Linked Life Data

15593384

Source:http://linkedlifedata.com/resource/pubmed/id/15593384

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2004-12-13
pubmed:abstractText
To screen and evaluate protein biomarkers for the detection of gliomas (Astrocytoma grade I-IV) from healthy individuals and gliomas from brain benign tumors by using surface enhanced laser desorption/ionization time of flight mass spectrometry (SELDI-TOF-MS) coupled with an artificial neural network (ANN) algorithm. SELDI-TOF-MS protein fingerprinting of serum from 105 brain tumor patients and healthy individuals, included 28 patients with glioma (Astrocytoma I-IV), 37 patients with brain benign tumor, and 40 age-matched healthy individuals. Two thirds of the total samples of every compared pair as training set were used to set up discriminating patterns, and one third of total samples of every compared pair as test set were used to cross-validate; simultaneously, discriminate-cluster analysis derived SPSS 10.0 software was used to compare Astrocytoma grade I-II with grade III-IV ones. An accuracy of 95.7%, sensitivity of 88.9%, specificity of 100%, positive predictive value of 90% and negative predictive value of 100% were obtained in a blinded test set comparing gliomas patients with healthy individuals; an accuracy of 86.4%, sensitivity of 88.9%, specificity of 84.6%, positive predictive value of 90% and negative predictive value of 85.7% were obtained when patient's gliomas was compared with benign brain tumor. Total accuracy of 85.7%, accuracy of grade I-II Astrocytoma was 86.7%, accuracy of III-IV Astrocytoma was 84.6% were obtained when grade I-II Astrocytoma was compared with grade III-IV ones (discriminant analysis). SELDI-TOF-MS combined with bioinformatics tools, could greatly facilitate the discovery of better biomarkers. The high sensitivity and specificity achieved by the use of selected biomarkers showed great potential application for the discrimination of gliomas patients from healthy individuals and gliomas from brain benign tumors.
pubmed:commentsCorrections
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Jan
pubmed:issn
1673-1581
pubmed:author
pubmed:issnType
Print
pubmed:volume
6
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
4-10
pubmed:dateRevised
2009-11-18
pubmed:meshHeading
pubmed-meshheading:15593384-Adult, pubmed-meshheading:15593384-Aged, pubmed-meshheading:15593384-Algorithms, pubmed-meshheading:15593384-Artificial Intelligence, pubmed-meshheading:15593384-Astrocytoma, pubmed-meshheading:15593384-Brain Neoplasms, pubmed-meshheading:15593384-Diagnosis, Computer-Assisted, pubmed-meshheading:15593384-Female, pubmed-meshheading:15593384-Humans, pubmed-meshheading:15593384-Male, pubmed-meshheading:15593384-Middle Aged, pubmed-meshheading:15593384-Neoplasm Proteins, pubmed-meshheading:15593384-Neural Networks (Computer), pubmed-meshheading:15593384-Peptide Mapping, pubmed-meshheading:15593384-Protein Array Analysis, pubmed-meshheading:15593384-Reproducibility of Results, pubmed-meshheading:15593384-Sensitivity and Specificity, pubmed-meshheading:15593384-Spectrometry, Mass, Matrix-Assisted Laser..., pubmed-meshheading:15593384-Tumor Markers, Biological
pubmed:year
2005
pubmed:articleTitle
Serum protein fingerprinting coupled with artificial neural network distinguishes glioma from healthy population or brain benign tumor.
pubmed:publicationType
Letter, Clinical Trial, Comparative Study, Controlled Clinical Trial, Research Support, Non-U.S. Gov't, Validation Studies