Linked Life Data

9331074

Source:http://linkedlifedata.com/resource/pubmed/id/9331074

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
19
pubmed:dateCreated
1997-10-28
pubmed:abstractText
Both tumor metabolism and its response to cytotoxic drugs are intrinsic properties of tumor cells. It is therefore likely that there is a relationship between the two properties, however subtle and complex, wherein the metabolic characteristics of tumor cells can reflect the inherent response (resistance or sensitivity) of these cells to cytotoxic drugs. We used artificial neural network analysis to show that it is possible to distinguish, prior to treatment, between drug-resistant and drug-sensitive human glioma cell cultures from their metabolic profiles, as given by high-resolution proton nuclear magnetic resonance spectra of the cell extracts, and to predict their cellular response to the chemotherapeutic drug 1-(2-chloroethyl)-3-cyclohexyl-1-nitrosourea in vitro. The results suggest that neural network analysis of tumor nuclear magnetic resonance spectra has potential as a prognostic tool for determining treatment of gliomas, ultimately noninvasively, and may be used to provide information about the metabolic pathways involved in drug response that may be helpful in developing novel treatments for these tumors.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Oct
pubmed:issn
0008-5472
pubmed:author
pubmed:issnType
Print
pubmed:day
1
pubmed:volume
57
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
4196-9
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
1997
pubmed:articleTitle
Pretreatment prediction of the chemotherapeutic response of human glioma cell cultures using nuclear magnetic resonance spectroscopy and artificial neural networks.
pubmed:affiliation
Department of Neurological Surgery, Institute of Neurology, London, United Kingdom. W.ElDeredy@ion.ucl.ac.uk
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't