The Effect of a Western Diet on Hepatic Autophagy in Age Accelerated SAMP8 Mice

Non-alcoholic steatohepatitis (NASH) is characterized as a dysregulation of hepatic lipid metabolism and a chronic inflammatory state. It is hypothesized the link between lipid dysregulation and inflammation may be due in part to defective hepatic autophagy and reduced mitochondrial capacity to oxidize fatty acids. It remains to be determined; however, the effects of a Western diet on hepatic autophagy and mitochondrial function during aging. PURPOSE: The purpose of this study was to determine the effect of a high-fat high fructose diet (HFF) on markers of hepatic autophagy and mitochondrial function in an age accelerated mouse model. METHODS: Twenty week old, male and female, SAMP8 mice (n=49) were randomly assigned, matching for gender, to either a standard chow (SC) or HFF (45% fat, 24% fructose) diet for 32 weeks. Liver tissue was analyzed for mRNA expression of autophagic (BNIP3, Beclin 1, p62, and Atg7) and mitochondrial (PGC1α and COXIV) genes. Differences between gender and dietary groups were identified by a 2 x 2 ANOVA and statistical significance was set at p\u3c0.05. RESULTS: Following 32 weeks of feeding, male mice fed the HFF diet were significantly heavier than male mice in the SC group (31.6 g vs 26.5 g; p=0.001); however, no difference was observed between diet groups for female mice. The HFF diet resulted in higher autophagic activity as observed by Beclin 1 (+36%; p=0.001) and BNIP3 (+40%; P=0.003) expression. Despite the higher autophagic activity, p62 was higher (+31%; p\u3c0.001) in the HFF compared to the SC group, suggesting impaired autophagic flux. In addition, mitochondrial COXIV expression was elevated (+43%; P\u3c0.001) in the HFF group compared to the SC group suggesting increased β-oxidation. Overall, the expression of all autophagic and mitochondrial markers was higher in male compared to female mice; however, both sexes responded similarly to the HFF diet. CONCLUSION: Despite the higher expression of autophagic and mitochondrial genes, elevated expression of p62 suggests an impaired autophagic flux in age accelerated mice following a Western diet

Intensity-based image registration using multiple distributed agents

Image registration is the process of geometrically aligning images taken from different sensors, viewpoints or instances in time. It plays a key role in the detection of defects or anomalies for automated visual inspection. A multiagent distributed blackboard system has been developed for intensity-based image registration. The images are divided into segments and allocated to agents on separate processors, allowing parallel computation of a similarity metric that measures the degree of likeness between reference and sensed images after the application of a transform. The need for a dedicated control module is removed by coordination of agents via the blackboard. Tests show that additional agents increase speed, provided the communication capacity of the blackboard is not saturated. The success of the approach in achieving registration, despite significant misalignment of the original images, is demonstrated in the detection of manufacturing defects on screen-printed plastic bottles and printed circuit boards

Finding Your Niche: Odbadrakh Tuguldur

Odbadrakh Tuguldur, who goes by “Togo”, is an upperclassman pursuing a Bachelors of Science in chemistry. Born in Mongolia, and hailing from Raleigh, North Carolina, Togo came to WVU when his parents moved to West Virginia. Initially a biology major with plans for medical school, Togo took Dr. Xiaodong Shi’s organic chemistry course and promptly switched to a chemistry track

Omega-3-Fatty Acids Hold Therapeutic Potential for the Prevention and Treatment of Diabetic Neuropathy

Diabetic neuropathy is a debilitating complication of diabetes, affecting over 50% of diabetic patients. Overweight humans display manifestations of diabetic neuropathy before developing overt diabetes and mice fed a high fat diet exhibit signs of neuropathy including mechanical hindpaw hypersensitivity and neuronal inflammation, suggesting fat diet-induced inflammation may play a role in the development of neuropathy. Omega-3 (n-3) fatty acids have anti-inflammatory properties and may hold therapeutic potential as a preventative treatment for prediabetic and diabetic patients at risk for neuropathy. PURPOSE: Investigate the impact of diet composition on signs of neuropathy. We hypothesized that a diet rich in n-3 fatty acids would attenuate hindpaw hypersensitivity during prolonged feeding of a high fat diet. METHODS: C57BL/6 mice were randomized into four diet groups (n = 12/group) for 32 weeks: 10% low fat-fish oil (LFFO), 41% high fat-fish oil (HFFO), 10% low fat-lard (LFL), or 41% high fat-lard (HFL). Neuropathy was characterized at baseline and every other week thereafter using the von Frey behavioral test for hindpaw mechanical sensitivity. A glucose tolerance test was performed at end study, and total area under the curve (AUC) was calculated using the trapezoidal method. RESULTS: At end study, body weight was greater in HFL compared to all other groups. Body weight was also greater in HFFO compared to LFFO. Fasting glucose and glucose AUC were higher in HFL compared to LFFO and HFFO. Following the same pattern as body weight, fasting glucose was higher in HFFO compared to LFFO. Although percent paw withdrawal was greater in HFL compared to HFFO and LFFO, there were no significant differences for LF vs. HF for fish oil or lard. CONCLUSION: A HFL diet induced signs of neuropathy including hindpaw hypersensitivity, whereas a fish oil diet was protective against hindpaw hypersensitivity. Moreover, omega-3-fatty acids may hold therapeutic potential for neuropathy prevention in nondiabetic and diabetic patients

Automatic Color Segmentation of Images with Application to Detection of Variegated Coloring in Skin Tumors

A description is given of a computer vision system, developed to serve as the front-end of a medical expert system, that automates visual feature identification for skin tumor evaluation. The general approach is to create different software modules that detect the presence or absence of critical features. Image analysis with artificial intelligence (AI) techniques, such as the use of heuristics incorporated into image processing algorithms, is the primary approach. On a broad scale, this research addressed the problem of segmentation of a digital image based on color information. The algorithm that was developed to segment the image strictly on the basis of color information was shown to be a useful aid in the identification of tumor border, ulcer, and other features of interest. As a specific application example, the method was applied to 200 digitized skin tumor images to identify the feature called variegated coloring. Extensive background information is provided, and the development of the algorithm is described

Plantar fascia segmentation and thickness estimation in ultrasound images

Ultrasound (US) imaging offers significant potential in diagnosis of plantar fascia (PF) injury and monitoring treatment. In particular US imaging has been shown to be reliable in foot and ankle assessment and offers a real-time effective imaging technique that is able to reliably confirm structural changes, such as thickening, and identify changes in the internal echo structure associated with diseased or damaged tissue. Despite the advantages of US imaging, images are difficult to interpret during medical assessment. This is partly due to the size and position of the PF in relation to the adjacent tissues. It is therefore a requirement to devise a system that allows better and easier interpretation of PF ultrasound images during diagnosis. This study proposes an automatic segmentation approach which for the first time extracts ultrasound data to estimate size across three sections of the PF (rearfoot, midfoot and forefoot). This segmentation method uses artificial neural network module (ANN) in order to classify small overlapping patches as belonging or not-belonging to the region of interest (ROI) of the PF tissue. Features ranking and selection techniques were performed as a post-processing step for features extraction to reduce the dimension and number of the extracted features. The trained ANN classifies the image overlapping patches into PF and non-PF tissue, and then it is used to segment the desired PF region. The PF thickness was calculated using two different methods: distance transformation and area-length calculation algorithms. This new approach is capable of accurately segmenting the PF region, differentiating it from surrounding tissues and estimating its thickness

Applying Artificial Intelligence to the Identification of Variegated Coloring in Skin Tumors

The importance of color information for the automatic diagnosis of skin tumors by computer vision is demonstrated. The utility of the relative color concept is proved by the results in identifying variegated coloring. A feature file paradigm is shown to provide an effective methodology for the independent development of software modules for expert system/computer vision research. An automatic induction tool is used effectively to generate rules for identifying variegated coloring. Variegated coloring can be identified at rates as high as 92% when using the automatic induction technique in conjunction with the color segmentation metho

Methods for the analysis of ordinal response data in medical image quality assessment.

The assessment of image quality in medical imaging often requires observers to rate images for some metric or detectability task. These subjective results are used in optimisation, radiation dose reduction or system comparison studies and may be compared to objective measures from a computer vision algorithm performing the same task. One popular scoring approach is to use a Likert scale, then assign consecutive numbers to the categories. The mean of these response values is then taken and used for comparison with the objective or second subjective response. Agreement is often assessed using correlation coefficients. We highlight a number of weaknesses in this common approach, including inappropriate analyses of ordinal data, and the inability to properly account for correlations caused by repeated images or observers. We suggest alternative data collection and analysis techniques such as amendments to the scale and multilevel proportional odds models. We detail the suitability of each approach depending upon the data structure and demonstrate each method using a medical imaging example. Whilst others have raised some of these issues, we evaluated the entire study from data collection to analysis, suggested sources for software and further reading, and provided a checklist plus flowchart, for use with any ordinal data. We hope that raised awareness of the limitations of the current approaches will encourage greater method consideration and the utilisation of a more appropriate analysis. More accurate comparisons between measures in medical imaging will lead to a more robust contribution to the imaging literature and ultimately improved patient care