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Items: 1 to 20 of 22

1.

Metabolic Profiling Reveals Differences in Plasma Concentrations of Arabinose and Xylose after Consumption of Fiber-Rich Pasta and Wheat Bread with Differential Rates of Systemic Appearance of Exogenous Glucose in Healthy Men.

Pantophlet AJ, Wopereis S, Eelderink C, Vonk RJ, Stroeve JH, Bijlsma S, van Stee L, Bobeldijk I, Priebe MG.

J Nutr. 2017 Feb;147(2):152-160. doi: 10.3945/jn.116.237404. Epub 2016 Dec 7.

PMID:
27927976
2.

Quantifying phenotypic flexibility as the response to a high-fat challenge test in different states of metabolic health.

Kardinaal AF, van Erk MJ, Dutman AE, Stroeve JH, van de Steeg E, Bijlsma S, Kooistra T, van Ommen B, Wopereis S.

FASEB J. 2015 Nov;29(11):4600-13. doi: 10.1096/fj.14-269852. Epub 2015 Jul 21.

3.

Toxicity assessment of aggregated/agglomerated cerium oxide nanoparticles in an in vitro 3D airway model: the influence of mucociliary clearance.

Frieke Kuper C, Gröllers-Mulderij M, Maarschalkerweerd T, Meulendijks NM, Reus A, van Acker F, Zondervan-van den Beuken EK, Wouters ME, Bijlsma S, Kooter IM.

Toxicol In Vitro. 2015 Mar;29(2):389-97. doi: 10.1016/j.tiv.2014.10.017. Epub 2014 Nov 4.

PMID:
25448805
4.

Raman spectroscopy as a promising tool for noninvasive point-of-care glucose monitoring.

Scholtes-Timmerman MJ, Bijlsma S, Fokkert MJ, Slingerland R, van Veen SJ.

J Diabetes Sci Technol. 2014 Sep;8(5):974-9. doi: 10.1177/1932296814543104. Epub 2014 Jul 18.

5.

Correlation network analysis reveals relationships between diet-induced changes in human gut microbiota and metabolic health.

Kelder T, Stroeve JH, Bijlsma S, Radonjic M, Roeselers G.

Nutr Diabetes. 2014 Jun 30;4:e122. doi: 10.1038/nutd.2014.18.

6.

Nutrigenomics approach elucidates health-promoting effects of high vegetable intake in lean and obese men.

Pasman WJ, van Erk MJ, Klöpping WA, Pellis L, Wopereis S, Bijlsma S, Hendriks HF, Kardinaal AF.

Genes Nutr. 2013 Sep;8(5):507-21. doi: 10.1007/s12263-013-0343-9. Epub 2013 Apr 18.

7.

Identification of biomarkers for intake of protein from meat, dairy products and grains: a controlled dietary intervention study.

Altorf-van der Kuil W, Brink EJ, Boetje M, Siebelink E, Bijlsma S, Engberink MF, van 't Veer P, Tomé D, Bakker SJ, van Baak MA, Geleijnse JM.

Br J Nutr. 2013 Sep 14;110(5):810-22. doi: 10.1017/S0007114512005788. Epub 2013 Mar 4.

PMID:
23452466
8.

Fish oil and inflammatory status alter the n-3 to n-6 balance of the endocannabinoid and oxylipin metabolomes in mouse plasma and tissues.

Balvers MG, Verhoeckx KC, Bijlsma S, Rubingh CM, Meijerink J, Wortelboer HM, Witkamp RF.

Metabolomics. 2012 Dec;8(6):1130-1147. Epub 2012 Apr 11.

9.

Time-dependent effect of in vivo inflammation on eicosanoid and endocannabinoid levels in plasma, liver, ileum and adipose tissue in C57BL/6 mice fed a fish-oil diet.

Balvers MG, Verhoeckx KC, Meijerink J, Bijlsma S, Rubingh CM, Wortelboer HM, Witkamp RF.

Int Immunopharmacol. 2012 Jun;13(2):204-14. doi: 10.1016/j.intimp.2012.03.022. Epub 2012 Apr 9.

PMID:
22498761
10.

Visualization and identification of health space, based on personalized molecular phenotype and treatment response to relevant underlying biological processes.

Bouwman J, Vogels JT, Wopereis S, Rubingh CM, Bijlsma S, Ommen Bv.

BMC Med Genomics. 2012 Jan 6;5:1. doi: 10.1186/1755-8794-5-1.

11.

Metabolomics as a tool for target identification in strain improvement: the influence of phenotype definition.

Braaksma M, Bijlsma S, Coulier L, Punt PJ, van der Werf MJ.

Microbiology. 2011 Jan;157(Pt 1):147-59. doi: 10.1099/mic.0.041244-0. Epub 2010 Sep 16.

PMID:
20847006
12.

Dynamic metabolomic data analysis: a tutorial review.

Smilde AK, Westerhuis JA, Hoefsloot HC, Bijlsma S, Rubingh CM, Vis DJ, Jellema RH, Pijl H, Roelfsema F, van der Greef J.

Metabolomics. 2010 Mar;6(1):3-17. Epub 2009 Dec 4.

13.

Analyzing longitudinal microbial metabolomics data.

Rubingh CM, Bijlsma S, Jellema RH, Overkamp KM, van der Werf MJ, Smilde AK.

J Proteome Res. 2009 Sep;8(9):4319-27. doi: 10.1021/pr900126e.

PMID:
19624157
14.

Matrix correlations for high-dimensional data: the modified RV-coefficient.

Smilde AK, Kiers HA, Bijlsma S, Rubingh CM, van Erk MJ.

Bioinformatics. 2009 Feb 1;25(3):401-5. doi: 10.1093/bioinformatics/btn634. Epub 2008 Dec 10.

PMID:
19073588
15.

Large-scale human metabolomics studies: a strategy for data (pre-) processing and validation.

Bijlsma S, Bobeldijk I, Verheij ER, Ramaker R, Kochhar S, Macdonald IA, van Ommen B, Smilde AK.

Anal Chem. 2006 Jan 15;78(2):567-74.

PMID:
16408941
16.

Assessing the performance of statistical validation tools for megavariate metabolomics data.

Rubingh CM, Bijlsma S, Derks EP, Bobeldijk I, Verheij ER, Kochhar S, Smilde AK.

Metabolomics. 2006;2(2):53-61. Epub 2006 Jul 11.

17.
18.

Fusion of mass spectrometry-based metabolomics data.

Smilde AK, van der Werf MJ, Bijlsma S, van der Werff-van der Vat BJ, Jellema RH.

Anal Chem. 2005 Oct 15;77(20):6729-36.

PMID:
16223263
19.

Quantitative structure activity relationship studies on the flavonoid mediated inhibition of multidrug resistance proteins 1 and 2.

van Zanden JJ, Wortelboer HM, Bijlsma S, Punt A, Usta M, Bladeren PJ, Rietjens IM, Cnubben NH.

Biochem Pharmacol. 2005 Feb 15;69(4):699-708. Epub 2004 Dec 23.

PMID:
15670588
20.

Characterization of anti-inflammatory compounds using transcriptomics, proteomics, and metabolomics in combination with multivariate data analysis.

Verhoeckx KC, Bijlsma S, Jespersen S, Ramaker R, Verheij ER, Witkamp RF, van der Greef J, Rodenburg RJ.

Int Immunopharmacol. 2004 Nov;4(12):1499-514.

PMID:
15351319

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