A Brain Controlled Wheelchair to Navigate in Familiar Environments

Ph.DDOCTOR OF PHILOSOPH

Prostacyclin production in rat aortic smooth muscle cells: role of protein kinase C, phospholipase D and cyclooxygenase-2 expression

Objective: The present study was designed to investigate the role of protein kinase C (PKC) and phospholipase D (PLD) in angiotensin II (AngII)- and phorbol ester (PMA)-induced cyclooxygenase-2 (COX-2) expression and prostacyclin (PGI2) production in rat aortic smooth muscle cells (VSMC). Methods: Prostacyclin production in cultured VSMC was determined by radioimmunoassay. PKC activity was examined by measuring the transfer of 32P from (γ-32P)ATP to histone III-S. COX-2 expression was determined by Western blotting. To measure PLD activity, thin layer chromatography was used. Results: AngII (50 nM) and PMA (100 nM) promoted the translocation of PKC activity from the cytosol to the membranes within 30 min, followed by a strong increase in PLD activity as well as COX-2 expression and PGI2 production. After 48 h exposure to PMA, PKC was downregulated resulting in a complete suppression of its activity. PKC-downregulation and the PKC inhibitor CGP41251 abolished PMA- and AngII-induced PLD activation, suppressed the stimulatory effect of PMA on COX-2 expression and PGI2 production and strongly inhibited that of AngII. Furthermore, AngII- and PMA-induced PGI2 production depended on protein synthesis and COX-2 but not COX-1 activity. Inhibition of PLD-mediated phosphatidic acid (PA) formation by 1% 1-butanol abolished AngII-induced COX-2 expression and PGI2 secretion, while dioctanoyl PA increased COX-2 expression and PGI2 production in a time- and concentration-dependent manner. Conclusion: Our results indicate that in VSMC, AngII promotes PGI2 production to a large extent through a rise in COX-2 expression which is mediated by PA generated from increased PKC-dependent PLD activit

Brain Morphometry Estimation: From Hours to Seconds Using Deep Learning.

Motivation: Brain morphometry from magnetic resonance imaging (MRI) is a promising neuroimaging biomarker for the non-invasive diagnosis and monitoring of neurodegenerative and neurological disorders. Current tools for brain morphometry often come with a high computational burden, making them hard to use in clinical routine, where time is often an issue. We propose a deep learning-based approach to predict the volumes of anatomically delineated subcortical regions of interest (ROI), and mean thicknesses and curvatures of cortical parcellations directly from T1-weighted MRI. Advantages are the timely availability of results while maintaining a clinically relevant accuracy. Materials and Methods: An anonymized dataset of 574 subjects (443 healthy controls and 131 patients with epilepsy) was used for the supervised training of a convolutional neural network (CNN). A silver-standard ground truth was generated with FreeSurfer 6.0. Results: The CNN predicts a total of 165 morphometric measures directly from raw MR images. Analysis of the results using intraclass correlation coefficients showed, in general, good correlation with FreeSurfer generated ground truth data, with some of the regions nearly reaching human inter-rater performance (ICC > 0.75). Cortical thicknesses predicted by the CNN showed cross-sectional annual age-related gray matter atrophy rates both globally (thickness change of -0.004 mm/year) and regionally in agreement with the literature. A statistical test to dichotomize patients with epilepsy from healthy controls revealed similar effect sizes for structures affecting all subtypes as reported in a large-scale epilepsy study. Conclusions: We demonstrate the general feasibility of using deep learning to estimate human brain morphometry directly from T1-weighted MRI within seconds. A comparison of the results to other publications shows accuracies of comparable magnitudes for the subcortical volumes and cortical thicknesses

Prostaglandin E2 activates Stat3 in neonatal rat ventricular cardiomyocytes: A role in cardiac hypertrophy

Objective: The purpose of this study was to investigate whether prostaglandin E2 (PGE2) induces Signal transducer and activator of transcription 3 (Stat3) activation in neonatal rat ventricular cardiomyocytes and if so to determine the possible role of this activation in PGE2-induced hypertrophic responses. Methods: Stat3 activation and its nuclear phosphorylation were determined by electrophoretic mobility shift assay (EMSA) and by Western blots, respectively. Protein synthesis was assessed by [3H]-leucine incorporation into total protein and cell surface was quantified by microscopic analysis. Results: We found that PGE2 induces a concentration- (1-100nM) and time-dependent increase in Stat3 activation, reaching maximal values after 90min of stimulation. Experiments with agonists and antagonists of the PGE2 receptor subtypes EP1-EP4 indicate that PGE2 activates Stat3 mainly through the EP4 receptor. We further observed that the extracellular signal-regulated kinase 1/2 (ERK1/2) inhibitor U0126 abolishes PGE2-induced Stat3 activation whereas the p38 MAP kinase blocker SB203580 has no effect. Nuclear Stat3 phosphorylation induced by PGE2 is also suppressed by the translation and transcription inhibitors, cycloheximide and actinomycin D, respectively. Transfecting ventricular cardiomyocytes with a small interfering RNA (siRNA) targeting rat Stat3, we obtained an approximately 70% reduction in Stat3 expression, 24 and 48h after electroporation. In these Stat3-silenced cells, the PGE2-induced increase in protein synthesis and cell surface is strongly inhibited. Conclusion: In ventricular cardiomyocytes, PGE2 induces the activation of Stat3 which plays an essential role in PGE2-induced increase in cell size and protein synthesis. The activation of Stat3 occurs mainly through EP4 and involves ERK1/2 as well as newly synthesized protein(s

A Quantitative Imaging Biomarker Supporting Radiological Assessment of Hippocampal Sclerosis Derived From Deep Learning-Based Segmentation of T1w-MRI.

Purpose Hippocampal volumetry is an important biomarker to quantify atrophy in patients with mesial temporal lobe epilepsy. We investigate the sensitivity of automated segmentation methods to support radiological assessments of hippocampal sclerosis (HS). Results from FreeSurfer and FSL-FIRST are contrasted to a deep learning (DL)-based segmentation method. Materials and Methods We used T1-weighted MRI scans from 105 patients with epilepsy and 354 healthy controls. FreeSurfer, FSL, and a DL-based method were applied for brain anatomy segmentation. We calculated effect sizes (Cohen's d) between left/right HS and healthy controls based on the asymmetry of hippocampal volumes. Additionally, we derived 14 shape features from the segmentations and determined the most discriminating feature to identify patients with hippocampal sclerosis by a support vector machine (SVM). Results Deep learning-based segmentation of the hippocampus was the most sensitive to detecting HS. The effect sizes of the volume asymmetries were larger with the DL-based segmentations (HS left d= -4.2, right = 4.2) than with FreeSurfer (left= -3.1, right = 3.7) and FSL (left= -2.3, right = 2.5). For the classification based on the shape features, the surface-to-volume ratio was identified as the most important feature. Its absolute asymmetry yielded a higher area under the curve (AUC) for the deep learning-based segmentation (AUC = 0.87) than for FreeSurfer (0.85) and FSL (0.78) to dichotomize HS from other epilepsy cases. The robustness estimated from repeated scans was statistically significantly higher with DL than all other methods. Conclusion Our findings suggest that deep learning-based segmentation methods yield a higher sensitivity to quantify hippocampal sclerosis than atlas-based methods and derived shape features are more robust. We propose an increased asymmetry in the surface-to-volume ratio of the hippocampus as an easy-to-interpret quantitative imaging biomarker for HS