Diagnostic prediction of early silicosis patients using neural network and MALDI-TOF-MS in serum / 生物医学工程学杂志
Journal of Biomedical Engineering
;
(6): 142-147, 2011.
Article
Dans Chinois
| WPRIM
| ID: wpr-306604
ABSTRACT
Serum of 79 workers exposed to silica and 25 healthy controls cases were determined by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS). 7 protein peaks were selected and used by artificial neural network (ANN) to establish a diagnostic model. A blinded test showed that accuracy, sensitivity and specificity were 91.35%, 93.69%, and 84.52%, respectively. The diagnostic pattern was also established to distinguish each stage of silica-exposed population. The diagnostic pattern worked excellently with 89.23%, 94.20% and 92.37% of accurate rate for classifying phase 0, phase 0+, and phase I of silicosis, respectively.
Texte intégral:
Disponible
Indice:
WPRIM (Pacifique occidental)
Sujet Principal:
Silicose
/
Sang
/
Protéines du sang
/
Marqueurs biologiques
/
Sensibilité et spécificité
/
Classification
/
/
Spectrométrie de masse MALDI
/
Diagnostic
/
Méthodes
Type d'étude:
Etude diagnostique
/
Étude pronostique
Limites du sujet:
Humains
langue:
Chinois
Texte intégral:
Journal of Biomedical Engineering
Année:
2011
Type:
Article
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