Please use this identifier to cite or link to this item: http://hdl.handle.net/10316/90808
DC FieldValueLanguage
dc.contributor.authorIldiz, Gülce Ögrüç-
dc.contributor.authorBayari, Sevgi-
dc.contributor.authorKaradag, Ahmet-
dc.contributor.authorKaygisiz, Ersin-
dc.contributor.authorLourenço, Rui Fausto-
dc.date.accessioned2020-09-08T13:27:11Z-
dc.date.available2020-09-08T13:27:11Z-
dc.date.issued2020-04-29-
dc.identifier.issn1420-3049pt
dc.identifier.urihttp://hdl.handle.net/10316/90808-
dc.description.abstractAutism spectrum disorder (ASD) is a neurodevelopmental disorder that begins early in life and continues lifelong with strong personal and societal implications. It affects about 1%-2% of the children population in the world. The absence of auxiliary methods that can complement the clinical evaluation of ASD increases the probability of false identification of the disorder, especially in the case of very young children. In this study, analytical models for auxiliary diagnosis of ASD in children and adolescents, based on the analysis of patients' blood serum ATR-FTIR (Attenuated Total Reflectance-Fourier Transform Infrared) spectra, were developed. The models use chemometrics (either Principal Component Analysis (PCA) or Partial Least Squares Discriminant Analysis (PLS-DA)) methods, with the infrared spectra being the X-predictor variables. The two developed models exhibit excellent classification performance for samples of ASD individuals vs. healthy controls. Interestingly, the simplest, unsupervised PCA-based model results to have a global performance identical to the more demanding, supervised (PLS-DA)-based model. The developed PCA-based model thus appears as the more economical alternative one for use in the clinical environment. Hierarchical clustering analysis performed on the full set of samples was also successful in discriminating the two groups.pt
dc.language.isoengpt
dc.publisherMDPIpt
dc.relationUI0313/QUI/2020pt
dc.rightsopenAccesspt
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt
dc.subjectFTIR spectroscopypt
dc.subjectAutism spectrum disorderpt
dc.subjectChemometricspt
dc.titleFourier Transform Infrared Spectroscopy Based Complementary Diagnosis Tool for Autism Spectrum Disorder in Children and Adolescentspt
dc.typearticle-
degois.publication.firstPage2079pt
degois.publication.issue9pt
degois.publication.titleJournal Molecules (Basel, Switzerland)pt
dc.relation.publisherversionhttps://www.mdpi.com/1420-3049/25/9/2079pt
dc.peerreviewedyespt
dc.identifier.doi10.3390/molecules25092079pt
degois.publication.volume25pt
dc.date.embargo2020-04-29*
uc.date.periodoEmbargo0pt
item.grantfulltextopen-
item.languageiso639-1en-
item.fulltextCom Texto completo-
crisitem.author.deptFaculdade de Ciências e Tecnologia, Universidade de Coimbra-
crisitem.author.parentdeptUniversidade de Coimbra-
crisitem.author.researchunitCoimbra Chemistry Center-
crisitem.author.researchunitCoimbra Chemistry Center-
crisitem.author.parentresearchunitFaculdade de Ciências e Tecnologia, Universidade de Coimbra-
crisitem.author.parentresearchunitFaculdade de Ciências e Tecnologia, Universidade de Coimbra-
crisitem.author.orcid0000-0002-7827-5050-
crisitem.author.orcid0000-0002-8264-6854-
Appears in Collections:I&D CQC - Artigos em Revistas Internacionais
Files in This Item:
File Description SizeFormat
Molecules_25_2020_2079.pdf3.27 MBAdobe PDFView/Open
Show simple item record

Page view(s)

1
checked on Oct 26, 2020

Download(s)

1
checked on Oct 26, 2020

Google ScholarTM

Check

Altmetric

Altmetric


This item is licensed under a Creative Commons License Creative Commons