Development of affinity-based delivery of NGF from a chondroitin sulfate biomaterial

Chondroitin sulfate is a major component of the extracellular matrix in both the central and peripheral nervous systems. Chondroitin sulfate is upregulated at injury, thus methods to promote neurite extension through chondroitin sulfate-rich matrices and synthetic scaffolds are needed. We describe the use of both chondroitin sulfate and a novel chondroitin sulfate-binding peptide to control the release of nerve growth factor. Interestingly, the novel chondroitin sulfate-binding peptide enhances the controlled release properties of the chondroitin sulfate gels. While introduction of chondroitin sulfate into a scaffold inhibits primary cortical outgrowth, the combination of chondroitin sulfate, chondroitin sulfate-binding peptide and nerve growth factor promotes primary cortical neurite outgrowth in chondroitin sulfate gels

Morphological alterations to neurons of the amygdala and impaired fear conditioning in a transgenic mouse model of Alzheimer's disease

Patients with Alzheimer's disease (AD) suffer from impaired memory and emotional disturbances, the pathogenesis of which is not entirely clear. In APP/PS1 transgenic mice, a model of AD in which amyloid β (Aβ) accumulates in the brain, we have examined neurons in the lateral nucleus of the amygdala (LA), a brain region crucial to establish cued fear conditioning. We found that although there was no neuronal loss in this region and Aβ plaques only occupy less than 1% of its volume, these mice froze for shorter times after auditory fear conditioning when compared to their non-transgenic littermates. We performed a three-dimensional analysis of projection neurons and of thousands of dendritic spines in the LA. We found changes in dendritic tree morphology and a substantial decrease in the frequency of large spines in plaque-free neurons of APP/PS1 mice. We suggest that these morphological changes in the neurons of the LA may contribute to the impaired auditory fear conditioning seen in this AD model. Copyright © 2009 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.Peer Reviewe

Genomic cloning and characterization of the human homeobox gene SIX9 reveals a cluster of SIX genes in chromosome 14 and associates SIX9 hemizygosity with bilateral anophthalmia and pituitary anomalies.

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A Deep Learning-Based Workflow for Dendritic Spine Segmentation

The morphological analysis of dendritic spines is an important challenge for the neuroscientific community. Most state-of-the-art techniques rely on user-supervised algorithms to segment the spine surface, especially those designed for light microscopy images. Therefore, processing large dendritic branches is costly and time-consuming. Although deep learning (DL) models have become one of the most commonly used tools in image segmentation, they have not yet been successfully applied to this problem. In this article, we study the feasibility of using DL models to automatize spine segmentation from confocal microscopy images. Supervised learning is the most frequently used method for training DL models. This approach requires large data sets of high-quality segmented images (ground truth). As mentioned above, the segmentation of microscopy images is time-consuming and, therefore, in most cases, neuroanatomists only reconstruct relevant branches of the stack. Additionally, some parts of the dendritic shaft and spines are not segmented due to dyeing problems. In the context of this research, we tested the most successful architectures in the DL biomedical segmentation field. To build the ground truth, we used a large and high-quality data set, according to standards in the field. Nevertheless, this data set is not sufficient to train convolutional neural networks for accurate reconstructions. Therefore, we implemented an automatic preprocessing step and several training strategies to deal with the problems mentioned above. As shown by our results, our system produces a high-quality segmentation in most cases. Finally, we integrated several postprocessing user-supervised algorithms in a graphical user interface application to correct any possible artifacts.The research leading to these results has received funding from the following entities: the Spanish Government under grants FPU18/05304, PRE2018-085403, TIN2017-83132-C2-1-R, PID2020-113013RB-C21, BES-2017-081264, TIN2017-85572-P, and DPI2017-86372-C3-3-R and the European Union's Horizon 2020 Framework under the Specific Grant Agreement No. 945539 (HBP SGA3)