Intelligent Identification of Cerebrospinal Fluid for the Diagnosis of Parkinson’s Disease

Cerebrospinal fluid (CSF) biomarkers are more sensitive than the Movement Disorder Society (MDS) criteria for detecting prodromal Parkinson’s disease (PD). Early detection of PD provides the best chance for successful implementation of disease-modifying treatments, making it crucial to effectively i...

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Published inAnalytical chemistry (Washington) Vol. 96; no. 6; pp. 2534 - 2542
Main Authors Chen, Huiting, Guo, Siyun, Zhuang, Zehong, Ouyang, Sixue, Lin, Peiru, Zheng, Zhiyuan, You, Yuanyuan, Zhou, Xiang, Li, Yuan, Lu, Jiajia, Liu, Ningxuan, Tao, Jia, Long, Hao, Zhao, Peng
Format Journal Article
LanguageEnglish
Published United States American Chemical Society 13.02.2024
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ISSN0003-2700
1520-6882
1520-6882
DOI10.1021/acs.analchem.3c04849

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Abstract Cerebrospinal fluid (CSF) biomarkers are more sensitive than the Movement Disorder Society (MDS) criteria for detecting prodromal Parkinson’s disease (PD). Early detection of PD provides the best chance for successful implementation of disease-modifying treatments, making it crucial to effectively identify CSF extracted from PD patients or normal individuals. In this study, an intelligent sensor array was built by using three metal–organic frameworks (MOFs) that exhibited varying catalytic kinetics after reacting with potential protein markers. Machine learning algorithms were used to process fingerprint response patterns, allowing for qualitative and quantitative assessment of the proteins. The results were robust and capable of discriminating between PD and non-PD patients via CSF detection. The k-nearest neighbor regression algorithm was used to predict MDS scores with a minimum mean square error of 38.88. The intelligent MOF sensor array is expected to promote the detection of CSF biomarkers due to its ability to identify multiple targets and could be used in conjunction with MDS criteria and other techniques to diagnose PD more sensitively and selectively.
AbstractList Cerebrospinal fluid (CSF) biomarkers are more sensitive than the Movement Disorder Society (MDS) criteria for detecting prodromal Parkinson's disease (PD). Early detection of PD provides the best chance for successful implementation of disease-modifying treatments, making it crucial to effectively identify CSF extracted from PD patients or normal individuals. In this study, an intelligent sensor array was built by using three metal–organic frameworks (MOFs) that exhibited varying catalytic kinetics after reacting with potential protein markers. Machine learning algorithms were used to process fingerprint response patterns, allowing for qualitative and quantitative assessment of the proteins. The results were robust and capable of discriminating between PD and non-PD patients via CSF detection. The k-nearest neighbor regression algorithm was used to predict MDS scores with a minimum mean square error of 38.88. The intelligent MOF sensor array is expected to promote the detection of CSF biomarkers due to its ability to identify multiple targets and could be used in conjunction with MDS criteria and other techniques to diagnose PD more sensitively and selectively.
Cerebrospinal fluid (CSF) biomarkers are more sensitive than the Movement Disorder Society (MDS) criteria for detecting prodromal Parkinson's disease (PD). Early detection of PD provides the best chance for successful implementation of disease-modifying treatments, making it crucial to effectively identify CSF extracted from PD patients or normal individuals. In this study, an intelligent sensor array was built by using three metal-organic frameworks (MOFs) that exhibited varying catalytic kinetics after reacting with potential protein markers. Machine learning algorithms were used to process fingerprint response patterns, allowing for qualitative and quantitative assessment of the proteins. The results were robust and capable of discriminating between PD and non-PD patients via CSF detection. The k-nearest neighbor regression algorithm was used to predict MDS scores with a minimum mean square error of 38.88. The intelligent MOF sensor array is expected to promote the detection of CSF biomarkers due to its ability to identify multiple targets and could be used in conjunction with MDS criteria and other techniques to diagnose PD more sensitively and selectively.Cerebrospinal fluid (CSF) biomarkers are more sensitive than the Movement Disorder Society (MDS) criteria for detecting prodromal Parkinson's disease (PD). Early detection of PD provides the best chance for successful implementation of disease-modifying treatments, making it crucial to effectively identify CSF extracted from PD patients or normal individuals. In this study, an intelligent sensor array was built by using three metal-organic frameworks (MOFs) that exhibited varying catalytic kinetics after reacting with potential protein markers. Machine learning algorithms were used to process fingerprint response patterns, allowing for qualitative and quantitative assessment of the proteins. The results were robust and capable of discriminating between PD and non-PD patients via CSF detection. The k-nearest neighbor regression algorithm was used to predict MDS scores with a minimum mean square error of 38.88. The intelligent MOF sensor array is expected to promote the detection of CSF biomarkers due to its ability to identify multiple targets and could be used in conjunction with MDS criteria and other techniques to diagnose PD more sensitively and selectively.
Author Zhuang, Zehong
Chen, Huiting
Tao, Jia
Zhou, Xiang
Ouyang, Sixue
Zheng, Zhiyuan
You, Yuanyuan
Li, Yuan
Liu, Ningxuan
Lin, Peiru
Lu, Jiajia
Long, Hao
Zhao, Peng
Guo, Siyun
AuthorAffiliation NMPA Key Laboratory for Research and Evaluation of Drug Metabolism, Guangdong Provincial Key Laboratory of New Drug Screening, Guangdong Provincial Key Laboratory of Cardiac Function and Microcirculation, School of Pharmaceutical Sciences
Department of Neurosurgery, Nanfang Hospital
School of Chemistry and Chemical Engineering
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Snippet Cerebrospinal fluid (CSF) biomarkers are more sensitive than the Movement Disorder Society (MDS) criteria for detecting prodromal Parkinson’s disease (PD)....
Cerebrospinal fluid (CSF) biomarkers are more sensitive than the Movement Disorder Society (MDS) criteria for detecting prodromal Parkinson's disease (PD)....
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SubjectTerms Algorithms
Biomarkers
Biomarkers - cerebrospinal fluid
Cerebrospinal fluid
Criteria
Early Diagnosis
Humans
Machine Learning
Metal-organic frameworks
Movement disorders
Neurodegenerative diseases
Parkinson Disease - diagnosis
Parkinson's disease
Proteins
Reaction kinetics
Sensor arrays
Target recognition
Title Intelligent Identification of Cerebrospinal Fluid for the Diagnosis of Parkinson’s Disease
URI http://dx.doi.org/10.1021/acs.analchem.3c04849
https://www.ncbi.nlm.nih.gov/pubmed/38302490
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Volume 96
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