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Computational Systems Biology

Overview of attention for book
Cover of 'Computational Systems Biology'

Table of Contents

  1. Altmetric Badge
    Book Overview
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    Chapter 1 DNA Sequencing Data Analysis
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    Chapter 2 Transcriptome Sequencing: RNA-Seq
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    Chapter 3 Capture Hybridization of Long-Range DNA Fragments for High-Throughput Sequencing
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    Chapter 4 The Introduction and Clinical Application of Cell-Free Tumor DNA
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    Chapter 5 Bioinformatics Analysis for Cell-Free Tumor DNA Sequencing Data
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    Chapter 6 An Overview of Genome-Wide Association Studies
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    Chapter 7 Integrative Analysis of Omics Big Data
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    Chapter 8 The Reconstruction and Analysis of Gene Regulatory Networks
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    Chapter 9 Differential Coexpression Network Analysis for Gene Expression Data
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    Chapter 10 iSeq: Web-Based RNA-seq Data Analysis and Visualization
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    Chapter 11 Revisit of Machine Learning Supported Biological and Biomedical Studies
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    Chapter 12 Identifying Interactions Between Long Noncoding RNAs and Diseases Based on Computational Methods
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    Chapter 13 Survey of Computational Approaches for Prediction of DNA-Binding Residues on Protein Surfaces
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    Chapter 14 Computational Prediction of Protein O-GlcNAc Modification
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    Chapter 15 Machine Learning-Based Modeling of Drug Toxicity
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    Chapter 16 Metabolomics: A High-Throughput Platform for Metabolite Profile Exploration
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    Chapter 17 Single-Cell Protein Assays: A Review
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    Chapter 18 Data Analysis in Single-Cell Transcriptome Sequencing
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    Chapter 19 Applications of Single-Cell Sequencing for Multiomics
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    Chapter 20 Progress on Diagnosis of Tuberculous Meningitis
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    Chapter 21 Insights of Acute Lymphoblastic Leukemia with Development of Genomic Investigation
Attention for Chapter 20: Progress on Diagnosis of Tuberculous Meningitis
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Chapter title
Progress on Diagnosis of Tuberculous Meningitis
Chapter number 20
Book title
Computational Systems Biology
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-7717-8_20
Pubmed ID
29536453
Book ISBNs
978-1-4939-7716-1, 978-1-4939-7717-8
Authors

Wang, Yi-yi, Xie, Bing-di, Yi-yi Wang, Bing-di Xie

Abstract

Central nervous system (CNS) disease caused by Mycobacterium tuberculosis (MTB) is highly devastating. Tuberculous meningitis (TBM) is the most common form of CNS tuberculosis (TB). Rapid, sensitive, and affordable diagnostic tests are not available. Ziehl-Neelsen (ZN) stain has a very low sensitivity in cases of TBM, the sensitivity rates is of about 10-20%.The detection rate can be improved by taking large volume CSF samples (>6 ml) and prolonged slide examination (30 min). Culture of MTB from the CSF is slow and insufficiently sensitive. The sensitivity is different, which varies from 36% to 81.8%. The microscopic observation drug susceptibility (MODS) assay was recommended by the World Health Organization in 2011. The sensitivity is 65%, which is more sensitive and faster than CSF smear. Commercial PCR assays were found to be insensitive at detecting MTB in CSF samples. Many research provided the value of ADA on the TBM diagnosis. Interferon-gamma release assays (IGRAs) are not recommended for diagnosis of active TB disease. Imaging is essential in diagnosis and showing complications of CNS TB. Thwaites criteria and the Lancet consensus scoring system (LCSS) were developed to improve the diagnosis of TBM. Clinicians will continue to make judgment based on clinical examination, inflammatory CSF examinations, imaging studies, and scoring systems.

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X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
 Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 49 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 49 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 20%
Student > Postgraduate 6 12%
Other 4 8%
Student > Doctoral Student 3 6%
Student > Bachelor 3 6%
Other 7 14%
Unknown 16 33%
Readers by discipline Count As %
Medicine and Dentistry 21 43%
Immunology and Microbiology 4 8%
Neuroscience 3 6%
Nursing and Health Professions 2 4%
Biochemistry, Genetics and Molecular Biology 2 4%
Other 1 2%
Unknown 16 33%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 15 March 2018.
All research outputs
#18,590,133
of 23,026,672 outputs
Outputs from Methods in molecular biology
#7,971
of 13,170 outputs
Outputs of similar age
#330,575
of 442,370 outputs
Outputs of similar age from Methods in molecular biology
#950
of 1,499 outputs
Altmetric has tracked 23,026,672 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,170 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 24th percentile – i.e., 24% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 442,370 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 14th percentile – i.e., 14% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1,499 others from the same source and published within six weeks on either side of this one. This one is in the 20th percentile – i.e., 20% of its contemporaries scored the same or lower than it.

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