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Biosignatures for Parkinson’s Disease and Atypical Parkinsonian Disorders Patients

Overview of attention for article published in PLOS ONE, August 2012
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Title
Biosignatures for Parkinson’s Disease and Atypical Parkinsonian Disorders Patients
Published in
PLOS ONE, August 2012
DOI 10.1371/journal.pone.0043595
Pubmed ID
Authors

Judith A. Potashkin, Jose A. Santiago, Bernard M. Ravina, Arthur Watts, Alexey A. Leontovich

Abstract

Diagnosis of Parkinson' disease (PD) carries a high misdiagnosis rate due to failure to recognize atypical parkinsonian disorders (APD). Usually by the time of diagnosis greater than 60% of the neurons in the substantia nigra are dead. Therefore, early detection would be beneficial so that therapeutic intervention may be initiated early in the disease process. We used splice variant-specific microarrays to identify mRNAs whose expression is altered in peripheral blood of early-stage PD patients compared to healthy and neurodegenerative disease controls. Quantitative polymerase chain reaction assays were used to validate splice variant transcripts in independent sample sets. Here we report a PD signature used to classify blinded samples with 90% sensitivity and 94% specificity and an APD signature that resulted in a diagnosis with 95% sensitivity and 94% specificity. This study provides the first discriminant functions with coherent diagnostic signatures for PD and APD. Analysis of the PD biomarkers identified a regulatory network with nodes centered on the transcription factors HNF4A and TNF, which have been implicated in insulin regulation.

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Geographical breakdown

Country Count As %
United States 2 2%
Germany 1 1%
Unknown 82 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 17 20%
Student > Ph. D. Student 10 12%
Student > Bachelor 9 11%
Student > Master 8 9%
Student > Doctoral Student 7 8%
Other 16 19%
Unknown 18 21%
Readers by discipline Count As %
Medicine and Dentistry 19 22%
Agricultural and Biological Sciences 15 18%
Biochemistry, Genetics and Molecular Biology 10 12%
Neuroscience 5 6%
Engineering 3 4%
Other 8 9%
Unknown 25 29%