and Frédérique Scamps. Axotomy-induced expression of calciumactivated chloride current in subpopulations of mouse dorsal root ganglion neurons. J Neurophysiol 90: 3764 -3773, 2003. First published August 27, 2003 10.1152/jn.00449.2003. Whole cell patchclamp recordings of calcium-activated chloride current [I Cl(Ca) ] were made from adult sensory neurons of naive and axotomized mouse L 4 -L 6 lumbar dorsal root ganglia after 1 day of culture in vitro. A basal I Cl(Ca) was specifically expressed in a subset of naive mediumdiameter neurons (30 -40 m). Prior nerve injury, induced by sciatic nerve transection 5 days before experiments, increased both I Cl(Ca) amplitude and its expression in medium-diameter neurons. Moreover, nerve injury also induced I Cl(Ca) expression in a new subpopulation of neurons, the large-diameter neurons (40 -50 m). Small-diameter neurons (inferior to 30 m) never expressed I Cl(Ca) . Regulated I Cl(Ca) expression was strongly correlated with injury-induced regenerative growth of sensory neurons in vitro and nerve regeneration in vivo. Cell culture on a substrate not permissive for growth, D,L-polyornithine, prevented both elongation growth and I Cl(Ca) expression in axotomized neurons. Regenerative growth and the induction of I Cl(Ca) expression take place 2 days after injury, peak after 5 days of conditioning in vivo, slowly declining thereafter to control values. The selective expression of I Cl(Ca) within medium-and large-diameter neurons conditioned for rapid, efficient growth suggests that these channels play a specific role in postinjury behavior of sensory neuron subpopulations such as neuropathic pain and/or axonal regeneration.
The success rate of ETVs in adults is comparable, if not better, than in children. In addition to the well-defined role of ETV in the treatment of hydrocephalus caused by tumors and aqueduct stenosis, ETV may also have a role in the management of CM-I, LOVA, persistent shunt infection, and IVH resistant to other CSF diversion procedures.
We propose a methodology to aid clinicians in performing lumbar spinal stenosis detection through semantic segmentation and delineation of magnetic resonance imaging (MRI) scans of the lumbar spine using deep learning. Our dataset contains MRI studies of 515 patients with symptomatic back pains. Each study is annotated by expert radiologists with notes regarding the observed characteristics and condition of the lumbar spine. We have developed a ground truth dataset, containing image labels of four important regions in the lumbar spine, to be used as the training and test images to develop classification models for segmentation. We developed two novel metrics, namely confidence, and consistency, to assess the quality of the ground truth dataset through a derivation of the Jaccard Index. We experimented with semantic segmentation of our dataset using SegNet. Our evaluation of the segmentation and the delineation results show that our proposed methodology produces a very good performance as measured by several contourbased and region-based metrics. In addition, using the Cohen's kappa and frequency-weighted confidence metrics, we can show that 1) the model's performance is within the range of the worst and the best manual labeling results and 2) the ground-truth dataset has an excellent inter-rater agreement score. We also presented two representative delineation results of the worst and best segmentation based on their BF-score to show visually how accurate and suitable the results are for computer-aided-diagnosis purposes.
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