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118 results on '"Bijlsma, S."'

1. A novel personalized systems nutrition program improves dietary patterns, lifestyle behaviors and health-related outcomes

Author

Hoogh, I.M. de, Winters, B.L., Nieman, K.M., Bijlsma, S., Krone, T., Broek, T.J. van den, Anderson, B.D., Caspers, M.P.M., Anthony, J.C., and Wopereis, S.

Abstract

Personalized nutrition may be more effective in changing lifestyle behaviors compared to population-based guidelines. This single-arm exploratory study evaluated the impact of a 10-week personalized systems nutrition (PSN) program on lifestyle behavior and health outcomes. Healthy men and women (n = 82) completed the trial. Individuals were grouped into seven diet types, for which phenotypic, genotypic and behavioral data were used to generate personalized recommendations. Behavior change guidance was also provided. The intervention reduced the intake of calories (−256.2 kcal; p < 0.0001), carbohydrates (−22.1 g; p < 0.0039), sugar (−13.0 g; p < 0.0001), total fat (−17.3 g; p < 0.0001), saturated fat (−5.9 g; p = 0.0003) and PUFA (−2.5 g; p = 0.0065). Additionally, BMI (−0.6 kg/m2; p < 0.0001), body fat (−1.2%; p = 0.0192) and hip circumference (−5.8 cm; p < 0.0001) were decreased after the intervention. In the subgroup with the lowest phenotypic flexibility, a measure of the body’s ability to adapt to environmental stressors, LDL (−0.44 mmol/L; p = 0.002) and total cholesterol (−0.49 mmol/L; p < 0.0001) were reduced after the intervention. This study shows that a PSN program in a workforce improves lifestyle habits and reduces body weight, BMI and other health-related outcomes. Health improvement was most pronounced in the compromised phenotypic flexibility subgroup, which indicates that a PSN program may be effective in targeting behavior change in health-compromised target groups.

Published
2022

3. Lipidome Analysis in Brain and Peripheral Plasma Following Milk Fat Globule Membrane Supplementation in Rodents

Author

Davies, R., Diepen, J.A. van, Brink, L.R., Bijlsma, S., Neufeld, K.M., Cryan, J.F., O'Mahony, S.M., Bobeldijk, I., Gross, G., Davies, R., Diepen, J.A. van, Brink, L.R., Bijlsma, S., Neufeld, K.M., Cryan, J.F., O'Mahony, S.M., Bobeldijk, I., and Gross, G.

Abstract

Item does not contain fulltext, SCOPE: Milk fat globule membrane (MFGM) is an essential component of milk. Bovine MFGM (bMFGM) has been shown to support cognitive development and increase relative concentrations of serum phospholipids. This study investigates bioavailability of bMFGM components after oral administration in two preclinical models to explore whether dietary bMFGM induces parallel changes to plasma and brain lipidomes. METHODS AND RESULTS: Transgenic APOE*3.Leiden mice (n = 18 per group) and Sprague-Dawley rats (n = 12 per group) are fed bMFGM-enriched (MFGM+) or Control diet, followed by phospholipid profile-determination in plasma, hippocampus, and prefrontal cortex tissue by targeted mass spectrometry. Multivariate analysis of lipidomic profiles demonstrates a separation between MFGM+ and Control plasma across rodents. In plasma, sphingomyelins contributed the most to the separation of lipid patterns among both models, where three sphingomyelins (d18:1/14:0, d18:1/23:0, d18:1/23:1[9Z]) are consistently higher in the circulation of MFGM+ groups. A similar trend is observed in rat prefrontal cortex, although no significant separation of the brain lipidome is demonstrated. CONCLUSION: bMFGM-enriched diet alters plasma phospholipid composition in rodents, predominantly increasing sphingomyelin levels in the systemic circulation with similar, but non-significant, trends in central brain regions. These changes may contribute to the beneficial effects of bMFGM on neurodevelopment during early life.

Published
2022

6. Nutrigenomics: application of genomics technologies in nutritional sciences and food technology

Author

van der Werf, M.J., Schuren, F.H.J., Bijlsma, S., Tas, A.C., and van Ommen, B.

Subjects
Food -- Biotechnology, Nutrition -- Research, Business, Food/cooking/nutrition
Abstract

Research is presented concerning the use of applied genomics for the holistic investigation of keystone biomolecules. The scientific methodology relating to proteomics, transcriptomics and metabolomics is discussed.

Published
2001

8. The possibilities of the use of N-of-1 and do-ityourself trials in nutritional research

Author

Krone, T., Boessen, R., Bijlsma, S., Stokkum, R. van, Clabbers, N.D.S., and Pasman, W.J.

Abstract

Background. N-of-1 designs gain popularity in nutritional research because of the improving technological possibilities, practical applicability and promise of increased accuracy and sensitivity, especially in the field of personalized nutrition. This move asks for a search of applicable statistical methods. Objective. To demonstrate the differences of three popular statistical methods in analyzing treatment effects of data obtained in N-of-1 designs. Method. We compare Individual-participant data meta-analysis, frequentist and Bayesian linear mixed effect models using a simulation experiment. Furthermore, we demonstrate the merits of the Bayesian model including prior information by analyzing data of an empirical study on weight loss. Results. The linear mixed effect models are to be preferred over the meta-analysis method, since the individual effects are estimated more accurately as evidenced by the lower errors, especially with lower sample sizes. Differences between Bayesian and frequentist mixed models were found to be small, indicating that they will lead to the same results without including an informative prior. Conclusion. For empirical data, the Bayesian mixed model allows the inclusion of prior knowledge and gives potential for population based and personalized inference.

Published
2020

11. Abstracts from the Food Allergy and Anaphylaxis Meeting 2016

Author

Pouessel, G, Claverie, C, Labreuche, J, Renaudin, J-M, Dorkenoo, A, Eb, M, Moneret-Vautrin, A, Deschildre, A, Leteurtre, S, Grabenhenrich, L, Worm, M, Dölle, S, Scherer, K, Hutteger, I, Christensen, M, Bindslev-Jensen, C, Mortz, C, Eller, E, Kjaer, HF, Carneiro-Leão, L, Badas, J, Coimbra, A, Levy, DP, Ben-Shoshan, M, Rimon, A, Benor, S, Arends, NJT, Edelbroek, N, de Groot, H, Emons, JAM, Brand, HKA, Verhoeven, D, van Veen, LN, de Jong, NW, Noh, G, Jang, EH, Pascal, M, Dominguez, O, Piquer, M, Alvaro, M, Jimenez-Feijoo, R, Lozano, J, Machinena, A, del Mar Folqué, M, Giner, MT, Plaza, AM, Turner, P, Patel, N, Vazquez-Ortiz, M, Lindsley, S, Walker, L, Rosenberg, S, Mari, A, Alessandri, C, Giangrieco, I, Tuppo, L, Rafaiani, C, Mitterer, G, Ciancamerla, M, Ferrara, R, Bernardi, ML, Zennaro, D, Tamburrini, M, Ciardiello, MA, Harwanegg, C, Fernandez, A, Selb, R, Egenmann, P, Epstein, M, Hoffmann-Sommergruber, K, Koning, F, Lovik, M, Clare Mills, EN, Moreno, J, van Loveren, H, Wal, J-M, Diesner, S, Bergmayr, C, Pfitzner, B, Assmann, VE, Starkl, P, Endesfelder, D, Eiwegger, T, Szepfalusi, Z, Fehrenbach, H, Jensen-Jarolim, E, Hartmann, A, Pali-Schöll, I, Untersmayr, E, Wille, S, Meyer, P, Klingebiel, C, Lidholm, J, Ehrenberg, A, Östling, J, Cleach, I, Mège, J-L, Vitte, J, Aina, R, Dubiela, P, Pfeifer, S, Bublin, M, Radauer, C, Humeniuk, P, Kabasser, S, Asero, R, Bogas, G, Gomez, F, Campo, P, Salas, M, Doña, I, Barrionuevo, E, Guerrero, MA, Mayorga, C, Prieto, A, Barber, D, Torres, MJ, Jamin, A, Wangorsch, A, Ballmer, B, Vieths, S, Scheurer, S, Apostolovic, D, Mihailovic, J, Krstic, M, Starkhammar, M, Velickovic, TC, Hamsten, C, van Hage, M, van Erp, FC, Knol, EF, Kansen, HM, Pontoppidan, B, Meijer, Y, van der Ent, CK, Knulst, AC, Sayers, R, Brown, H, Custovic, A, Simpson, A, Mills, C, Schulz, J, Akkerdaas, J, Totis, M, Capt, A, Herouet-Guicheney, C, van Ree, R, Banerjee, T, Banerjee, A, Claude, M, Bouchaud, G, Lupi, R, Castan, L, Tranquet, O, Denery-Papini, S, Bodinier, M, Brossard, C, De Poi, R, Gritti, E, De Dominicis, E, Popping, B, de Laureto, PP, Palosuo, K, Kukkonen, AK, Pelkonen, A, Mäkelä, M, Lee, NA, Rost, J, Muralidharan, S, Campbell, D, Mehr, S, Nock, C, Baumert, J, Taylor, S, Mastrorilli, C, Tripodi, S, Caffarelli, C, Perna, S, Di Rienzo Businco, A, Sfika, I, Dondi, A, Bianchi, A, Dascola, CP, Ricci, G, Cipriani, F, Maiello, N, del Giudice, MM, Frediani, T, Frediani, S, Macrì, F, Pistoletti, C, Iacono, ID, Patria, MF, Varin, E, Peroni, D, Comberiati, P, Chini, L, Moschese, V, Lucarelli, S, Bernardini, R, Pingitore, G, Pelosi, U, Olcese, R, Moretti, M, Cirisano, A, Faggian, D, Travaglini, A, Plebani, M, Verga, MC, Calvani, M, Giordani, P, Matricardi, PM, Ontiveros, N, Cabrera-Chavez, F, Galand, J, Beaudouin, E, Pineau, F, Sakai, S, Matsunaga, K, Teshima, R, Larré, C, Denery, S, Tschirner, S, Trendelenburg, V, Schulz, G, Niggemann, B, Beyer, K, Bouferkas, Y, Belabbas, Y, Saidi, D, Kheroua, O, Mecherfi, KEE, Guendouz, M, Haddi, A, Kaddouri, H, Amaral, L, Pereira, A, Rodrigues, S, Datema, M, Jongejan, L, Clausen, M, Knulst, A, Papadopoulos, N, Kowalski, M, de Blay, F, Zwinderman, A, Hoffman-Sommergruber, K, Ballmer-Weber, B, Fernandez-Rivas, M, Deng, S, Yin, J, Eisenmann, C, Nassiri, M, Reinert, R, van der Valk, JPM, van Wijk, RG, Vergouwe, Y, Steyerberg, EW, Reitsma, M, Wichers, HJ, Savelkoul, HFJ, Vlieg-Boerstra, B, Dubois, AEJ, Carolino, F, Rodolfo, A, Cernadas, J, Roa-Medellín, D, Rodriguez-Fernandez, A, Navarro, J, Albendiz, V, Baeza, ML, Intente-Herrero, S, Mikkelsen, A, Mehlig, K, Lissner, L, Verrill, L, Luccioli, S, van Bilsen, J, Kuper, F, Wolterbeek, A, Rankouhi, TR, Verschuren, L, Cnossen, H, Jeurink, P, Garssen, J, Knippels, L, Garthoff, J, Houben, G, Leeman, W, Eleonore Pettersson, M, Schins, AMM, Koppelman, GH, Kollen, BJ, Zubchenko, S, Kuntz, S, Mérida, P, Álvaro, M, Riggioni, C, Castellanos, JH, Jimenez, R, Cap, M, Drumez, E, Lejeune, S, Thumerelle, C, Mordacq, C, Nève, V, Ricò, S, Varini, M, Nocerino, R, Cosenza, L, Amoroso, A, Di Costanzo, M, Di Scala, C, Bedogni, G, Canani, RB, Turner, PJ, Poza-Guedes, P, González-Pérez, R, Sánchez-Machín, I, Matheu-Delgado, V, Wambre, E, Ballegaard, A-S, Madsen, C, Gregersen, J, Bøgh, KL, Aubert, P, Neunlist, M, Magnan, A, Lozano-Ojalvo, D, Pablos-Tanarro, A, Pérez-Rodríguez, L, Molina, E, López-Fandiño, R, Rekima, A, Macchiaverni, P, Turfkruyer, M, Holvoet, S, Dupuis, L, Baiz, N, Annesi-Maesano, I, Mercenier, A, Nutten, S, Verhasselt, V, Mrakovcic-Sutic, I, Banac, S, Sutic, I, Baricev-Novakovic, Z, Pavisic, V, Muñoz-Cano, R, Jiménez-Rodríguez, T, Corbacho, D, Roca-Ferrer, J, Bartra, J, Bulog, A, Micovic, V, Markiewicz, L, Szymkiewicz, A, Szyc, A, Wróblewska, B, Harvey, BM, Harthoorn, LF, Wesley Burks, A, Rentzos, G, Björk, A-LB, Bengtsson, U, Barber, C, Kalicinsky, C, Breynaert, C, Coorevits, L, Jansen, C, Van Hoeyveld, E, Verbeke, K, Kochuyt, A-M, Schrijvers, R, Deleanu, D, Muntean, A, Konstantakopoulou, M, Pasioti, M, Papadopoulou, A, Iliopoulou, A, Mikos, N, Kompoti, E, de Castro, ED, Bartalomé, B, Ue, KL, Griffiths, E, Till, S, Grimshaw, K, Roberts, G, Selby, A, Butiene, I, Larco, JI, Dubakiene, R, Fiandor, A, Fiocchi, A, Sigurdardottir, S, Sprikkelman, A, Schoemaker, A-F, Xepapadaki, P, Keil, T, Cojocariu, Z, Barbado, BS, Iancu, V, Arroabarren, E, Esarte, MG, Arteaga, M, Andrade, MC, Borges, D, Kalil, J, Bianchi, PG, Agondi, RC, Gupta, RK, Sharma, A, Gupta, K, Das, M, Dwivedi, P, Karseladze, R, Jorjoliani, L, Saginadze, L, Tskhakaia, M, Basello, K, Piuri, G, Speciani, AF, Speciani, MC, Camerotto, C, Zinno, F, Pakholchuk, O, Nedelska, S, Pattini, S, Costantino, MT, Peveri, S, Villalta, D, Savi, E, Costanzi, A, Revyakina, VA, Kiseleva, MA, Kuvshinova, ED, Larkova, IA, Shekhetov, AA, Silva, D, Moreira, A, Plácido, J, van der Kleij, H, van Twuijver, E, Sutorius, R, de Kam, P-J, van Odijk, J, Lindqvist, H, Lustig, E, Jácome, AAA, Aguilar, KLB, Domínguez, MG, Hernández, DAM, Caruso, C, Casale, C, Rapaccini, GL, Romano, A, De Vitis, I, Cocco, RR, Aranda, C, Mallozi, MC, Motta, JF, Moraes, L, Pastorino, A, Rosario, N, Goudouris, E, Porto, A, Wandalsen, NF, Sarinho, E, Sano, F, Solé, D, Pitsios, C, Petrodimopoulou, M, Papadopoulou, E, Passioti, M, Kontogianni, M, Adamia, N, Khaleva, E, del Prado, AP, Du Toit, G, Krzych, E, Samolinska-Zawisza, U, Furmanczyk, K, Tomaszewska, A, Raciborski, F, Lipiec, A, Samel-Kowalik, P, Walkiewicz, A, Borowicz, J, Samolinski, B, Nano, AL, Recto, M, Somoza, ML, López, NB, Alzate, DP, Ruano, FJ, Garcimartín, MI, Haroun, E, de la Torre, MV, Rojas, A, Onieva, ML, Canto, G, Rodrigues, A, Forno, A, Cabral, AJ, Gonçalves, R, Vorozhko, I, Sentsova, T, Chernyak, O, Denisova, S, Ilènko, L, Muhortnich, V, Zimmermann, C, Rohrbach, A, Bakhsh, FR, Boudewijn, K, Oomkes-Pilon, A-M, Van Ginkle, D, Šilar, M, Jeverica, A, Vesel, T, Avčin, T, Korošec, P, van der Valk, J, Berends, I, Arends, N, van Maaren, M, Wichers, H, Emons, J, Dubois, A, de Jong, N, Matsyura, O, Besh, L, Huang, C-H, Jan, T-R, Stiefel, G, Tratt, J, Kirk, K, Arasi, S, Caminiti, L, Crisafulli, G, Fiamingo, C, Fresta, J, Pajno, G, Remington, B, Kruizinga, A, Marty Blom, W, Westerhout, J, Bijlsma, S, Blankestijn, M, Otten, H, Klemans, R, Michelsen-Huisman, AD, van Os-Medendorp, H, Kruizinga, AG, Versluis, A, van Duijn, G, de Zeeuw-Brouwer, HM-L, Castenmiller, JJM, Noteborn, HPJM, Houben, GF, Bravin, K, Luyt, D, Javed, B, Couch, P, Munro, C, Padfield, P, Sperrin, M, Byrne, A, Oosthuizen, L, Kelleher, C, Ward, F, Brosnan, N, King, G, Corbet, E, Guzmán, JAH, García, MB, Asensio, O, Navarrete, LV, Larramona, H, Miró, XD, Pyrz, K, Austin, M, Boloh, Y, Galloway, D, Hernandez, P, Hourihane, JOB, Kenna, F, Majkowska-Wojciechowska, B, Regent, L, Themisb, M, Schnadt, S, Semic-Jusufagic, A, Galvin, AD, Kauppila, T, Kuitunen, M, Kitsioulis, NA, Douladiris, N, Kostoudi, S, Manolaraki, I, Mitsias, D, Manousakis, E, Papadopoulos, NG, Knibb, R, Hammond, J, Cooke, R, Yrjänä, J, Hanni, A-M, Vähäsarja, P, Mustonen, O, Dunder, T, Kulmala, P, Lasa, E, D’Amelio, C, Martínez, S, Joral, A, Gastaminza, G, Goikoetxea, MJ, Candy, DCA, Van Ampting, MTJ, Oude Nijhuis, MM, Butt, AM, Peroni, DG, Fox, AT, Knol, J, Michaelis, LJ, Padua, I, Padrao, P, Moreira, P, Barros, R, Sharif, H, Ahmed, M, Gomaa, N, Mens, J, Smit, K, Timmermans, F, Poredoš, T, Jeverica, AK, Sedmak, M, Benedik, E, Accetto, M, Zupančič, M, Yonamine, G, Soldateli, G, Aquilante, B, Pastorino, AC, de Moraes Beck, CL, Gushken, AK, de Barros Dorna, M, dos Santos, CN, Castro, APM, Al-Qahtani, A, Arnaout, R, Khaliq, AR, Amin, R, Sheikh, F, Alvarez, J, Anda, M, Palacios, M, De Prada, M, Ponce, C, Balbino, B, Sibilano, R, Marichal, T, Gaudenzio, N, Karasuyama, H, Bruhns, P, Tsai, M, Reber, LL, Galli, SJ, Ferreira, AR, Cernadas, JR, del Campo García, A, Fernández, SP, Carrera, NS, Sánchez-Cruz, FB, Lorenzo, JRF, Claus, S, Pföhler, C, Ruëff, F, Treudler, R, Jaume, ME, Madroñero, A, Perez, MTG, Julia, JC, Plovdiv, CH, Gethings, L, Langridge, J, Adel-Patient, K, Bernard, H, Barcievic-Jones, I, Sokolova, R, Yankova, R, Ivanovska, M, Murdjeva, M, Popova, T, Dermendzhiev, S, Karjalainen, M, Lehnigk, U, Brown, D, Locklear, JC, Locklear, J, Maris, I, Hourihane, J, Ornelas, C, Caiado, J, Ferreira, MB, Pereira-Barbosa, M, Puente, Y, Daza, JC, Monteseirin, FJ, Ukleja-Sokolowska, N, Gawronska-Ukleja, E, Zbikowska-Gotz, M, Bartuzi, Z, Sokolowski, L, Adams, A, Mahon, B, English, K, Gourdon-Dubois, N, Sellam, L, Pereira, B, Michaud, E, Messaoudi, K, Evrard, B, Fauquert, J-L, Palomares, F, Gomez, G, Rodriguez, MJ, Galindo, L, Molina, A, Paparo, L, Mennini, M, Aitoro, R, Wawrzeńczyk, A, Przybyszewski, M, Sarıcoban, HE, Ugras, M, Yalvac, Z, Flokstra-de Blok, BMJ, van der Velde, JL, Vereda, A, Ippolito, C, Traversa, A, Adriano, D, Bianchi, DM, Gallina, S, Decastelli, L, Makatsori, M, Miles, A, Devetak, SP, Devetak, I, Tabet, SA, Trandbohus, JF, Winther, P, Malling, H-J, Hansen, KS, Garvey, LH, Wang, C-C, Cheng, Y-H, Tung, C-W, Dietrich, M, Marenholz, I, Kalb, B, Grosche, S, Blümchen, K, Schlags, R, Price, M, Rietz, S, Esparza-Gordillo, J, Lau, S, Lee, Y-A, Almontasheri, A, Bahkali, MA, Elshorbagi, S, Alfhaid, A, Altamimi, M, Madbouly, E, Al-Dhekri, H, Arnaout, RK, Basagaña, M, Miquel, S, Bartolomé, B, Brix, B, Rohwer, S, Brandhoff, S, Berger, A, Suer, W, Weimann, A, Bueno, C, Martín-Pedraza, L, Abián, S, Segundo-Acosta, PS, López-Rodríguez, JC, Barderas, R, Batanero, E, Cuesta-Herranz, J, Villalba, MT, Correia, M, Benito-Garcia, F, Arêde, C, Piedade, S, Morais-Almeida, M, Hindley, J, Yarham, R, Kuklinska-Pijanka, A, Gillick, D, Patient, K, Chapman, MD, Miranda, A, Matos, E, Sokolova, A, Rao, H, Baricevic-Jones, I, Smith, F, Xue, W, Magnusdottir, H, Vidarsdottir, AG, Lund, S, Jensen, AB, Ludviksson, BR, Simon, R, Elfont, R, Bennett, S, Voyksner, R, de Lurdes Torre, M, Yürek, S, Faber, MA, Bastiaensen, A, Mangodt, E, van Gasse, A, Decuyper, I, Sabato, V, Hagendorens, MM, Bridts, CH, De Clerck, LS, Ebo, D, Schwarz, S, Ziegert, M, Albroscheit, S, Schwager, C, Kull, S, Behrends, J, Röckendorf, N, Schocker, F, Frey, A, Homann, A, Becker, W-M, Jappe, U, Zaabat, N, Osscini, S, Agabriel, C, Sterling, B, Carsin, A, Liabeuf, V, Maćków, M, Zbróg, A, Bronkowska, M, Courtois, J, Gadisseur, R, Bertholet, C, Lukas, P, Cavalier, E, Delahaut, P, Quinting, B, Gertmo, MB, Hasseus, ET, Barzylovych, V, Oliveira, J, Ensina, LF, Aranda, CS, Dopazo, L, Lopez, R, Perez, R, Santos-Diez, L, Bilbao, A, Garcia, JM, Núñez, IG, Mármol, MÁA, Villarejo, MJB, Martos, JAB, Vergara, MS, García, JMI, Michalska, A, Sergiejko, G, Zacniewski, R, Ghiordanescu, I-M, Deaconu, C, Popescu, M, Bumbacea, RS, Ibranji, A, Nikolla, E, Loloci, G, Juel-Berg, N, Larsen, LF, Poulsen, LK, Marcelino, J, Prata, R, Costa, AC, Duarte, F, Neto, M, Santos, J, Pestana, LC, Sampaio, D, Minale, P, Dignetti, P, Bignardi, D, Nedelea, I, Popescu, F-D, Vieru, M, Secureanu, F-A, Ganea, CS, Vieira, M, Silva, JPM, Watts, T, Watts, S, Lomikovska, M, Peredelskaya, M, Nenasheva, N, Filipovic, I, Zivkovic, Z, Filipovic, D, Higgs, J, Warner, A, Jones, C, Pouessel, G, Claverie, C, Labreuche, J, Renaudin, J-M, Dorkenoo, A, Eb, M, Moneret-Vautrin, A, Deschildre, A, Leteurtre, S, Grabenhenrich, L, Worm, M, Dölle, S, Scherer, K, Hutteger, I, Christensen, M, Bindslev-Jensen, C, Mortz, C, Eller, E, Kjaer, HF, Carneiro-Leão, L, Badas, J, Coimbra, A, Levy, DP, Ben-Shoshan, M, Rimon, A, Benor, S, Arends, NJT, Edelbroek, N, de Groot, H, Emons, JAM, Brand, HKA, Verhoeven, D, van Veen, LN, de Jong, NW, Noh, G, Jang, EH, Pascal, M, Dominguez, O, Piquer, M, Alvaro, M, Jimenez-Feijoo, R, Lozano, J, Machinena, A, del Mar Folqué, M, Giner, MT, Plaza, AM, Turner, P, Patel, N, Vazquez-Ortiz, M, Lindsley, S, Walker, L, Rosenberg, S, Mari, A, Alessandri, C, Giangrieco, I, Tuppo, L, Rafaiani, C, Mitterer, G, Ciancamerla, M, Ferrara, R, Bernardi, ML, Zennaro, D, Tamburrini, M, Ciardiello, MA, Harwanegg, C, Fernandez, A, Selb, R, Egenmann, P, Epstein, M, Hoffmann-Sommergruber, K, Koning, F, Lovik, M, Clare Mills, EN, Moreno, J, van Loveren, H, Wal, J-M, Diesner, S, Bergmayr, C, Pfitzner, B, Assmann, VE, Starkl, P, Endesfelder, D, Eiwegger, T, Szepfalusi, Z, Fehrenbach, H, Jensen-Jarolim, E, Hartmann, A, Pali-Schöll, I, Untersmayr, E, Wille, S, Meyer, P, Klingebiel, C, Lidholm, J, Ehrenberg, A, Östling, J, Cleach, I, Mège, J-L, Vitte, J, Aina, R, Dubiela, P, Pfeifer, S, Bublin, M, Radauer, C, Humeniuk, P, Kabasser, S, Asero, R, Bogas, G, Gomez, F, Campo, P, Salas, M, Doña, I, Barrionuevo, E, Guerrero, MA, Mayorga, C, Prieto, A, Barber, D, Torres, MJ, Jamin, A, Wangorsch, A, Ballmer, B, Vieths, S, Scheurer, S, Apostolovic, D, Mihailovic, J, Krstic, M, Starkhammar, M, Velickovic, TC, Hamsten, C, van Hage, M, van Erp, FC, Knol, EF, Kansen, HM, Pontoppidan, B, Meijer, Y, van der Ent, CK, Knulst, AC, Sayers, R, Brown, H, Custovic, A, Simpson, A, Mills, C, Schulz, J, Akkerdaas, J, Totis, M, Capt, A, Herouet-Guicheney, C, van Ree, R, Banerjee, T, Banerjee, A, Claude, M, Bouchaud, G, Lupi, R, Castan, L, Tranquet, O, Denery-Papini, S, Bodinier, M, Brossard, C, De Poi, R, Gritti, E, De Dominicis, E, Popping, B, de Laureto, PP, Palosuo, K, Kukkonen, AK, Pelkonen, A, Mäkelä, M, Lee, NA, Rost, J, Muralidharan, S, Campbell, D, Mehr, S, Nock, C, Baumert, J, Taylor, S, Mastrorilli, C, Tripodi, S, Caffarelli, C, Perna, S, Di Rienzo Businco, A, Sfika, I, Dondi, A, Bianchi, A, Dascola, CP, Ricci, G, Cipriani, F, Maiello, N, del Giudice, MM, Frediani, T, Frediani, S, Macrì, F, Pistoletti, C, Iacono, ID, Patria, MF, Varin, E, Peroni, D, Comberiati, P, Chini, L, Moschese, V, Lucarelli, S, Bernardini, R, Pingitore, G, Pelosi, U, Olcese, R, Moretti, M, Cirisano, A, Faggian, D, Travaglini, A, Plebani, M, Verga, MC, Calvani, M, Giordani, P, Matricardi, PM, Ontiveros, N, Cabrera-Chavez, F, Galand, J, Beaudouin, E, Pineau, F, Sakai, S, Matsunaga, K, Teshima, R, Larré, C, Denery, S, Tschirner, S, Trendelenburg, V, Schulz, G, Niggemann, B, Beyer, K, Bouferkas, Y, Belabbas, Y, Saidi, D, Kheroua, O, Mecherfi, KEE, Guendouz, M, Haddi, A, Kaddouri, H, Amaral, L, Pereira, A, Rodrigues, S, Datema, M, Jongejan, L, Clausen, M, Knulst, A, Papadopoulos, N, Kowalski, M, de Blay, F, Zwinderman, A, Hoffman-Sommergruber, K, Ballmer-Weber, B, Fernandez-Rivas, M, Deng, S, Yin, J, Eisenmann, C, Nassiri, M, Reinert, R, van der Valk, JPM, van Wijk, RG, Vergouwe, Y, Steyerberg, EW, Reitsma, M, Wichers, HJ, Savelkoul, HFJ, Vlieg-Boerstra, B, Dubois, AEJ, Carolino, F, Rodolfo, A, Cernadas, J, Roa-Medellín, D, Rodriguez-Fernandez, A, Navarro, J, Albendiz, V, Baeza, ML, Intente-Herrero, S, Mikkelsen, A, Mehlig, K, Lissner, L, Verrill, L, Luccioli, S, van Bilsen, J, Kuper, F, Wolterbeek, A, Rankouhi, TR, Verschuren, L, Cnossen, H, Jeurink, P, Garssen, J, Knippels, L, Garthoff, J, Houben, G, Leeman, W, Eleonore Pettersson, M, Schins, AMM, Koppelman, GH, Kollen, BJ, Zubchenko, S, Kuntz, S, Mérida, P, Álvaro, M, Riggioni, C, Castellanos, JH, Jimenez, R, Cap, M, Drumez, E, Lejeune, S, Thumerelle, C, Mordacq, C, Nève, V, Ricò, S, Varini, M, Nocerino, R, Cosenza, L, Amoroso, A, Di Costanzo, M, Di Scala, C, Bedogni, G, Canani, RB, Turner, PJ, Poza-Guedes, P, González-Pérez, R, Sánchez-Machín, I, Matheu-Delgado, V, Wambre, E, Ballegaard, A-S, Madsen, C, Gregersen, J, Bøgh, KL, Aubert, P, Neunlist, M, Magnan, A, Lozano-Ojalvo, D, Pablos-Tanarro, A, Pérez-Rodríguez, L, Molina, E, López-Fandiño, R, Rekima, A, Macchiaverni, P, Turfkruyer, M, Holvoet, S, Dupuis, L, Baiz, N, Annesi-Maesano, I, Mercenier, A, Nutten, S, Verhasselt, V, Mrakovcic-Sutic, I, Banac, S, Sutic, I, Baricev-Novakovic, Z, Pavisic, V, Muñoz-Cano, R, Jiménez-Rodríguez, T, Corbacho, D, Roca-Ferrer, J, Bartra, J, Bulog, A, Micovic, V, Markiewicz, L, Szymkiewicz, A, Szyc, A, Wróblewska, B, Harvey, BM, Harthoorn, LF, Wesley Burks, A, Rentzos, G, Björk, A-LB, Bengtsson, U, Barber, C, Kalicinsky, C, Breynaert, C, Coorevits, L, Jansen, C, Van Hoeyveld, E, Verbeke, K, Kochuyt, A-M, Schrijvers, R, Deleanu, D, Muntean, A, Konstantakopoulou, M, Pasioti, M, Papadopoulou, A, Iliopoulou, A, Mikos, N, Kompoti, E, de Castro, ED, Bartalomé, B, Ue, KL, Griffiths, E, Till, S, Grimshaw, K, Roberts, G, Selby, A, Butiene, I, Larco, JI, Dubakiene, R, Fiandor, A, Fiocchi, A, Sigurdardottir, S, Sprikkelman, A, Schoemaker, A-F, Xepapadaki, P, Keil, T, Cojocariu, Z, Barbado, BS, Iancu, V, Arroabarren, E, Esarte, MG, Arteaga, M, Andrade, MC, Borges, D, Kalil, J, Bianchi, PG, Agondi, RC, Gupta, RK, Sharma, A, Gupta, K, Das, M, Dwivedi, P, Karseladze, R, Jorjoliani, L, Saginadze, L, Tskhakaia, M, Basello, K, Piuri, G, Speciani, AF, Speciani, MC, Camerotto, C, Zinno, F, Pakholchuk, O, Nedelska, S, Pattini, S, Costantino, MT, Peveri, S, Villalta, D, Savi, E, Costanzi, A, Revyakina, VA, Kiseleva, MA, Kuvshinova, ED, Larkova, IA, Shekhetov, AA, Silva, D, Moreira, A, Plácido, J, van der Kleij, H, van Twuijver, E, Sutorius, R, de Kam, P-J, van Odijk, J, Lindqvist, H, Lustig, E, Jácome, AAA, Aguilar, KLB, Domínguez, MG, Hernández, DAM, Caruso, C, Casale, C, Rapaccini, GL, Romano, A, De Vitis, I, Cocco, RR, Aranda, C, Mallozi, MC, Motta, JF, Moraes, L, Pastorino, A, Rosario, N, Goudouris, E, Porto, A, Wandalsen, NF, Sarinho, E, Sano, F, Solé, D, Pitsios, C, Petrodimopoulou, M, Papadopoulou, E, Passioti, M, Kontogianni, M, Adamia, N, Khaleva, E, del Prado, AP, Du Toit, G, Krzych, E, Samolinska-Zawisza, U, Furmanczyk, K, Tomaszewska, A, Raciborski, F, Lipiec, A, Samel-Kowalik, P, Walkiewicz, A, Borowicz, J, Samolinski, B, Nano, AL, Recto, M, Somoza, ML, López, NB, Alzate, DP, Ruano, FJ, Garcimartín, MI, Haroun, E, de la Torre, MV, Rojas, A, Onieva, ML, Canto, G, Rodrigues, A, Forno, A, Cabral, AJ, Gonçalves, R, Vorozhko, I, Sentsova, T, Chernyak, O, Denisova, S, Ilènko, L, Muhortnich, V, Zimmermann, C, Rohrbach, A, Bakhsh, FR, Boudewijn, K, Oomkes-Pilon, A-M, Van Ginkle, D, Šilar, M, Jeverica, A, Vesel, T, Avčin, T, Korošec, P, van der Valk, J, Berends, I, Arends, N, van Maaren, M, Wichers, H, Emons, J, Dubois, A, de Jong, N, Matsyura, O, Besh, L, Huang, C-H, Jan, T-R, Stiefel, G, Tratt, J, Kirk, K, Arasi, S, Caminiti, L, Crisafulli, G, Fiamingo, C, Fresta, J, Pajno, G, Remington, B, Kruizinga, A, Marty Blom, W, Westerhout, J, Bijlsma, S, Blankestijn, M, Otten, H, Klemans, R, Michelsen-Huisman, AD, van Os-Medendorp, H, Kruizinga, AG, Versluis, A, van Duijn, G, de Zeeuw-Brouwer, HM-L, Castenmiller, JJM, Noteborn, HPJM, Houben, GF, Bravin, K, Luyt, D, Javed, B, Couch, P, Munro, C, Padfield, P, Sperrin, M, Byrne, A, Oosthuizen, L, Kelleher, C, Ward, F, Brosnan, N, King, G, Corbet, E, Guzmán, JAH, García, MB, Asensio, O, Navarrete, LV, Larramona, H, Miró, XD, Pyrz, K, Austin, M, Boloh, Y, Galloway, D, Hernandez, P, Hourihane, JOB, Kenna, F, Majkowska-Wojciechowska, B, Regent, L, Themisb, M, Schnadt, S, Semic-Jusufagic, A, Galvin, AD, Kauppila, T, Kuitunen, M, Kitsioulis, NA, Douladiris, N, Kostoudi, S, Manolaraki, I, Mitsias, D, Manousakis, E, Papadopoulos, NG, Knibb, R, Hammond, J, Cooke, R, Yrjänä, J, Hanni, A-M, Vähäsarja, P, Mustonen, O, Dunder, T, Kulmala, P, Lasa, E, D’Amelio, C, Martínez, S, Joral, A, Gastaminza, G, Goikoetxea, MJ, Candy, DCA, Van Ampting, MTJ, Oude Nijhuis, MM, Butt, AM, Peroni, DG, Fox, AT, Knol, J, Michaelis, LJ, Padua, I, Padrao, P, Moreira, P, Barros, R, Sharif, H, Ahmed, M, Gomaa, N, Mens, J, Smit, K, Timmermans, F, Poredoš, T, Jeverica, AK, Sedmak, M, Benedik, E, Accetto, M, Zupančič, M, Yonamine, G, Soldateli, G, Aquilante, B, Pastorino, AC, de Moraes Beck, CL, Gushken, AK, de Barros Dorna, M, dos Santos, CN, Castro, APM, Al-Qahtani, A, Arnaout, R, Khaliq, AR, Amin, R, Sheikh, F, Alvarez, J, Anda, M, Palacios, M, De Prada, M, Ponce, C, Balbino, B, Sibilano, R, Marichal, T, Gaudenzio, N, Karasuyama, H, Bruhns, P, Tsai, M, Reber, LL, Galli, SJ, Ferreira, AR, Cernadas, JR, del Campo García, A, Fernández, SP, Carrera, NS, Sánchez-Cruz, FB, Lorenzo, JRF, Claus, S, Pföhler, C, Ruëff, F, Treudler, R, Jaume, ME, Madroñero, A, Perez, MTG, Julia, JC, Plovdiv, CH, Gethings, L, Langridge, J, Adel-Patient, K, Bernard, H, Barcievic-Jones, I, Sokolova, R, Yankova, R, Ivanovska, M, Murdjeva, M, Popova, T, Dermendzhiev, S, Karjalainen, M, Lehnigk, U, Brown, D, Locklear, JC, Locklear, J, Maris, I, Hourihane, J, Ornelas, C, Caiado, J, Ferreira, MB, Pereira-Barbosa, M, Puente, Y, Daza, JC, Monteseirin, FJ, Ukleja-Sokolowska, N, Gawronska-Ukleja, E, Zbikowska-Gotz, M, Bartuzi, Z, Sokolowski, L, Adams, A, Mahon, B, English, K, Gourdon-Dubois, N, Sellam, L, Pereira, B, Michaud, E, Messaoudi, K, Evrard, B, Fauquert, J-L, Palomares, F, Gomez, G, Rodriguez, MJ, Galindo, L, Molina, A, Paparo, L, Mennini, M, Aitoro, R, Wawrzeńczyk, A, Przybyszewski, M, Sarıcoban, HE, Ugras, M, Yalvac, Z, Flokstra-de Blok, BMJ, van der Velde, JL, Vereda, A, Ippolito, C, Traversa, A, Adriano, D, Bianchi, DM, Gallina, S, Decastelli, L, Makatsori, M, Miles, A, Devetak, SP, Devetak, I, Tabet, SA, Trandbohus, JF, Winther, P, Malling, H-J, Hansen, KS, Garvey, LH, Wang, C-C, Cheng, Y-H, Tung, C-W, Dietrich, M, Marenholz, I, Kalb, B, Grosche, S, Blümchen, K, Schlags, R, Price, M, Rietz, S, Esparza-Gordillo, J, Lau, S, Lee, Y-A, Almontasheri, A, Bahkali, MA, Elshorbagi, S, Alfhaid, A, Altamimi, M, Madbouly, E, Al-Dhekri, H, Arnaout, RK, Basagaña, M, Miquel, S, Bartolomé, B, Brix, B, Rohwer, S, Brandhoff, S, Berger, A, Suer, W, Weimann, A, Bueno, C, Martín-Pedraza, L, Abián, S, Segundo-Acosta, PS, López-Rodríguez, JC, Barderas, R, Batanero, E, Cuesta-Herranz, J, Villalba, MT, Correia, M, Benito-Garcia, F, Arêde, C, Piedade, S, Morais-Almeida, M, Hindley, J, Yarham, R, Kuklinska-Pijanka, A, Gillick, D, Patient, K, Chapman, MD, Miranda, A, Matos, E, Sokolova, A, Rao, H, Baricevic-Jones, I, Smith, F, Xue, W, Magnusdottir, H, Vidarsdottir, AG, Lund, S, Jensen, AB, Ludviksson, BR, Simon, R, Elfont, R, Bennett, S, Voyksner, R, de Lurdes Torre, M, Yürek, S, Faber, MA, Bastiaensen, A, Mangodt, E, van Gasse, A, Decuyper, I, Sabato, V, Hagendorens, MM, Bridts, CH, De Clerck, LS, Ebo, D, Schwarz, S, Ziegert, M, Albroscheit, S, Schwager, C, Kull, S, Behrends, J, Röckendorf, N, Schocker, F, Frey, A, Homann, A, Becker, W-M, Jappe, U, Zaabat, N, Osscini, S, Agabriel, C, Sterling, B, Carsin, A, Liabeuf, V, Maćków, M, Zbróg, A, Bronkowska, M, Courtois, J, Gadisseur, R, Bertholet, C, Lukas, P, Cavalier, E, Delahaut, P, Quinting, B, Gertmo, MB, Hasseus, ET, Barzylovych, V, Oliveira, J, Ensina, LF, Aranda, CS, Dopazo, L, Lopez, R, Perez, R, Santos-Diez, L, Bilbao, A, Garcia, JM, Núñez, IG, Mármol, MÁA, Villarejo, MJB, Martos, JAB, Vergara, MS, García, JMI, Michalska, A, Sergiejko, G, Zacniewski, R, Ghiordanescu, I-M, Deaconu, C, Popescu, M, Bumbacea, RS, Ibranji, A, Nikolla, E, Loloci, G, Juel-Berg, N, Larsen, LF, Poulsen, LK, Marcelino, J, Prata, R, Costa, AC, Duarte, F, Neto, M, Santos, J, Pestana, LC, Sampaio, D, Minale, P, Dignetti, P, Bignardi, D, Nedelea, I, Popescu, F-D, Vieru, M, Secureanu, F-A, Ganea, CS, Vieira, M, Silva, JPM, Watts, T, Watts, S, Lomikovska, M, Peredelskaya, M, Nenasheva, N, Filipovic, I, Zivkovic, Z, Filipovic, D, Higgs, J, Warner, A, and Jones, C

Published
2017

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

Author

Kuper, C.F., Gröllers-Mulderij, M., Maarschalkerweerd, T., Meulendijks, N.M.M., Reus, A., Acker, F. van, Zondervan-van den Beuken, E.K., Wouters, M.E.L., Bijlsma, S., and Kooter, I.M.

Subjects
Life, Mucociliary apparatus, RAPID - Risk Analysis for Products in Development, Food and Nutrition, Cerium oxide, In vitro 3D human bronchial epithelial model, Mucociliary clearance, ELSS - Earth, Life and Social Sciences, Healthy Living, Nutrition
Abstract

We investigated the toxicity of aggregated nanoparticles of cerium oxide (CeO2) using an in vitro 3D human bronchial epithelial model that included a mucociliary apparatus (MucilAir™). CeO2 was dispersed in saline and applied to the apical surface of the model. CeO2 did not induce distinct effects in the model, whereas it did in BEAS-2B and A549 cell cultures. The absence of effects of CeO2 was not because of the model’s insensitivity. Nanoparticles of zinc oxide (ZnO) elicited positive responses in the toxicological assays. Respiratory mucus (0.1% and 1%) added to dispersions increased aggregation/agglomeration to such an extent that most CeO2 sedimented within a few minutes. Also, the mucociliary apparatus of the model removed CeO2 from the central part of the apical surface to the borders. This ‘clearance’ may have prevented the majority of CeO2 from reaching the epithelial cells. Chemical analysis of cerium in the basal tissue culture medium showed only minimal translocation of cerium across the 3D barrier. In conclusion, mucociliary defence appeared to prevent CeO2 reaching the respiratory epithelial cells in this 3D in vitro model. This model and approach can be used to study compounds of specific toxicological concern in airway defence mechanisms in vitro.

Published
2014

13. Raman Spectroscopy as a Promising Tool for Noninvasive Point-of-Care Glucose Monitoring

Author

Scholtes-Timmerman, M.J., Bijlsma, S., Fokkert, M.J., Slingerland, R., and Veen, S.J.F. van

Subjects
Adult, Male, Hospital information system, Point-of-Care Systems, Glucose blood level, Noninvasive, Procedures, Spectrum Analysis, Raman, Devices, Blood glucose, Humans, Food and Nutrition, Nutrition, Aged, Monitoring, Physiologic, Aged, 80 and over, Blood Glucose Self-Monitoring, Blood glucose monitoring, Very elderly, Middle Aged, Point of care, Self-monitoring of blood glucose, Raman spectroscopy, Female, RAPID - Risk Assessment Products in Development AEC - Applied Environmental Chemistry, ELSS - Earth, Life and Social Sciences, Healthy Living, Life Earth / Environmental, Analysis
Abstract

Self-monitoring of glucose is important for managing diabetes. Noninvasive glucose monitors are not yet available, but patients would benefit highly from such a device. We present results that may lead to a novel, point-of-care noninvasive system to measure blood glucose based on Raman spectroscopy. A hospitalized cohort of 111 subjects was measured using a custommade Raman spectrometer system. Blood glucose reference samples were used to correlate Raman data to glucose levels, using advanced preprocessing and analysis algorithms. A correlation coefficient (R2) of .83 was found correlating independent Raman-based predictions on reference blood glucose for the full cohort. Stratification of the cohort in gender-specific groups raised correlation levels to .88 (females) and .94 (males). Glucose could be measured noninvasively with average errors as low as 0.9 mM. We conclude that this novel system shows promising results for the advance of noninvasive, point-of-care glucose monitoring.

Published
2014

14. Dielectric breakdown II: Related projects at the University of twente

Author

Klootwijk, J.H., Verweij, J.F., Rem, J.B., Bijlsma, S., and Bijlsma, S.J.

Subjects
Materials science, Dielectric strength, business.industry, Gate dielectric, General Engineering, Electrical engineering, Dielectric, Semiconductor device, Engineering physics, IR-15125, law.invention, law, Thin-film transistor, METIS-111997, Thin film, business, Diode, EEPROM
Abstract

In this paper an overview is given of the related activities in our group of the University of Twente. These are on thin film transistors with the inherent difficulty of making a gate dielectric at low temperature, on thin dielectrics for EEPROM devices with well-known requirements with respect to charge retention and endurance and, finally, on thin film diodes in displays with unexpected breakdown properties.

Published
1996
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15. Metabolite profiling of small cerebrospinal fluid sample volumes with gas chromatography-mass spectrometry: application to a rat model of Multiple Sclerosis

Author

Coulier, L., Muilwijk, B., Bijlsma, S., Noga, M., Tienstra, M., Attali, A., Aken, H. van, Suidgeest, E., Tuinstra, T., Hankemeier, T., and Bobeldijk, I.

Subjects
Life, Biology Health, Healthy for Life, QS - Quality & Safety, EELS - Earth, Environmental and Life Sciences, Healthy Living
Abstract

Analysis of metabolites in biofluids by gas chromatography–mass spectrometry (GC–MS) after oximation and silylation is a key method in metabolomics. The GC–MS method was modified by a modified vial design and sample work-up procedure in order to make the method applicable to small volumes of cerebrospinal fluid (CSF), i.e. 10 μL, with similar coverage compared to the standard procedure using ≥100 μL of CSF. The data quality of the modified GC–MS method was assessed by analyzing a study sample set in an animal model for multiple sclerosis, including repetitively analysed quality control rat CSF samples. Automated normalization and intra- and inter-batch correction significantly improved the data quality with the majority of metabolites showing a relative standard deviation

Published
2012

16. Analyzing longitudinal microbial metabolomics data

Author

Rubingh, C.M., Bijlsma, S., Jellema, R.H., Overkamp, K.M., Werf, M.J. van der, Smilde, A.K., and TNO Kwaliteit van Leven

Subjects
Multiway analysis, Dynamic data, Longitudinal data, nPLS, Metabolomics, Biology, Megavariate data, Microbiology, Analytical research
Abstract

A longitudinal experimental design in combination with metabolomics and multiway data analysis is a powerful approach in the identification of metabolites whose correlation with bioproduct formation shows a shift in time. In this paper, a strategy is presented for the analysis of longitudinal microbial metabolomics data, which was performed in order to identify metabolites that are likely inducers of phenylalanine production by Escherichia coli. The variation in phenylalanine production as a function of differences in metabolism induced by the different environmental conditions in time was described by a validated multiway statistical model. Notably, most of the metabolites showing the strongest relations with phenylalanine production seemed to hardly change in time. Apparently, potential bottlenecks in phenylalanine seem to hardly change in the course of a batch fermentation. The approach described in this study is not limited to longitudinal microbial studies but can also be applied to other (biological) studies in which similar longitudinal data need to be analyzed. © 2009 American Chemical Society.

Published
2009

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

Author

Zanden, J.J. van, Wortelboer, H.M., Bijlsma, S., Punt, A., Usta, M., Bladeren, P.J.V., Rietjens, I.M.C.M., Cnubben, N.H.P., and TNO Kwaliteit van Leven

Subjects
flavone, Quantitative Structure-Activity Relationship, robinetin, animal cell, 3,3',4' trihydroxyflavone, quercetin, taxifolin, chrysin, myricetin, 4' hydroxyflavone, acacetin, 3 hydroxyflavone, heterocyclic compounds, multidrug resistance protein 1, multidrug resistance protein 2, QSAR, MRP2, drug effect, article, quantitative structure activity relation, 3,3' dihydroxyflavone, galangin, 3',4' dihydroxyflavone, Fluoresceins, unclassified drug, priority journal, MRP1, Multidrug Resistance-Associated Proteins, 3' hydroxyflavone, drug potency, baicalein, cell kinetics, fisetin, naringenin, kaempferide, Cell Line, cell transport, Dogs, catechin, Membrane Transport Modulators, Nutrition Pharmacology, valspodar, unindexed drug, Animals, flavonoid, controlled study, luteolin, drug inhibition, drug selectivity, Analytical research, Flavonoids, apigenin, kaempferol, nonhuman, Membrane Transport Proteins, IC 50, morin, 5,7,3',4' tetramethoxyflavone, protein inhibitor, verlukast, protein analysis, Calcein, genetic transfection
Abstract

In the present study, the effects of a large series of flavonoids on multidrug resistance proteins (MRPs) were studied in MRP1 and MRP2 transfected MDCKII cells. The results were used to define the structural requirements of flavonoids necessary for potent inhibition of MRP1- and MRP2-mediated calcein transport in a cellular model. Several of the methoxylated flavonoids are among the best MRP1 inhibitors (IC50 values, ranging between 2.7 and 14.3 μM) followed by robinetin, myricetin and quercetin (IC50 values ranging between 13.6 and 21.8 μM). Regarding inhibition of MRP2 activity especially robinetin and myricetin appeared to be good inhibitors (IC 50 values of 15.0 and 22.2 μM, respectively). Kinetic characterization revealed that the two transporters differ marginally in the apparent Km for the substrate calcein. For one flavonoid, robinetin, the kinetics of inhibition were studied in more detail and revealed competitive inhibition with respect to calcein, with apparent inhibition constants of 5.0 μM for MRP1 and 8.5 μM for MRP2. For inhibition of MRP1, a quantitative structure activity relationship (QSAR) was obtained that indicates three structural characteristics to be of major importance for MRP1 inhibition by flavonoids: the total number of methoxylated moieties, the total number of hydroxyl groups and the dihedral angle between the B- and C-ring. Regarding MRP2 mediated calcein efflux inhibition, only the presence of a flavonol B-ring pyrogallol group seems to be an important structural characteristic. Overall, this study provides insight in the structural characteristics involved in MRP inhibition and explores the differences between inhibitors of these two transporters, MRP1 and MRP2. Ultimately, MRP2 displays higher selectivity for flavonoid type inhibition than MRP1. © 2004 Elsevier Inc. All rights reserved.

Published
2005

18. In search of secreted protein biomarkers for the anti-inflammatory effect of β2-adrenergic receptor agonists: Application of DIGE technology in combination with multivariate and univariate data analysis tools

Author

Verhoeckx, K.C.M., Gaspari, M., Bijlsma, S., Greef, J. van der, Witkamp, R.F., Doornbos, R.P., Rodenburg, R.J.T., and TNO Kwaliteit van Leven

Subjects
β2-adrenergic receptor, Lipopolysaccharides, Proteomics, endotoxin, Trimethylsilyl Compounds, Proteome, Partial least squares discriminant analysis, principal component analysis, Macrophage inflammatory protein-1β, data analysis, heat shock protein, Anti-Inflammatory Agents, Mass Spectrometry, Monocytes, beta 2 adrenergic receptor blocking agent, Cluster Analysis, Electrophoresis, Gel, Two-Dimensional, Immunoassay, analytic method, beta 2 adrenergic receptor stimulating agent, Statistics, zilpaterol, article, U937 Cells, biological marker, Adrenergic Agonists, Propranolol, unclassified drug, Up-Regulation, multivariate analysis, priority journal, Difference gel electrophoresis, Biological Markers, macrophage inflammatory protein 1alpha, two dimensional gel electrophoresis, Adrenergic beta-Antagonists, adrenergic system, formoterol, Down-Regulation, Enzyme-Linked Immunosorbent Assay, macrophage, adenylate kinase, forskolin, clenbuterol, statistical analysis, beta 2 adrenergic receptor, protein secretion, macrophage inflammatory protein 1beta, Escherichia coli, liquid chromatography, Humans, controlled study, human, Multivariate data analysis, protein expression, Analytical research, Pharmacology, Inflammation, prostaglandin E2, Macrophage Inflammatory Protein-1, human cell, Macrophages, antiinflammatory activity, nucleotide sequence, Escherichia coli lipopolysaccharide, discriminant analysis, phosphoglycerate mutase, salbutamol, butyryl cyclic AMP, Receptors, Adrenergic, beta-2, two dimensional difference gel electrophoresis
Abstract

Two-dimensional difference gel electrophoresis (DIGE) in combination with univariate (Student's t-test) and multivariate data analysis, principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were used to study the anti-inflammatory effects of the β2-adrenergic receptor (β2-AR) agonist zilpaterol. U937 macrophages were exposed to the endotoxin lipopolysaccharide (LPS) to induce an inflammatory reaction, which was inhibited by the addition of zilpaterol (LZ). This inhibition was counteracted by addition of the β2-AR antagonist propranolol (LZP). The extracellular proteome of the U937 cells induced by the three treatments were examined by DIGE. PCA was used as an explorative tool to investigate the clustering of the proteome dataset. Using this tool, the dataset obtained from cells treated with LPS and LZP were separated from those obtained from LZ treated cells. PLS-DA, a multivariate data analysis tool that also takes correlations between protein spots and class assignment into account, correctly classified the different extracellular proteomes and showed that many proteins were differentially expressed between the proteome of inflamed cells (LPS and LZP) and cells in which the inflammatory response was inhibited (LZ). The Student's t-test revealed 8 potential protein biomarkers, each of which was expressed at a similar level in the LPS and LZP treated cells, but differently expressed in the LZ treated cells. Two of the identified proteins, macrophage inflammatory protein-1beta (MIP-1β) and macrophage inflammatory protein-1 alpha (MIP-1α) are known secreted proteins. The inhibition of MIP-1β by zilpaterol and the involvement of the β2-AR and cAMP were confirmed using a specific immunoassay. © 2005 American Chemical Society.

Published
2005

19. Characterization of anti-inflammatory compounds using transcriptions, proteomics, and metabolics in combination with multivariate data analysis

Author

Verhoeckx, K.C.M., Bijlsma, S., Jespersen, S., Ramaker, R., Verheij, E.R., Witkamp, R.F., Greef, J. van der, Rodenburg, R.J.T., and TNO Voeding Centraal Instituut voor Voedingsonderzoek TNO

Subjects
Proteomics, Lipopolysaccharides, Unclassified drug, Proteome, Salbutamol, Anti-Inflammatory Agents, Monocyte, Dexamethasone, Electrophoresis, Gel, Two-Dimensional, Formoterol, Priority journal, Oligonucleotide Array Sequence Analysis, Genetic transcription, Zilpaterol, 2-D gel electrophoresis, Adrenergic beta-Agonists, Lipids, Anti-inflammatory drugs, Beta 2 adrenergic receptor stimulating agent, Human, Micro-array, Antiinflammatory activity, Principal component discriminant analysis, Data analysis, Liquid chromatography, Lipopolysaccharide, Gas Chromatography-Mass Spectrometry, Antiinflammatory agent, Cell Line, Tumor, Metabolomics, 4 (4 fluorophenyl) 2 (4 methylsulfinylphenyl) 5 (4 pyridyl)imidazole, Humans, Clenbuterol, controlled study, RNA, Messenger, Multivariate data analysis, Mass spectrometry, Lipogenesis, Macrophages, DNA microarray, Methodology, Molecule, Macrophage Activation, Gel electrophoresis, Pharmacology Health, Metabolism, Human cell, Multivariate analysis, Drug determination, Protein expression, Receptors, Adrenergic, beta-2
Abstract

The discovery of new anti-inflammatory drugs is often based on an interaction with a specific target, although other pathways often play a primary or secondary role. Anti-inflammatory drugs can be categorized into classes, based on their mechanism of action. In this article we investigate the possibility to characterize novel anti-inflammatory compounds by three holistic methods. For this purpose, we make use of macrophage-like U937 cells which are stimulated with LPS in the absence or presence of an anti-inflammatory compound. Using micro-arrays, 2-D gel electrophoresis and a LC-MS method for lipids the effects on the transcriptome, proteome and metabolome of the exposed cells is investigated. The expression patterns are subsequently analyzed using in-house developed pattern recognition tools. Using the methods described above, we have examined the effects of six anti-inflammatory compounds. Our results demonstrate that different classes of anti-inflammatory compounds show distinct and characteristic mRNA, protein, and lipid expression patterns, which can be used to categorise known molecules and to discover and classify new leads. The potential of our approach is illustrated by the analysis of several beta (2)-adrenergic agonists (β2-agonists). In addition to their primary pharmacological target, β2-agonists posses certain anti-inflammatory properties. We were able to show that zilpaterol, a poorly characterized β2-agonist, gives rise to an almost identical expression pattern as the β2-agonists clenbuterol and salbutamol. Furthermore we have identified specific mRNA, protein and lipid markers for the anti-inflammatory compounds investigated in this study. © 2004 Elsevier B.V. All rights reserved. Chemicals/CAS: 4 (4 fluorophenyl) 2 (4 methylsulfinylphenyl) 5 (4 pyridyl)imidazole, 152121-47-6; clenbuterol, 21898-19-1, 37148-27-9; dexamethasone, 50-02-2; formoterol, 73573-87-2; salbutamol, 18559-94-9; Adrenergic beta-Agonists; Anti-Inflammatory Agents; Lipids; lipopolysaccharide, Escherichia coli 0111 B4; Lipopolysaccharides; Proteome; Receptors, Adrenergic, beta-2; RNA, Messenger

Published
2004

22. Sufficient conditions for unique solutions within a certain class of curve resolution models

Author

Smilde, A.K., Hoefsloot, H.C.J., Kiers, H.A.L., Bijlsma, S., Boelens, H.F.M., Other departments, HIMS (FNWI), and TNO Voeding

Subjects
Nutrition
Abstract

Curve resolution is a class of techniques concerned with estimating profiles underlying a set of measurements of time-evolving chemical systems. In general, the estimated profiles are not unique. Both intensity and rotational ambiguities exist in the solutions of these problems. Constraints can be imposed on the solution to decrease the ambiguity. Some chemical systems show closure. It is proven that imposing a closure constraint is sufficient to solve the intensity ambiguity but not the rotational ambiguity. Some curve resolution techniques are concerned with estimating reaction rate or equilibrium constants from measurements of evolving systems. Then a kinetic model is imposed on the measured data. It is proven that imposing a certain class of kinetic models is sufficient to solve both the rotational and the intensity ambiguity. Copyright (C) 2001 John Wiley gr Sons, Ltd

Published
2001

23. Chaperonnes, deelproject 4 Rapportage Technology Scan

Author

Meloen, R.H., Oonk, H.B., van de Wiel, D.F.M., Swanenburg, M., Rijsman, D.F.M., Leloux, M.S., Haasnoot, W., van der Gaag, A., Tas, A.C., and Bijlsma, S.

Subjects
food safety, monitoring, melkproducten, rundvlees, Wageningen Food Safety Research, pigmeat, voedselveiligheid, milk products, beef, varkensvlees
Published
2001

27. In search of secreted protein biomarkers for the anti-inflammatory effect of beta2-adrenergic receptor agonists: application of DIGE technology in combination with multivariate and univariate data analysis tools.

Author

Verhoeckx, K.C., Gaspari, M., Bijlsma, S., Greef, J. van der, Witkamp, R.F., Doornbos, R.P., Rodenburg, R.J.T., Verhoeckx, K.C., Gaspari, M., Bijlsma, S., Greef, J. van der, Witkamp, R.F., Doornbos, R.P., and Rodenburg, R.J.T.

Abstract

Contains fulltext : 32622.pdf (publisher's version ) (Closed access), Two-dimensional difference gel electrophoresis (DIGE) in combination with univariate (Student's t-test) and multivariate data analysis, principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were used to study the anti-inflammatory effects of the beta(2)-adrenergic receptor (beta(2)-AR) agonist zilpaterol. U937 macrophages were exposed to the endotoxin lipopolysaccharide (LPS) to induce an inflammatory reaction, which was inhibited by the addition of zilpaterol (LZ). This inhibition was counteracted by addition of the beta(2)-AR antagonist propranolol (LZP). The extracellular proteome of the U937 cells induced by the three treatments were examined by DIGE. PCA was used as an explorative tool to investigate the clustering of the proteome dataset. Using this tool, the dataset obtained from cells treated with LPS and LZP were separated from those obtained from LZ treated cells. PLS-DA, a multivariate data analysis tool that also takes correlations between protein spots and class assignment into account, correctly classified the different extracellular proteomes and showed that many proteins were differentially expressed between the proteome of inflamed cells (LPS and LZP) and cells in which the inflammatory response was inhibited (LZ). The Student's t-test revealed 8 potential protein biomarkers, each of which was expressed at a similar level in the LPS and LZP treated cells, but differently expressed in the LZ treated cells. Two of the identified proteins, macrophage inflammatory protein-1beta (MIP-1beta) and macrophage inflammatory protein-1alpha (MIP-1alpha) are known secreted proteins. The inhibition of MIP-1beta by zilpaterol and the involvement of the beta(2)-AR and cAMP were confirmed using a specific immunoassay.

Published
2005

28. A combination of proteomics, principal component analysis and transcriptomics is a powerful tool for the identification of biomarkers for macrophage maturation in the U937 cell line.

Author

Verhoeckx, K.C., Bijlsma, S., Groene, E.M. de, Witkamp, R.F., Greef, J. van der, Rodenburg, R.J.T., Verhoeckx, K.C., Bijlsma, S., Groene, E.M. de, Witkamp, R.F., Greef, J. van der, and Rodenburg, R.J.T.

Abstract

Contains fulltext : 57316.pdf (publisher's version ) (Closed access), The monocyte-like human histiocytic lymphoma cell line U937 can be induced by phorbol 12-myristate 13-acetate (PMA) to undergo differentiation into a macrophage-like phenotype. We have used two-dimensional gel electrophoresis (2-DE), oligonucleotide microarrays and principal component analysis (PCA) to characterize the U937 cell line as a model system for the differentiation of monocytes into macrophages. A total of 226 differentially expressed proteins were found, of which 41 were selected by PCA for identification using matrix-assisted laser desorption/ionization tandem mass spectrometry. Based on the PCA results, three marker proteins were selected for confirmation of differential expression using Western blot and quantitative real time-PCR. The selected marker proteins were: gamma interferon inducible lysosomal thiol reductase, cathepsin D and adipocyte-fatty acid binding protein. All three proved to be good differentiation markers for macrophage maturation of U937 cells as well as peripheral blood-derived macrophages. The transcriptomics data revealed a large number of additional putative differentiation markers in U937 macrophages, many of which are known to be expressed in peripheral blood-derived macrophages. These include osteospontin, matrix metalloproteinase 9, and HC-gp39. Our results show that the characteristics of U937 macrophages resemble those of inflammatory (exudate) macrophages, exemplified by the down-regulation of 5' nucleotidase and the up-regulation of leucine aminopeptidase mRNAs. In conclusion, using the powerful combination of transcriptomics, 2-DE and PCA, our results show that U937 cells differentiated by PMA treatment are an excellent model system for monocyte derived macrophage generation from blood.

Published
2004

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

Author

Verhoeckx, K.C., Bijlsma, S., Jespersen, S., Ramaker, R., Verheij, E.R., Witkamp, R.F., Greef, J. van der, Rodenburg, R.J.T., Verhoeckx, K.C., Bijlsma, S., Jespersen, S., Ramaker, R., Verheij, E.R., Witkamp, R.F., Greef, J. van der, and Rodenburg, R.J.T.

Abstract

Contains fulltext : 58859.pdf (publisher's version ) (Closed access), The discovery of new anti-inflammatory drugs is often based on an interaction with a specific target, although other pathways often play a primary or secondary role. Anti-inflammatory drugs can be categorized into classes, based on their mechanism of action. In this article we investigate the possibility to characterize novel anti-inflammatory compounds by three holistic methods. For this purpose, we make use of macrophage-like U937 cells which are stimulated with LPS in the absence or presence of an anti-inflammatory compound. Using micro-arrays, 2-D gel electrophoresis and a LC-MS method for lipids the effects on the transcriptome, proteome and metabolome of the exposed cells is investigated. The expression patterns are subsequently analyzed using in-house developed pattern recognition tools. Using the methods described above, we have examined the effects of six anti-inflammatory compounds. Our results demonstrate that different classes of anti-inflammatory compounds show distinct and characteristic mRNA, protein, and lipid expression patterns, which can be used to categorise known molecules and to discover and classify new leads. The potential of our approach is illustrated by the analysis of several beta (2)-adrenergic agonists (beta2-agonists). In addition to their primary pharmacological target, beta2-agonists posses certain anti-inflammatory properties. We were able to show that zilpaterol, a poorly characterized beta2-agonist, gives rise to an almost identical expression pattern as the beta2-agonists clenbuterol and salbutamol. Furthermore we have identified specific mRNA, protein and lipid markers for the anti-inflammatory compounds investigated in this study.

Published
2004

33. Dynamic metabolomic data analysis: a tutorial review

Author

Smilde, A. K., primary, Westerhuis, J. A., additional, Hoefsloot, H. C. J., additional, Bijlsma, S., additional, Rubingh, C. M., additional, Vis, D. J., additional, Jellema, R. H., additional, Pijl, H., additional, Roelfsema, F., additional, and van der Greef, J., additional

Published
2009
Full Text
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42. In Search of Secreted Protein Biomarkers for the Anti-inflammatory Effect of β<INF>2</INF>-Adrenergic Receptor Agonists:  Application of DIGE Technology in Combination with Multivariate and Univariate Data Analysis Tools

Author

Verhoeckx, K. C. M., Gaspari, M., Bijlsma, S., Greef, J. van der, Witkamp, R. F., Doornbos, R. P., and Rodenburg, R. J. T.

Abstract

Two-dimensional difference gel electrophoresis (DIGE) in combination with univariate (Student's t-test) and multivariate data analysis, principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were used to study the anti-inflammatory effects of the β2-adrenergic receptor (β2-AR) agonist zilpaterol. U937 macrophages were exposed to the endotoxin lipopolysaccharide (LPS) to induce an inflammatory reaction, which was inhibited by the addition of zilpaterol (LZ). This inhibition was counteracted by addition of the β2-AR antagonist propranolol (LZP). The extracellular proteome of the U937 cells induced by the three treatments were examined by DIGE. PCA was used as an explorative tool to investigate the clustering of the proteome dataset. Using this tool, the dataset obtained from cells treated with LPS and LZP were separated from those obtained from LZ treated cells. PLS-DA, a multivariate data analysis tool that also takes correlations between protein spots and class assignment into account, correctly classified the different extracellular proteomes and showed that many proteins were differentially expressed between the proteome of inflamed cells (LPS and LZP) and cells in which the inflammatory response was inhibited (LZ). The Student's t-test revealed 8 potential protein biomarkers, each of which was expressed at a similar level in the LPS and LZP treated cells, but differently expressed in the LZ treated cells. Two of the identified proteins, macrophage inflammatory protein-1beta (MIP-1β) and macrophage inflammatory protein-1alpha (MIP-1α) are known secreted proteins. The inhibition of MIP-1β by zilpaterol and the involvement of the β2-AR and cAMP were confirmed using a specific immunoassay. Keywords: β2-adrenergic receptor • difference gel electrophoresis • macrophage inflammatory protein-1β • multivariate data analysis • principal component analysis • partial least squares discriminant analysis • Zilpaterol

Published
2005

43. A Computational Method in Ship Routing Using the Concept of Limited Manoeuvrability

Author

Bijlsma, S. J.

Abstract

Under some circumstances, dependent on a ship's velocity, the wave period and wave direction, certain courses induce heavy rolling and must be avoided. This paper proposes a computational method for the solution of optimal control problems in ship routing for ships with such limited manoeuvrability. Known results for the control problem of Bolza with additional constraints are interpreted in terms of this new problem. This approach is equivalent to the application of Pontryagin's maximum principle. The method is an extension of an earlier method dealing with the meteorological navigation of ships with unrestricted manoeuvrability and gives a more realistic picture of what really could happen in practice.

Published
2004

48. Roofvogelvervolging in Friesland

Author

Bijlsma, S. and Bijlsma, S.

Abstract

Gegevens over roofvogelvervolging en opzettelijke nestverstoringen in de periode 2000 t/m 2006 in Friesland.

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