brain, intestine and liver, at a detection limit of 10 mM of carbon-deuterium bonds. STRIDE provides a high-resolution and chemically informative assessment of glucose anabolic utilization

Passive element enriched photoacoustic computed tomography (PER PACT) for simultaneous imaging of acoustic propagation properties and light absorption\ud

We present a ‘hybrid’ imaging approach which can image both light absorption properties and acoustic transmission properties of an object in a two-dimensional slice using a computed tomography (CT) photoacoustic imager. The ultrasound transmission measurement method uses a strong optical absorber of small cross-section placed in the path of the light illuminating the sample. This absorber, which we call a passive element acts as a source of ultrasound. The interaction of ultrasound with the sample can be measured in transmission, using the same ultrasound detector used for photoacoustics. Such measurements are made at various angles around the sample in a CT approach. Images of the ultrasound propagation parameters, attenuation and speed of sound, can be reconstructed by inversion of a measurement model. We validate the method on specially designed phantoms and biological specimens. The obtained images are quantitative in terms of the shape, size, location, and acoustic properties of the examined heterogeneitie

Label-free high-throughput photoacoustic tomography of suspected circulating melanoma tumor cells in patients in vivo

Significance: Detection and characterization of circulating tumor cells (CTCs), a key determinant of metastasis, are critical for determining risk of disease progression, understanding metastatic pathways, and facilitating early clinical intervention. Aim: We aim to demonstrate label-free imaging of suspected melanoma CTCs. Approach: We use a linear-array-based photoacoustic tomography system (LA-PAT) to detect melanoma CTCs, quantify their contrast-to-noise ratios (CNRs), and measure their flow velocities in most of the superficial veins in humans. Results: With LA-PAT, we successfully imaged suspected melanoma CTCs in patients in vivo, with a CNR >9. CTCs were detected in 3 of 16 patients with stage III or IV melanoma. Among the three CTC-positive patients, two had disease progression; among the 13 CTC-negative patients, 4 showed disease progression. Conclusions: We suggest that LA-PAT can detect suspected melanoma CTCs in patients in vivo and has potential clinical applications for disease monitoring in melanoma

Effects of synbiotic supplement on human gut microbiota, body composition and weight loss in obesity

Targeting gut microbiota with synbiotics (probiotic supplements containing prebiotic components) is emerging as a promising intervention in the comprehensive nutritional approach to reducing obesity. Weight loss resulting from low-carbohydrate high-protein diets can be significant but has also been linked to potentially negative health effects due to increased bacterial fermentation of undigested protein within the colon and subsequent changes in gut microbiota composition. Correcting obesity-induced disruption of gut microbiota with synbiotics can be more effective than supplementation with probiotics alone because prebiotic components of synbiotics support the growth and survival of positive bacteria therein. The purpose of this placebo-controlled intervention clinical trial was to evaluate the effects of a synbiotic supplement on the composition, richness and diversity of gut microbiota and associations of microbial species with body composition parameters and biomarkers of obesity in human subjects participating in a weight loss program. The probiotic component of the synbiotic used in the study contained Lactobacillus acidophilus, Bifidobacterium lactis, Bifidobacterium longum, and Bifidobacterium bifidum and the prebiotic component was a galactooligosaccharide mixture. The results showed no statistically significant differences in body composition (body mass, BMI, body fat mass, body fat percentage, body lean mass, and bone mineral content) between the placebo and synbiotic groups at the end of the clinical trial (3-month intervention, 20 human subjects participating in weight loss intervention based on a low-carbohydrate, high-protein, reduced energy diet). Synbiotic supplementation increased the abundance of gut bacteria associated with positive health effects, especially Bifidobacterium and Lactobacillus, and it also appeared to increase the gut microbiota richness. A decreasing trend in the gut microbiota diversity in the placebo and synbiotic groups was observed at the end of trial, which may imply the effect of the high-protein low-carbohydrate diet used in the weight loss program. Regression analysis performed to correlate abundance of species following supplementation with body composition parameters and biomarkers of obesity found an association between a decrease over time in blood glucose and an increase in Lactobacillus abundance, particularly in the synbiotic group. However, the decrease over time in body mass, BMI, waist circumstance, and body fat mass was associated with a decrease in Bifidobacterium abundance. The results obtained support the conclusion that synbiotic supplement used in this clinical trial modulates human gut microbiota by increasing abundance of potentially beneficial microbial species

'Real men don't diet': an analysis of contemporary newspaper representations of men, food and health

Little research to date has focused on the meanings men attach to food and the relationship between diet and health. This is an important topic in light of the current ‘crisis’ in men's health and the role of lifestyle factors such as diet in illness prevention. Since the mass media is a powerful source of information about health matters generally, media representations bear critical examination. The present paper reports on an in-depth qualitative analysis of contemporary UK newspaper articles on the topic of men and diet (N=44). The findings indicate a persistent adherence to hegemonic masculinities predicated on health-defeating diets, special occasion cooking of hearty meals, and a general distancing from the feminised realm of dieting. At the same time, men are constructed as naïve and vulnerable when it comes to diet and health, while women are viewed as experts. The implications for health promotion with men are discussed

Mice lacking sialyltransferase ST3Gal-II develop late-onset obesity and insulin resistance

Sialyltransferases are a family of 20 gene products in mice and humans that transfer sialic acid from its activated precursor, CMP-sialic acid, to the terminus of glycoprotein and glycolipid acceptors. ST3Gal-II (coded by the St3gal2 gene) transfers sialic acid preferentially to the three positions of galactose on the Galβ1-3GalNAc terminus of gangliosides GM1 and GD1b to synthesize GD1a and GT1b, respectively. Mice with a targeted disruption of St3gal2 unexpectedly displayed lateonset obesity and insulin resistance. At 3 months of age, St3gal2-null mice were the same weight as their wild type (WT) counterparts, but by 13 months on standard chow they were visibly obese, 22% heavier and with 37% greater fat/lean ratio than WT mice. St3gal2-null mice became hyperglycemic and displayed impaired glucose tolerance by 9 months of age. They had sharply reduced insulin responsiveness despite equivalent pancreatic islet morphology. Analyses of insulin receptor (IR) tyrosine kinase substrate IRS-1 and downstream target Akt revealed decreased insulininduced phosphorylation in adipose tissue but not liver or skeletal muscle of St3gal2-null mice. Thin-layer chromatography and mass spectrometry revealed altered ganglioside profiles in the adipose tissue of St3gal2-null mice compared to WT littermates. Metabolically, St3gal2-null mice display a reduced respiratory exchange ratio compared to WT mice, indicating a preference for lipid oxidation as an energy source. Despite their altered metabolism, St3gal2-null mice were hyperactive. We conclude that altered ganglioside expression in adipose tissue results in diminished IR sensitivity and late-onset obesity.Fil: Lopez, Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigación Médica Mercedes y Martín Ferreyra. Universidad Nacional de Córdoba. Instituto de Investigación Médica Mercedes y Martín Ferreyra; Argentina. Johns Hopkins University School of Medicine; Estados UnidosFil: Aja, Susan. Johns Hopkins University School of Medicine; Estados UnidosFil: Aoki, Kazuhiro. University of Georgia; GreciaFil: Seldin, Marcus M.. Johns Hopkins University School of Medicine; Estados UnidosFil: Lei, Xia. Johns Hopkins University School of Medicine; Estados UnidosFil: Ronnett, Gabriele V. Johns Hopkins University School of Medicine; Estados UnidosFil: Wong, G. William. Johns Hopkins University School of Medicine; Estados UnidosFil: Schnaar, Ronald L.. Johns Hopkins University School of Medicine; Estados Unido

Joint and individual analysis of breast cancer histologic images and genomic covariates

A key challenge in modern data analysis is understanding connections between complex and differing modalities of data. For example, two of the main approaches to the study of breast cancer are histopathology (analyzing visual characteristics of tumors) and genetics. While histopathology is the gold standard for diagnostics and there have been many recent breakthroughs in genetics, there is little overlap between these two fields. We aim to bridge this gap by developing methods based on Angle-based Joint and Individual Variation Explained (AJIVE) to directly explore similarities and differences between these two modalities. Our approach exploits Convolutional Neural Networks (CNNs) as a powerful, automatic method for image feature extraction to address some of the challenges presented by statistical analysis of histopathology image data. CNNs raise issues of interpretability that we address by developing novel methods to explore visual modes of variation captured by statistical algorithms (e.g. PCA or AJIVE) applied to CNN features. Our results provide many interpretable connections and contrasts between histopathology and genetics

On Body Mass Index Analysis from Human Visual Appearance

In the past few decades, overweight and obesity are spreading widely like an epidemic. Generally, a person is considered overweight by body mass index (BMI). In addition to a body fat measurement, BMI is also a risk factor for many diseases, such as cardiovascular diseases, cancers and diabetes, etc. Therefore, BMI is important for personal health monitoring and medical research. Currently, BMI is measured in person with special devices. It is an urgent demand to explore conveniently preventive tools. This work investigates the feasibility of analyzing BMI from human visual appearances, including 2-dimensional (2D)/3-dimensional (3D) body and face data. Motivated by health science studies which have shown that anthropometric measures, such as waist-hip ratio, waist circumference, etc., are indicators for obesity, we analyze body weight from frontal view human body images. A framework is developed for body weight analysis from body images, along with the computation methods of five anthropometric features for body weight characterization. Then, we study BMI estimation from the 3D data by measuring the correlation between the estimated body volume and BMIs, and develop an efficient BMI computation method which consists of body weight and height estimation from normally dressed people in 3D space. We also intensively study BMI estimation from frontal view face images via two key aspects: facial representation extracting and BMI estimator learning. First, we investigate the visual BMI estimation problem from the aspect of the characteristics and performance of different facial representation extracting methods by three designed experiments. Then we study visual BMI estimation from facial images by a two-stage learning framework. BMI related facial features are learned in the first stage. To address the ambiguity of BMI labels, a label distribution based BMI estimator is proposed for the second stage. The experimental results show that this framework improves the performance step by step. Finally, to address the challenges caused by BMI data and labels, we integrate feature learning and estimator learning in one convolutional neural network (CNN). A label assignment matching scheme is proposed which successfully achieves an improvement in BMI estimation from face images