| Autor: |
Kateryna Tkachenko, María Espinosa, Isabel Esteban-Díez, José M. González-Sáiz, Consuelo Pizarro |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Chemosensors, Vol 10, Iss 6, p 229 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2227-9040 |
| DOI: |
10.3390/chemosensors10060229 |
| Popis: |
An untargeted Fourier transform infrared (FTIR) metabolomic approach was employed to study metabolic changes and disarrangements, recorded as infrared signatures, in Parkinson’s disease (PD). Herein, the principal aim was to propose an efficient sequential classification strategy based on SELECT-LDA, which enabled optimal stratification of three main categories: PD patients from subjects with Alzheimer’s disease (AD) and healthy controls (HC). Moreover, sub-categories, such as PD at the early stage (PDI) from PD in the advanced stage (PDD), and PDD vs. AD, were stratified. Every classification step with selected wavenumbers achieved 90.11% to 100% correct assignment rates in classification and internal validation. Therefore, selected metabolic signatures from new patients could be used as input features for screening and diagnostic purposes. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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