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- van Doorn, Ljcv, et al.
(författare)
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Improved Cerebrospinal Fluid-Based Discrimination between Alzheimer's Disease Patients and Controls after Correction for Ventricular Volumes
- 2017
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Ingår i: Journal of Alzheimers Disease. - : IOS Press. - 1387-2877 .- 1875-8908. ; 56:2, s. 543-555
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Tidskriftsartikel (refereegranskat)abstract
- Cerebrospinal fluid (CSF) biomarkers may support the diagnosis of Alzheimer's disease (AD). We studied if the diagnostic power of AD CSF biomarker concentrations, i.e., A beta(42), total tau (t-tau), and phosphorylated tau (p-tau), is affected by differences in lateral ventricular volume (VV), using CSF biomarker data and magnetic resonance imaging (MRI) scans of 730 subjects, from 13 European Memory Clinics. We developed a Matlab-algorithm for standardized automated segmentation analysis of T1 weighted MRI scans in SPM8 for determining VV, and computed its ratio with total intracranial volume (TIV) as proxy for total CSF volume. The diagnostic power of CSF biomarkers (and their combination), either corrected for VV/TIV ratio or not, was determined by ROC analysis. CSF A beta(42) levels inversely correlated to VV/TIV in the whole study population (A beta(42): r = -0.28; p < 0.0001). For CSF t-tau and p-tau, this association only reached statistical significance in the combined MCI and AD group (t-tau: r = -0.15; p-tau: r = -0.13; both p < 0.01). Correction for differences in VV/TIV improved the differentiation of AD versus controls based on CSF A beta(42) alone (AUC: 0.75 versus 0.81) or in combination with t-tau (AUC: 0.81 versus 0.91). In conclusion, differences in VV may be an important confounder in interpreting CSF A beta(42) levels.
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- Ostaszewski, Marek, et al.
(författare)
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COVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms
- 2021
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Ingår i: Molecular Systems Biology. - : John Wiley & Sons. - 1744-4292 .- 1744-4292. ; 17:10
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Tidskriftsartikel (refereegranskat)abstract
- We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective.
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