The cumulative effects of hepatic injury due to hepatitis B virus (HBV) infections and aflatoxin-B1 (AFB1) exposure are the major risk factors of HCC. Understanding early metabolic changes involving these risk factors in an animal model closely resembling human hepatocellular carcinoma (HCC) is critical for biomarker discovery and disease therapeutics. We have used the hepatitis B surface antigen (HBsAg) transgenic mouse model that mimics HBV carriers with and without AFB1 treatment. We investigated early metabolic changes from preneoplastic state to HCC by non-invasive longitudinal imaging in three HCC groups of mice: HBsAg + AFB1(Gp-I), AFB1 alone (Gp-II), HBsAg alone (Gp-III) and a control group (wild-type untreated; Gp-IV). For the first time, we have identified acylcarnitine signals in vivo in the liver prior to the histological manifestation of the tumors in all three groups. Acylcarnitine concentration increased with increase in tumor growth in all HCC mouse models, indicating elevated metabolic activity and increased cell turnover. This was confirmed in a pilot study using human serum from HCC patients, which revealed a higher concentration of acylcarnitine compared with normal subjects. Translational clinical studies can be designed to detect acylcarnitine in patients with high risk factors for HCC.
majority of body fat, which is utilized for energy storage, whereas BAT is a minor portion that is involved in thermogenesis, due to the presence of uncoupling protein 1 (UCP1) in the mitochondria. In addition to the differences in energy storage and expenditure, BAT is a highly heterogeneous, densely vascularized tissue with abundant oxygen, blood supply, and iron-rich mitochondria (6, 7). WAT is composed of unilocular lipid droplets, whereas BAT is composed of multilocular lipid droplets scattered throughout the cytoplasm and surrounded by mitochondria.Positron emission tomography (PET) has been the gold standard for imaging BAT (8, 9) because of a selective image contrast of activated BAT by the increased uptake of 18 F-deoxyglucose. However, PET is not suitable for longitudinal studies because it requires injection of exogenous radioactive tracers. Magnetic resonance (MR)-based methods are more promising for real-time and long-term observation of fat accumulation and consumption (10). Traditional MR-based approaches exploit the differences in the water content in WAT and BAT using chemical shift imaging or the Dixon technique (11-13). Imaging of water and fat permits quantitative assessment of the fat fraction. Additionally, the differences in iron content, perfusion, and vasculature have been exploited by T 2 and T 2 * relaxation techniques (14, 15). However, although WAT and BAT are manually separable by color and texture, they cannot be easily distinguished by MR because of the similarity in their magnetic and chemical characteristics (14,15).Applications of nontraditional MR methodologies for selectively detecting BAT have been attempted. Branca and Warren (16, 17) have studied focusing on intermolecular multiple-quantum coherences through dipolar Abstract There are two types of fat tissues, white adipose tissue (WAT) and brown adipose tissue (BAT), which essentially perform opposite functions in whole body energy metabolism. There is a large interest in identifying novel biophysical properties of WAT and BAT by a quantitative and easy-to-run technique. In this work, we used high-resolution pulsed field gradient diffusion NMR spectroscopy to study the apparent diffusion coefficient (ADC) of fat molecules in rat BAT and WAT samples. The ADC of fat in BAT and WAT from rats fed with a chow diet was compared with that of rats fed with a high-fat diet to monitor how the diffusion properties change due to obesity-associated parameters such as lipid droplet size, fatty acid chain length, and saturation. Feeding a high-fat diet resulted in increased saturation, increased chain lengths, and reduced ADC of fat in WAT. The ADC of fat was lower in BAT relative to WAT in rats fed both chow and high-fat diets. Diffusion of fat was restricted in BAT due to the presence of small multilocular lipid droplets. Our findings indicate that in vivo diffusion might be a potential way for better delineation of BAT and WAT in both lean and obese states.-Verma, S. K., K. Nagashima, J. Yaligar, N. Michael, S. S. Lee, T. Xianfeng, V...
Background & AimsObesity is a leading healthcare issue contributing to metabolic diseases. There is a great interest in non-invasive approaches for quantitating abdominal fat in obese animals and humans. In this work, we propose an automated method to distinguish and quantify subcutaneous and visceral adipose tissues (SAT and VAT) in rodents during obesity and weight loss interventions. We have also investigated the influence of different magnetic resonance sequences and sources of variability in quantification of fat depots.Materials and MethodsHigh-fat diet fed rodents were utilized for investigating the changes during obesity, exercise, and calorie restriction interventions (N = 7/cohort). Imaging was performed on a 7T Bruker ClinScan scanner using fast spin echo (FSE) and Dixon imaging methods to estimate the fat depots. Finally, we quantified the SAT and VAT volumes between the L1–L5 lumbar vertebrae using the proposed automatic hybrid geodesic region-based curve evolution algorithm.ResultsSignificant changes in SAT and VAT volumes (p<0.01) were observed between the pre- and post-intervention measurements. The SAT and VAT were 44.22±9%, 21.06±1.35% for control, −17.33±3.07%, −15.09±1.11% for exercise, and 18.56±2.05%, −3.9±0.96% for calorie restriction cohorts, respectively. The fat quantification correlation between FSE (with and without water suppression) sequences and Dixon for SAT and VAT were 0.9709, 0.9803 and 0.9955, 0.9840 respectively. The algorithm significantly reduced the computation time from 100 sec/slice to 25 sec/slice. The pre-processing, data-derived contour placement and avoidance of strong background–image boundary improved the convergence accuracy of the proposed algorithm.ConclusionsWe developed a fully automatic segmentation algorithm to quantitate SAT and VAT from abdominal images of rodents, which can support large cohort studies. We additionally identified the influence of non-algorithmic variables including cradle disturbance, animal positioning, and MR sequence on the fat quantification. There were no large variations between FSE and Dixon-based estimation of SAT and VAT.
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