Paper spray mass spectrometry combined with machine learning as a rapid diagnostic for chronic kidney disease.
Analyst
; 149(9): 2600-2608, 2024 Apr 29.
Article
em En
| MEDLINE
| ID: mdl-38529879
ABSTRACT
A new analytical method for chronic kidney disease (CKD) detection utilizing paper spray mass spectrometry (PS-MS) combined with machine learning is presented. The analytical protocol is rapid and simple, based on metabolic profile alterations in urine. Anonymized raw urine samples were deposited (10 µL each) onto pointed PS-MS sample strips. Without waiting for the sample to dry, 75 µL of acetonitrile and high voltage were applied to the strips, using high resolution mass spectrometry measurement (15 s per sample) with polarity switching to detect a wide range of metabolites. Random forest machine learning was used to classify the resulting data. The diagnostic performance for the potential diagnosis of CKD was evaluated for accuracy, sensitivity, and specificity, achieving results >96% for the training data and >91% for validation and test data sets. Metabolites selected by the classification model as up- or down-regulated in healthy or CKD samples were tentatively identified and in agreement with previously reported literature. The potential utilization of this approach to discriminate albuminuria categories (normo, micro, and macroalbuminuria) was also demonstrated. This study indicates that PS-MS combined with machine learning has the potential to be used as a rapid and simple diagnostic tool for CKD.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Espectrometria de Massas
/
Insuficiência Renal Crônica
/
Aprendizado de Máquina
Limite:
Humans
Idioma:
En
Revista:
Analyst
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Canadá