UNASSIGNED: The discovery and validation of biomarkers in @entity16 is an important challenge for early diagnosis of disease and for the development of therapeutics. @entity302 is often a consequence of brain insults such as @entity131 or @entity64 , but as yet no biomarker exists to predict the development of @entity302 in @entity1 at risk. Given the complexity of @entity302 , it is unlikely that a single biomarker is sufficient for this purpose, but a combinatorial approach may be needed to overcome the challenge of individual variability and disease heterogeneity. The goal of the present prospective study in the @entity1091 - @entity2134 model of @entity302 in @entity35 was to determine the discriminative utility of combinations of phenotypic biomarkers by examining their ability to predict @entity302 . For this purpose, we used a recent model refinement that allows comparing @entity35 that will or will not develop @entity410 ( @entity410 ) after @entity2134 -induced @entity159 ( @entity159 ). Potential biomarkers included in our study were @entity410 threshold and @entity410 severity in response to timed i.v. infusion of @entity5093 ( @entity17202 ) and behavioral alterations determined by a battery of tests during the three weeks following @entity159 . Three months after @entity159 , video/EEG monitoring was used to determine which @entity35 had developed @entity410 . To determine whether a biomarker or combination of biomarkers performed better than chance at predicting @entity302 after @entity159 , derived data underwent receiver operating characteristic (ROC) curve analyses. When comparing @entity35 with and without @entity410 and sham controls, the best intergroup discrimination was obtained by combining all measurements, resulting in a ROC area under curve (AUC) of 0.9592 (P<0.01), indicating an almost perfect discrimination or accuracy to predict development of @entity410 . These data indicate that a combinatorial biomarker approach may overcome the challenge of individual variability in the prediction of @entity302 .

Novel combinations of phenotypic biomarkers predict development of @entity302 in the XXXX - @entity2134 model of temporal lobe epilepsy in @entity35 .

@entity1 :: ('9606', 'Species') :: ['patients'] @entity17202 :: ('D010433', 'Chemical') :: ['PTZ'] @entity131 :: ('MESH:D001930', 'Disease') :: ['traumatic brain injury'] @entity64 :: ('MESH:D020521', 'Disease') :: ['stroke'] @entity410 :: ('MESH:D012640', 'Disease') :: ['seizure', 'SRS', 'spontaneous recurrent seizures'] @entity16 :: ('MESH:D009422', 'Disease') :: ['neurological and neurodegenerative diseases'] @entity5093 :: ('MESH:D010433', 'Chemical') :: ['pentylenetetrazole'] @entity2134 :: ('MESH:D010862', 'Chemical') :: ['pilocarpine'] @entity302 :: ('MESH:D004827', 'Disease') :: ['epilepsy', 'Epilepsy'] @entity35 :: ('10116', 'Species') :: ['rats'] @entity1091 :: ('MESH:D008094', 'Chemical') :: ['lithium'] @entity159 :: ('MESH:D013226', 'Disease') :: ['status epilepticus', 'SE']
@entity1091 :: (MESH:D008094,Chemical) :: ['lithium']
