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Neuroproteomics

Overview of attention for book
Cover of 'Neuroproteomics'

Table of Contents

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    Book Overview
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    Chapter 1 Neuroproteomics Studies: Challenges and Updates
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    Chapter 2 Progress and Potential of Imaging Mass Spectrometry Applied to Biomarker Discovery
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    Chapter 3 Biofluid Proteomics and Biomarkers in Traumatic Brain Injury
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    Chapter 4 Degradomics in Neurotrauma: Profiling Traumatic Brain Injury
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    Chapter 5 Evolving Relevance of Neuroproteomics in Alzheimer’s Disease
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    Chapter 6 Genome to Phenome: A Systems Biology Approach to PTSD Using an Animal Model
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    Chapter 7 Photoaffinity Labeling of Pentameric Ligand-Gated Ion Channels: A Proteomic Approach to Identify Allosteric Modulator Binding Sites
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    Chapter 8 Quantitative Phosphoproteomic Analysis of Brain Tissues
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    Chapter 9 Glycoproteins Enrichment and LC-MS/MS Glycoproteomics in Central Nervous System Applications
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    Chapter 10 A Novel 2-DE-Based Proteomic Analysis to Identify Multiple Substrates for Specific Protease in Neuronal Cells
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    Chapter 11 Neuroproteomic Profiling of Cerebrospinal Fluid (CSF) by Multiplexed Affinity Arrays
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    Chapter 12 Isolation and Proteomic Analysis of Microvesicles and Exosomes from HT22 Cells and Primary Neurons
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    Chapter 13 Combined MALDI Mass Spectrometry Imaging and Parafilm-Assisted Microdissection-Based LC-MS/MS Workflows in the Study of the Brain
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    Chapter 14 De Novo and Uninterrupted SILAC Labeling of Primary Microglia
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    Chapter 15 Spike-In SILAC Approach for Proteomic Analysis of Ex Vivo Microglia
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    Chapter 16 A Proteomic Evaluation of Sympathetic Activity Biomarkers of the Hypothalamus-Pituitary-Adrenal Axis by Western Blotting Technique Following Experimental Traumatic Brain Injury
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    Chapter 17 Efficient and Accurate Algorithm for Cleaved Fragments Prediction (CFPA) in Protein Sequences Dataset Based on Consensus and Its Variants: A Novel Degradomics Prediction Application
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    Chapter 18 Effect of Second-Hand Tobacco Smoke on the Nitration of Brain Proteins: A Systems Biology and Bioinformatics Approach
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    Chapter 19 An Advanced Omic Approach to Identify Co-Regulated Clusters and Transcription Regulation Network with AGCT and SHOE Methods
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    Chapter 20 AutoDock and AutoDockTools for Protein-Ligand Docking: Beta-Site Amyloid Precursor Protein Cleaving Enzyme 1(BACE1) as a Case Study
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    Chapter 21 An Integration of Decision Tree and Visual Analysis to Analyze Intracranial Pressure
Attention for Chapter 21: An Integration of Decision Tree and Visual Analysis to Analyze Intracranial Pressure
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Chapter title
An Integration of Decision Tree and Visual Analysis to Analyze Intracranial Pressure
Chapter number 21
Book title
Neuroproteomics
Published in
Methods in molecular biology, January 2017
DOI 10.1007/978-1-4939-6952-4_21
Pubmed ID
Book ISBNs
978-1-4939-6950-0, 978-1-4939-6952-4
Authors

Soo-Yeon Ji, Kayvan Najarian, Toan Huynh, Dong Hyun Jeong

Editors

Firas H. Kobeissy, Stanley M. Stevens, Jr.

Abstract

In Traumatic Brain Injury (TBI), elevated Intracranial Pressure (ICP) causes severe brain damages due to hemorrhage and swelling. Monitoring ICP plays an important role in the treatment of TBI patients because ICP is considered a strong predictor of neurological outcome and a potentially amenable method to treat patients. However, it is difficult to predict and measure accurate ICP due to the complex nature of patients' clinical conditions. ICP monitoring for severe TBI patient is a challenging problem for clinicians because traditionally known ICP monitoring is an invasive procedure by placing a device inside the brain to measure pressure. Therefore, ICP monitoring might have a high infection risk and cause medical complications. In here, an ICP monitoring using texture features is proposed to overcome this limitation. The combination of image processing methods and a decision tree algorithm is utilized to estimate ICP of TBI patients noninvasively. In addition, a visual analytics tool is used to conduct an interactive visual factor analysis and outlier detection.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 16 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 3 19%
Other 1 6%
Student > Doctoral Student 1 6%
Professor 1 6%
Student > Bachelor 1 6%
Other 2 13%
Unknown 7 44%
Readers by discipline Count As %
Computer Science 2 13%
Psychology 2 13%
Medicine and Dentistry 2 13%
Engineering 2 13%
Unknown 8 50%