Short-term exposure to exenatide can restore the insulin secretory pattern in response to acute rises in glucose concentrations in DM2 patients who, in the absence of exenatide, do not display a first phase of insulin secretion. Loss of first-phase insulin secretion in DM2 patients may be restored by treatment with exenatide.

The incretin mimetic exenatide improved glycemic control and reduced body weight in patients with type 2 diabetes inadequately controlled with metformin+/-a sulfonylurea. We assessed postprandial beta-cell function by mathematical modeling, independent of confounding effects from differing ambient glucose levels among treatments. Subjects were 63% males, 55+/-10 years, BMI 33+/-6 kg/m2, HbA1C 8.1+/-1.1% (+/- SD) randomized to 5 microg exenatide or placebo twice daily for 4 weeks. Subsequently, one arm remained at 5 microg twice daily, one arm escalated to 10 microg twice daily, and one treatment arm remained on placebo for 26 weeks. Subjects continued metformin+/-a sulfonylurea. A subset with meal tests at baseline and week 30 were analyzed (n=73). Outcome measures were the model-based beta-cell function parameters dose-response relating insulin secretion to glucose concentration, rate sensitivity, and potentiation. Exenatide reduced postprandial glucose excursions. Modeling predicted an upward shift of the beta-cell dose-response. Model-predicted insulin secretion rate at a reference glucose concentration increased 72% (10 microg), increased 40% (5 microg), or decreased 21% (placebo) at week 30 [ p=0.015 (10 microg); p=0.045 (5 microg); vs. placebo]. At week 30, the 2-hour post-meal to basal potentiation factor ratio was increased to 1.53+/-0.10 (10 microg; p=0.0142 vs. placebo) or 1.40+/-0.08 (5 microg; p=0.0402 vs. placebo) compared with 1.15+/-0.06 (placebo). Exenatide caused an upward shift of the beta-cell dose-response and enhanced potentiation of insulin secretion. This model suggests exenatide improved beta-cell function in patients with type 2 diabetes treated with metformin+/-a sulfonylurea.

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Our results suggest that macrophages, specific cytokines (bFGF, PDGF, and VEGF), and angiogenesis within the neointima and adventitia are likely to contribute to the pathogenesis of VNH in PTFE dialysis grafts. Interventions aimed at these specific mediators and processes may be successful in reducing the very significant human and economic costs of vascular access dysfunction.

The processing of brain diffusion tensor imaging (DTI) data for large cohort studies requires fully automatic pipelines to perform quality control (QC) and artifact/outlier removal procedures on the raw DTI data prior to calculation of diffusion parameters. In this study, three automatic DTI processing pipelines, each complying with the general ENIGMA framework, were designed by uniquely combining multiple image processing software tools. Different QC procedures based on the RESTORE algorithm, the DTIPrep protocol, and a combination of both methods were compared using simulated ground truth and artifact containing DTI datasets modeling eddy current induced distortions, various levels of motion artifacts, and thermal noise. Variability was also examined in 20 DTI datasets acquired in subjects with vascular cognitive impairment (VCI) from the multi-site Ontario Neurodegenerative Disease Research Initiative (ONDRI). The mean fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were calculated in global brain grey matter (GM) and white matter (WM) regions. For the simulated DTI datasets, the measure used to evaluate the performance of the pipelines was the normalized difference between the mean DTI metrics measured in GM and WM regions and the corresponding ground truth DTI value. The performance of the proposed pipelines was very similar, particularly in FA measurements. However, the pipeline based on the RESTORE algorithm was the most accurate when analyzing the artifact containing DTI datasets. The pipeline that combined the DTIPrep protocol and the RESTORE algorithm produced the lowest standard deviation in FA measurements in normal appearing WM across subjects. We concluded that this pipeline was the most robust and is preferred for automated analysis of multisite brain DTI data.

Our findings suggest that the APOE epsilon4 allele may be included among the variables that predict high cognitive function in cognitively intact, very old adults. Although the presence or absence of the epsilon4 allele is known to be related to the risk of dementia, it also appears to be related to maintaining high levels of cognitive function in old age.

Large scale research studies combining magnetic resonance imaging data generated at multiple sites on multiple vendor platforms are becoming more commonplace. The Ontario Neurodegenerative Disease Research Initiative (ONDRI -http://ondri.ca/), a project funded by the Ontario Brain Institute (OBI), is a recently established province-wide natural history study, which has recruited more than 500 participants from neurodegenerative disease groups including amyotrophic lateral sclerosis, fronto-temporal dementia, Parkinson's disease, Alzheimer's disease, mild cognitive impairment, and cerebrovascular disease (previously referred to as the vascular cognitive impairment cohort). Because of its multi-site nature, all captured data must be standardized and meet minimum quality standards to reduce variability. The goal of the ONDRI imaging platform is to maximize data quality by implementing vendor-specific harmonized MR imaging protocols (consistent with the Canadian Dementia Imaging Protocol -http://www.cdip-pcid.ca/), monitoring protocol adherence, qualitatively assessing image quality, measuring signal-to-noise and contrast-to-noise, monitoring system stability, and applying corrections based on the analysis of images from two different phantoms regularly acquired at each site. To maximize image quality, this work describes the use of various automatic pipelines and manual assessment steps, integrated within an established informatics and databasing platform, the Stroke Patient Recovery Research Database (SPReD) built on the Extensible Neuroimaging Archive Toolkit (XNAT), and contained within the Brain-CODE (Centre for Ontario Data Exploration) framework. The purpose of the current paper is to describe the steps undertaken by ONDRI to achieve this high standard of data integrity. Data have been successfully collected for the past 4 years with the pipelines and assessments identifying deviations, allowing for timely interventions and assessment of image quality.

Estimation of prostate location and volume is essential in determining a dose plan for ultrasound-guided brachytherapy, a common prostate cancer treatment. However, manual segmentation is difficult, time consuming and prone to variability. In this paper, we present a semi-automatic discrete dynamic contour (DDC) model based image segmentation algorithm, which effectively combines a multi-resolution model refinement procedure together with the domain knowledge of the image class. The segmentation begins on a low-resolution image by defining a closed DDC model by the user. This contour model is then deformed progressively towards higher resolution images. We use a combination of a domain knowledge based fuzzy inference system (FIS) and a set of adaptive region based operators to enhance the edges of interest and to govern the model refinement using a DDC model. The automatic vertex relocation process, embedded into the algorithm, relocates deviated contour points back onto the actual prostate boundary, eliminating the need of user interaction after initialization. The accuracy of the prostate boundary produced by the proposed algorithm was evaluated by comparing it with a manually outlined contour by an expert observer. We used this algorithm to segment the prostate boundary in 114 2D transrectal ultrasound (TRUS) images of six patients scheduled for brachytherapy. The mean distance between the contours produced by the proposed algorithm and the manual outlines was 2.70 +/- 0.51 pixels (0.54 +/- 0.10 mm). We also showed that the algorithm is insensitive to variations of the initial model and parameter values, thus increasing the accuracy and reproducibility of the resulting boundaries in the presence of noise and artefacts.