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Biomarkers in Psychiatry

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Cover of 'Biomarkers in Psychiatry'

Table of Contents

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    Book Overview
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    Chapter 41 Network Neuroscience: A Framework for Developing Biomarkers in Psychiatry
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    Chapter 42 Reappraising Preclinical Models of Separation Anxiety Disorder, Panic Disorder, and CO 2 Sensitivity: Implications for Methodology and Translation into New Treatments
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    Chapter 43 Immunological Processes in Schizophrenia Pathology: Potential Biomarkers?
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    Chapter 44 Translational Shifts in Preclinical Models of Depression: Implications for Biomarkers for Improved Treatments
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    Chapter 45 Neuroimmune Biomarkers in Mental Illness
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    Chapter 46 Imaging and Genetic Biomarkers Predicting Transition to Psychosis
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    Chapter 47 Using Pattern Classification to Identify Brain Imaging Markers in Autism Spectrum Disorder
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    Chapter 48 Deconstructing Schizophrenia: Advances in Preclinical Models for Biomarker Identification
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    Chapter 49 Imaging and Genetic Approaches to Inform Biomarkers for Anxiety Disorders, Obsessive–Compulsive Disorders, and PSTD
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    Chapter 50 Cognitive Phenotypes for Biomarker Identification in Mental Illness: Forward and Reverse Translation
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    Chapter 52 Genomic and Imaging Biomarkers in Schizophrenia
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    Chapter 57 Stem Cells to Inform the Neurobiology of Mental Illness
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    Chapter 58 Biomarkers in Neuropsychiatry: A Prospect for the Twenty-First Century?
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    Chapter 64 Correction to: Imaging and Genetic Approaches to Inform Biomarkers for Anxiety Disorders, Obsessive–Compulsive Disorders, and PSTD
Attention for Chapter 43: Immunological Processes in Schizophrenia Pathology: Potential Biomarkers?
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Chapter title
Immunological Processes in Schizophrenia Pathology: Potential Biomarkers?
Chapter number 43
Book title
Biomarkers in Psychiatry
Published in
Current topics in behavioral neurosciences, January 2018
DOI 10.1007/7854_2018_43
Pubmed ID
Book ISBNs
978-3-31-999641-7, 978-3-31-999642-4
Authors

Tina Notter

Abstract

Accumulating evidence suggests that the pathophysiology or schizophrenia involves alterations in immune functions, both peripherally and centrally. Immunopsychiatric research has provided a number of candidate biomarkers that could aid estimating the risk of developing schizophrenia and/or predicting its clinical course or outcomes. This chapter summarizes the findings of immune dysfunctions along the clinical course of schizophrenia and discusses their potential value as predictive, trait or state biomarkers. Given the convergence of findings deriving from immunology, epidemiology, and genetics, the possibility of identifying immune-based biomarkers of schizophrenia seems realistic. Despite these promises, however, the field has realized that immune dysfunctions in schizophrenia may be as heterogeneous as the disorder itself. While challenging for psychiatric nosology, this heterogeneity offers the opportunity to define patient subgroups based on the presence or absence of distinct immune dysfunctions. This stratification may be clinically relevant for schizophrenic patients as it may help establishing personalized add-on therapies or preventive interventions with immunomodulating drugs.

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X Demographics

The data shown below were collected from the profiles of 3 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 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 27 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 5 19%
Other 4 15%
Student > Bachelor 3 11%
Student > Ph. D. Student 2 7%
Professor > Associate Professor 1 4%
Other 0 0%
Unknown 12 44%
Readers by discipline Count As %
Medicine and Dentistry 5 19%
Psychology 2 7%
Biochemistry, Genetics and Molecular Biology 1 4%
Nursing and Health Professions 1 4%
Pharmacology, Toxicology and Pharmaceutical Science 1 4%
Other 4 15%
Unknown 13 48%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 11 April 2018.
All research outputs
#16,919,456
of 25,654,806 outputs
Outputs from Current topics in behavioral neurosciences
#324
of 516 outputs
Outputs of similar age
#272,399
of 451,419 outputs
Outputs of similar age from Current topics in behavioral neurosciences
#7
of 10 outputs
Altmetric has tracked 25,654,806 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 516 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 12.8. This one is in the 33rd percentile – i.e., 33% of its peers scored the same or lower than it.
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We're also able to compare this research output to 10 others from the same source and published within six weeks on either side of this one. This one has scored higher than 3 of them.