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Sökning: WFRF:(Takala Riikka Sk.)

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1.
  • Dickens, Alex Mountfort, et al. (författare)
  • Serum Metabolites Associated with Computed TomographyFindings after Traumatic Brain Injury
  • 2018
  • Ingår i: Journal of Neurotrauma. - : Mary Ann Liebert. - 0897-7151 .- 1557-9042. ; 35:22, s. 2673-2683
  • Tidskriftsartikel (refereegranskat)abstract
    • There is a need to rapidly detect patients with traumatic brain injury (TBI) who require head computed tomography (CT). Given the energy crisis in the brain following TBI, we hypothesized that serum metabolomics would be a useful tool for developing a set of biomarkers to determine the need for CT and to distinguish between different types of injuries observed. Logistic regression models using metabolite data from the discovery cohort (n=144, Turku, Finland) were used to distinguish between patients with traumatic intracranial findings and negative findings on head CT. The resultant models were then tested in the validation cohort (n=66, Cambridge, UK). The levels of glial fibrillary acidic protein and ubiquitin C-terminal hydrolase-L1 were also quantified in the serum from the same patients. Despite there being significant differences in the protein biomarkers in patients with TBI, the model that determined the need for a CT scan validated poorly (AUC=0.64: Cambridge patients). However, using a combination of six metabolites (two amino acids, three sugar derivatives and one ketoacid) it was possible to discriminate patients with intracranial abnormalities on CT and patients with a normal CT (AUC=0.77 in Turku patients and AUC=0.73 in Cambridge patients). Furthermore, a combination of three metabolites could distinguish between diffuse brain injuries and mass lesions (AUC=0.87 in Turku patients and AUC=0.68 in Cambridge patients). This study identifies a set of validated serum polar metabolites, which associate with the need for a CT scan. Additionally, serum metabolites can also predict the nature of the brain injury. These metabolite markers may prevent unnecessary CT scans, thus reducing the cost of diagnostics and radiation load.
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2.
  • Hossain, Iftakher, et al. (författare)
  • Early Levels of Glial Fibrillary Acidic Protein and Neurofilament Light Protein in Predicting the Outcome of Mild Traumatic Brain Injury
  • 2019
  • Ingår i: Journal of neurotrauma. - : Mary Ann Liebert Inc. - 1557-9042 .- 0897-7151. ; 36:10, s. 1551-1560
  • Tidskriftsartikel (refereegranskat)abstract
    • To correlate the early levels of glial fibrillary acidic protein (GFAP) and neurofilament light protein (NF-L) with outcome in patients with mild traumatic brain injury (mTBI). 107 patients with mTBI [Glasgow Coma Scale (GCS) ≥13] having the blood samples for GFAP and NF-L available within 24 hrs from arrival were included. Patients with mTBI were divided into computed tomography (CT)-positive and CT-negative groups. Glasgow Outcome Scale extended (GOSE) was used to assess the outcome. Outcomes were defined as complete (GOSE 8) vs. incomplete (GOSE <8), and favorable (GOSE 5-8) vs. unfavorable (GOSE 1-4). GFAP and NF-L concentrations in blood were measured using ultrasensitive single molecule array technology. Patients with incomplete recovery had significantly higher levels of NF-L compared to those with complete recovery (p=0.005). The levels of GFAP and NF-L were significantly higher in patients with unfavorable outcome than in patients with favorable outcome (p=0.002 for GFAP and p <0.001 for NF-L). For predicting favorable outcome, the area under the ROC curve for GFAP and NF-L was 0.755 and 0.826, respectively. In a multivariate logistic regression model, the level of NF-L was still a significant predictor for complete recovery (OR=1.008, 95%CI, 1.000-1.016). Moreover, the level of NF-L was a significant predictor for complete recovery in CT-positive patients (OR=1.009, 95%CI, 1.001-1.016). The early levels of GFAP and NF-L are significantly correlated with the outcome in patients with mTBI. The level of NF-L within 24 hrs from arrival has a significant predictive value in mTBI also in a multivariate model.
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