Title |
Proteomic profiling in schizophrenia: enabling stratification for more effective treatment
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Published in |
Genome Medicine, March 2013
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DOI | 10.1186/gm429 |
Pubmed ID | |
Authors |
Paul C Guest, Daniel Martins-de-Souza, Emanuel Schwarz, Hassan Rahmoune, Murtada Alsaif, Jakub Tomasik, Christoph W Turck, Sabine Bahn |
Abstract |
Schizophrenia is a heterogeneous psychiatric disorder characterized by an array of clinical manifestations. Although the best known manifestations include serious effects on mood and behavior, patients can also display co-morbidities, including immune system or metabolic abnormalities. Thorough characterization of these conditions using proteomic profiling methods has increased our knowledge of these molecular differences and has helped to unravel the complexity and heterogeneity of this debilitating condition. This could lead to patient stratification through characterization of biochemically different subtypes of the disease. In addition, proteomic methods have recently been used for molecular characterization of the mechanism of action of antipsychotic medications in both preclinical models and patients. This has resulted in identification of molecular panels that show some promise for prediction of response or for monitoring treatment outcome. This review describes how proteomic profiling methods can impact the future of schizophrenia diagnosis and therapeutics, and facilitate personalized medicine approaches for more effective treatment management of schizophrenia patients. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United States | 1 | 33% |
Brazil | 1 | 33% |
United Kingdom | 1 | 33% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 3 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 2 | 2% |
United States | 1 | 1% |
Unknown | 90 | 97% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 14 | 15% |
Researcher | 13 | 14% |
Student > Bachelor | 13 | 14% |
Student > Master | 11 | 12% |
Other | 8 | 9% |
Other | 17 | 18% |
Unknown | 17 | 18% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 21 | 23% |
Agricultural and Biological Sciences | 20 | 22% |
Biochemistry, Genetics and Molecular Biology | 12 | 13% |
Neuroscience | 7 | 8% |
Psychology | 6 | 6% |
Other | 7 | 8% |
Unknown | 20 | 22% |