Got Sugar? Pharmacist Intervention to Improve A1c

AIM: Within 6 months, we aim to decrease by 10% the number of our diabetic patients with an A1c \u3e8 through Clinical Pharmacist referrals.https://jdc.jefferson.edu/patientsafetyposters/1033/thumbnail.jp

Molecular characterization of putative neuropeptide, amine, diffusible gas and small molecule transmitter biosynthetic enzymes in the eyestalk ganglia of the American lobster, Homarus americanus

The American lobster, Homarus americanus, is a model for investigating the neuromodulatory control of physiology and behavior. Prior studies have shown that multiple classes of chemicals serve as locally released/circulating neuromodulators/neurotransmitters in this species. Interestingly, while many neuroactive compounds are known from Homarus, little work has focused on identifying/characterizing the enzymes responsible for their biosynthesis, despite the fact that these enzymes are key components for regulating neuromodulation/neurotransmission. Here, an eyestalk ganglia-specific transcriptome was mined for transcripts encoding enzymes involved in neuropeptide, amine, diffusible gas and small molecule transmitter biosynthesis. Using known Drosophila melanogaster proteins as templates, transcripts encoding putative Homarus homologs of peptide precursor processing (signal peptide peptidase, prohormone processing protease and carboxypeptidase) and immature peptide modifying (glutaminyl cyclase, tyrosylprotein sulfotransferase, protein disulfide isomerase, peptidylglycine-α-hydroxylating monooxygenase and peptidyl-α-hydroxyglycine-α-amidating lyase) enzymes were identified in the eyestalk assembly. Similarly, transcripts encoding full complements of the enzymes responsible for dopamine [tryptophan-phenylalanine hydroxylase (TPH), tyrosine hydroxylase and DOPA decarboxylase (DDC)], octopamine (TPH, tyrosine decarboxylase and tyramine β-hydroxylase), serotonin (TPH or tryptophan hydroxylase and DDC) and histamine (histidine decarboxylase) biosynthesis were identified from the eyestalk ganglia, as were those responsible for the generation of the gases nitric oxide (nitric oxide synthase) and carbon monoxide (heme oxygenase), and the small molecule transmitters acetylcholine (choline acetyltransferase), glutamate (glutaminase) and GABA (glutamic acid decarboxylase). The presence and identity of the transcriptome-derived transcripts were confirmed using RT-PCR. The data presented here provide a foundation for future gene-based studies of neuromodulatory control at the level of neurotransmitter/modulator biosynthesis in Homarus

Volumetric Neuroimage Analysis Extensions for the MIPAV Software Package

We describe a new collection of publicly available software tools for performing quantitative neuroimage analysis. The tools perform semi-automatic brain extraction, tissue classification, Talairach alignment, and atlas-based measurements within a user-friendly graphical environment. They are implemented as plug-ins for MIPAV, a freely available medical image processing software package from the National Institutes of Health. Because the plug-ins and MIPAV are implemented in Java, both can be utilized on nearly any operating system platform. In addition to the software plug-ins, we have also released a digital version of the Talairach atlas that can be used to perform regional volumetric analyses. Several studies are conducted applying the new tools to simulated and real neuroimaging data sets

Patellar segmentation from 3D magnetic resonance images using guided recursive ray-tracing for edge pattern detection

The paper presents an automatic segmentation methodology for the patellar bone, based on 3D gradient recalled echo and gradient recalled echo with fat suppression magnetic resonance images. Constricted search space outlines are incorporated into recursive ray-tracing to segment the outer cortical bone. A statistical analysis based on the dependence of information in adjacent slices is used to limit the search in each image to between an outer and inner search region. A section based recursive ray-tracing mechanism is used to skip inner noise regions and detect the edge boundary. The proposed method achieves higher segmentation accuracy (0.23mm) than the current state-of-the-art methods with the average dice similarity coefficient of 96.0% (SD 1.3%) agreement between the auto-segmentation and ground truth surfaces.</p

Geophysics Field Camp 2003

Four different geophysical methods were used near Chino Valley, Arizona in order to map a suspected andesite intrusive, identified as plug 15, which had originally been found using aeromagnetic data already acquired over the area. Magnetic, transient electromagnetic, seismic, and gravity measurements were collected between 3/1/2003-3/2/2003 and 3/22/2003-3/23/2003. The surveys were located near the center of section 35, township 17 North, Range 2 West, just north of Chino Valley, AZ. The magnetic and TEM surveys provided the best indication of the location and depth of the plug. The north-south spatial extent of the plug was estimated to be approximately 600 meters. The depth to the top of the plug was found from the TEM survey to be approximately 300 meters at the center of the survey. The seismic survey did not reach deep enough to find the andesite anomaly and the gravity survey did not appear to be affected by the plug. Magnetic, TEM, and seismic surveys were also performed at another site located approximately 1.25 km northeast of plug 15. The seismic survey did not reach deep enough and the magnetic survey was too short to provide a depth interpretation. The single TEM sounding measured a very high resistivity (approximately 900 ohm-m) at this site.The Geophysics Field Camp Reports are made available by the Laboratory for Advanced Subsurface Imaging (LASI) and the University of Arizona Libraries. Visit the LASI website for more information http://www.lasi.arizona.edu

Fully automated prostate whole gland and central gland segmentation on MRI using holistically nested networks with short connections

© 2019 Society of Photo-Optical Instrumentation Engineers (SPIE). Accurate and automated prostate whole gland and central gland segmentations on MR images are essential for aiding any prostate cancer diagnosis system. Our work presents a 2-D orthogonal deep learning method to automatically segment the whole prostate and central gland from T2-weighted axial-only MR images. The proposed method can generate high-density 3-D surfaces from low-resolution (z axis) MR images. In the past, most methods have focused on axial images alone, e.g., 2-D based segmentation of the prostate from each 2-D slice. Those methods suffer the problems of over-segmenting or under-segmenting the prostate at apex and base, which adds a major contribution for errors. The proposed method leverages the orthogonal context to effectively reduce the apex and base segmentation ambiguities. It also overcomes jittering or stair-step surface artifacts when constructing a 3-D surface from 2-D segmentation or direct 3-D segmentation approaches, such as 3-D U-Net. The experimental results demonstrate that the proposed method achieves 92.4 % ± 3 % Dice similarity coefficient (DSC) for prostate and DSC of 90.1 % ± 4.6 % for central gland without trimming any ending contours at apex and base. The experiments illustrate the feasibility and robustness of the 2-D-based holistically nested networks with short connections method for MR prostate and central gland segmentation. The proposed method achieves segmentation results on par with the current literature

Automatic magnetic resonance prostate segmentation by deep learning with holistically nested networks

© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE). Accurate automatic segmentation of the prostate in magnetic resonance images (MRI) is a challenging task due to the high variability of prostate anatomic structure. Artifacts such as noise and similar signal intensity of tissues around the prostate boundary inhibit traditional segmentation methods from achieving high accuracy. We investigate both patch-based and holistic (image-to-image) deep-learning methods for segmentation of the prostate. First, we introduce a patch-based convolutional network that aims to refine the prostate contour which provides an initialization. Second, we propose a method for end-to-end prostate segmentation by integrating holistically nested edge detection with fully convolutional networks. Holistically nested networks (HNN) automatically learn a hierarchical representation that can improve prostate boundary detection. Quantitative evaluation is performed on the MRI scans of 250 patients in fivefold cross-validation. The proposed enhanced HNN model achieves a mean ± standard deviation. A Dice similarity coefficient (DSC) of 89.77%±3.29% and a mean Jaccard similarity coefficient (IoU) of 81.59%±5.18% are used to calculate without trimming any end slices. The proposed holistic model significantly (p\u3c0.001) outperforms a patch-based AlexNet model by 9% in DSC and 13% in IoU. Overall, the method achieves state-of-the-art performance as compared with other MRI prostate segmentation methods in the literature