| 1. |
- Bratulic, Sinisa, 1981, et al.
(author)
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The Translational Status of Cancer Liquid Biopsies
- 2021
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In: Regenerative Engineering and Translational Medicine. - : Springer Science and Business Media LLC. - 2364-4133 .- 2364-4141. ; 7:3, s. 312-352
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Journal article (peer-reviewed)abstract
- Precision oncology aims to tailor clinical decisions specifically to patients with the objective of improving treatment outcomes. This can be achieved by leveraging omics information for accurate molecular characterization of tumors. Tumor tissue biopsies are currently the main source of information for molecular profiling. However, biopsies are invasive and limited in resolving spatiotemporal heterogeneity in tumor tissues. Alternative non-invasive liquid biopsies can exploit patient’s body fluids to access multiple layers of tumor-specific biological information (genomes, epigenomes, transcriptomes, proteomes, metabolomes, circulating tumor cells, and exosomes). Analysis and integration of these large and diverse datasets using statistical and machine learning approaches can yield important insights into tumor biology and lead to discovery of new diagnostic, predictive, and prognostic biomarkers. Translation of these new diagnostic tools into standard clinical practice could transform oncology, as demonstrated by a number of liquid biopsy assays already entering clinical use. In this review, we highlight successes and challenges facing the rapidly evolving field of cancer biomarker research. Lay Summary: Precision oncology aims to tailor clinical decisions specifically to patients with the objective of improving treatment outcomes. The discovery of biomarkers for precision oncology has been accelerated by high-throughput experimental and computational methods, which can inform fine-grained characterization of tumors for clinical decision-making. Moreover, advances in the liquid biopsy field allow non-invasive sampling of patient’s body fluids with the aim of analyzing circulating biomarkers, obviating the need for invasive tumor tissue biopsies. In this review, we highlight successes and challenges facing the rapidly evolving field of liquid biopsy cancer biomarker research.
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| 3. |
- Barbé, Kurt, et al.
(author)
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Fractional-Order Time Series Models for Extracting the Haemodynamic Response From Functional Magnetic Resonance Imaging Data
- 2012
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In: IEEE Transactions on Biomedical Engineering. - 0018-9294 .- 1558-2531. ; 59:8, s. 2264-2272
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Journal article (peer-reviewed)abstract
- The postprocessing of functional magnetic resonance imaging (fMRI) data to study the brain functions deals mainly with two objectives: signal detection and extraction of the haemodynamic response. Signal detection consists of exploring and detecting those areas of the brain that are triggered due to an external stimulus. Extraction of the haemodynamic response deals with describing and measuring the physiological process of activated regions in the brain due to stimulus. The haemodynamic response represents the change in oxygen levels since the brain functions require more glucose and oxygen upon stimulus that implies a change in blood flow. In the literature, different approaches to estimate and model the haemodynamic response have been proposed. These approaches can be discriminated in model structures that either provide a proper representation of the obtained measurements but provide no or a limited amount of physiological information, or provide physiological insight but lacks a proper fit to the data. In this paper, a novel model structure is studied for describing the haemodynamics in fMRI measurements: fractional models. We show that these models are flexible enough to describe the gathered data with the additional merit of providing physiological information.
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| 5. |
- Bryan, Jim, et al.
(author)
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The local Gromov-Witten theory of curves
- 2008
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In: Journal of The American Mathematical Society. - 0894-0347 .- 1088-6834. ; 21:1, s. 101-136
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Journal article (peer-reviewed)
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| 7. |
- Dick, FD, et al.
(author)
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Gene-environment interactions in parkinsonism and Parkinson's disease : The Geoparkinson study
- 2007
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In: Occupational and Environmental Medicine. - : BMJ. - 1351-0711 .- 1470-7926. ; 64:10, s. 673-680
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Journal article (peer-reviewed)abstract
- Objectives: To investigate associations of Parkinson's disease (PD) and parkinsonian syndromes with polymorphic genes that influence metabolism of either foreign chemical substances or dopamine and to seek evidence of gene-environment interaction effects that modify risk. Methods: A case-control study of 959 prevalent cases of parkinsonism (767 with PD) and 1989 controls across five European centres. Occupational hygienists estimated the average annual intensity of exposure to solvents, pesticides and metals, (iron, copper, manganese), blind to disease status. CYP2D6, PON1, GSTM1, GSTT1, GSTM3, GSTP1, NQO1, CYP1B1, MAO-A, MAO-B, SOD 2, EPHX, DATl, DRD2 and NAT2 were genotyped. Results were analysed using multiple logistic regression adjusting for key confounders. Results: There was a modest but significant association between MAO-A polymorphism in males and disease risk (G vs T, OR 1.30, 95% C1 1.02 to 1.66, adjusted). The majority of gene-environment analyses did not show significant interaction effects. There were possible interaction effects between GSTM1 null genotype and solvent exposure (which were stronger when limited to PD cases only). Conclusions: Many small studies have reported associations between genetic polymorphisms and PD. Fewer have examined gene-environment interactions. This large study was sufficiently powered to examine these aspects. GSTM1 null subjects heavily exposed to solvents appear to be at increased risk of PD. There was insufficient evidence that the other gene-environment combinations investigated modified disease risk, suggesting they contribute little to the burden of PD.
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| 10. |
- Kobetski, Avenir, et al.
(author)
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Paving the Way for Apps in Vehicles
- 2013
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In: ERCIM News. - : ERCIM News. - 0926-4981 .- 1564-0094. ; :94
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Journal article (other academic/artistic)
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