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2. Electrically driven quantum light emission in electromechanically-tuneable photonic crystal cavities

Author

Petruzzella, M., Pagliano, F. M., Zobenica, Z., Birindelli, S., Cotrufo, M., van Otten, F. W. M, van der Heijden, R. W., and Fiore, A.

Subjects
Quantum Physics, Physics - Optics
Abstract

A single quantum dot deterministically coupled to a photonic crystal environment constitutes an indispensable elementary unit to both generate and manipulate single-photons in next-generation quantum photonic circuits. To date, the scaling of the number of these quantum nodes on a fully-integrated chip has been prevented by the use of optical pumping strategies that require a bulky off-chip laser along with the lack of methods to control the energies of nano-cavities and emitters. Here, we concurrently overcome these limitations by demonstrating electrical injection of single excitonic lines within a nano-electro-mechanically tuneable photonic crystal cavity. When an electrically-driven dot line is brought into resonance with a photonic crystal mode, its emission rate is enhanced. Anti-bunching experiments reveal the quantum nature of these on-demand sources emitting in the telecom range. These results represent an important step forward in the realization of integrated quantum optics experiments featuring multiple electrically-triggered Purcell-enhanced single-photon sources embedded in a reconfigurable semiconductor architecture.

Published
2017
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4. InP-based two-dimensional photonic crystals filled with polymers

Author

van der Heijden, R., Carlstrom, C. F., Snijders, J. A. P., van der Heijden, R. W., Karouta, F., Notzel, R., Salemink, H. W. M., Kjellander, B. K. C., Bastiaansen, C. W. M., Broer, D. J., and van der Drift, E.

Subjects
Physics - Optics
Abstract

Polymer filling of the air holes of Indium Phosphide based two-dimensional photonic crystals is reported. After infiltration of the holes with a liquid monomer and solidification of the infill in situ by thermal polymerization, complete filling is proven using scanning electron microscopy. Optical transmission measurements of a filled photonic crystal structure exhibit a redshift of the air band, confirming the complete filling., Comment: To be published in Appl. Phys. Lett

Published
2006
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5. Wat als er geen water meer is?

Author

Heijden, R. van der and Heijden, R. van der

Abstract

Droogtes komen steeds vaker voor en duren langer, terwijl er steeds meer drinkwater nodig is. Wat doen we als we niet meer genoeg water hebben?

Published
2024

6. Observation of the screening signature in the lateral photovoltage of electrons in the Quantum Hall regime

Author

van Zalinge, H., Oezyilmaz, B., Boehm, A., van der Heijden, R. W., Wolter, J. H., and Wyder, P.

Subjects
Condensed Matter - Mesoscale and Nanoscale Physics
Abstract

The lateral photovoltage generated in the plane of a two-dimensional electron system (2DES) by a focused light spot, exhibits a fine-structure in the quantum oscillations in a magnetic field near the Quantum Hall conductivity minima. A double peak structure occurs near the minima of the longitudinal conductivity oscillations. This is the characteristic signature of the interplay between screening and Landau quantization., Comment: 4 pages, 4 figures, to be published in Phys. Rev. B

Published
2001
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7. Hall magnetocapacitance in two-dimensional electron systems

Author

Valkering, A. M. C., Sommerfeld, P. K. H., van de Ven, R. A. M., van der Heijden, R. W., Blom, F. A. P., Lea, M. J., and Peeters, F. M.

Subjects
Condensed Matter - Mesoscale and Nanoscale Physics
Abstract

The magnetocapacitance of a two-dimensional electron system (2DES) is investigated experimentally, both under and away from Quantum Hall (QH) conditions, at frequencies between 1 kHz and 100 MHz. The nature of the capacitive signal in a bounded 2DES is determined by a resistive cut-off frequency 1/$\tau \propto \sigma_{xx}$, the longitudinal magnetoconductivity. A new response mechanism is reported for angular frequencies $\omega > 1/\tau$, which is controlled by the $\em transverse$ or Hall conductivity $\sigma_{xy}$ and the boundaries of the sample, even at frequencies far below those of the Edge Magnetoplasma resonances and away from the QH-conditions., Comment: Experimental paper, 4 figures

Published
1998
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9. The KNee OsteoArthritis Prediction (KNOAP2020) challenge:An image analysis challenge to predict incident symptomatic radiographic knee osteoarthritis from MRI and X-ray images

Author

Hirvasniemi, J., Runhaar, J., van der Heijden, R. A., Zokaeinikoo, M., Yang, M., Li, X., Tan, J., Rajamohan, H. R., Zhou, Y., Deniz, C. M., Caliva, F., Iriondo, C., Lee, J. J., Liu, F., Martinez, A. M., Namiri, N., Pedoia, V., Panfilov, E., Bayramoglu, N., Nguyen, H. H., Nieminen, M. T., Saarakkala, S., Tiulpin, A., Lin, E., Li, A., Li, V., Dam, E. B., Chaudhari, A. S., Kijowski, R., Bierma-Zeinstra, S., Oei, E. H.G., Klein, S., Hirvasniemi, J., Runhaar, J., van der Heijden, R. A., Zokaeinikoo, M., Yang, M., Li, X., Tan, J., Rajamohan, H. R., Zhou, Y., Deniz, C. M., Caliva, F., Iriondo, C., Lee, J. J., Liu, F., Martinez, A. M., Namiri, N., Pedoia, V., Panfilov, E., Bayramoglu, N., Nguyen, H. H., Nieminen, M. T., Saarakkala, S., Tiulpin, A., Lin, E., Li, A., Li, V., Dam, E. B., Chaudhari, A. S., Kijowski, R., Bierma-Zeinstra, S., Oei, E. H.G., and Klein, S.

Abstract

Objectives: The KNee OsteoArthritis Prediction (KNOAP2020) challenge was organized to objectively compare methods for the prediction of incident symptomatic radiographic knee osteoarthritis within 78 months on a test set with blinded ground truth. Design: The challenge participants were free to use any available data sources to train their models. A test set of 423 knees from the Prevention of Knee Osteoarthritis in Overweight Females (PROOF) study consisting of magnetic resonance imaging (MRI) and X-ray image data along with clinical risk factors at baseline was made available to all challenge participants. The ground truth outcomes, i.e., which knees developed incident symptomatic radiographic knee osteoarthritis (according to the combined ACR criteria) within 78 months, were not provided to the participants. To assess the performance of the submitted models, we used the area under the receiver operating characteristic curve (ROCAUC) and balanced accuracy (BACC). Results: Seven teams submitted 23 entries in total. A majority of the algorithms were trained on data from the Osteoarthritis Initiative. The model with the highest ROCAUC (0.64 (95% confidence interval (CI): 0.57–0.70)) used deep learning to extract information from X-ray images combined with clinical variables. The model with the highest BACC (0.59 (95% CI: 0.52–0.65)) ensembled three different models that used automatically extracted X-ray and MRI features along with clinical variables. Conclusion: The KNOAP2020 challenge established a benchmark for predicting incident symptomatic radiographic knee osteoarthritis. Accurate prediction of incident symptomatic radiographic knee osteoarthritis is a complex and still unsolved problem requiring additional investigation.

Published
2023

14. General Strategies

Author

Verpoorte, R., Van Der Heijden, R., Memelink, J., Verpoorte, Robert, editor, and Alfermann, A. W., editor

Published
2000
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17. Developing Clinical and Research Priorities for Pain and Psychological Features in People With Patellofemoral Pain: An International Consensus Process With Health Care Professionals

Author

Vicenzino, BT, Rathleff, MS, Holden, S, Maclachlan, L, Smith, BE, de Oliveira Silva, D, van Middelkoop, M, Arendt, E, Arvinen-Barrow, MM, Azevedo, F, Barton, C, Bazett-Jones, D, Boudreau, S, Briani, RV, Collins, NJ, Cowan, S, Crossley, K, Domenech, J, Dye, SF, Earl-Boehm, J, Esculier, JF, Glaviano, NR, Kedroff, L, Lack, S, Matthews, M, McConnell, J, O’Donovan, J, Pazzinatto, M, Robertson, C, Sanchis-Alfonso, V, Selfe, J, Selhorst, M, Thompson, C, Tobin, L, van der Heijden, R, van Linschoten, R, Alsaleh, S, Baellow, A, Belvedere, C, Bolgla, L, Boling, M, Callaghan, M, Ferreira, A, Fox, A, Giblin, N, Hart, H, Hill, J, Ho, KY, Hoglund, L, Lally, E, Macri, E, Powers, C, Salsich, G, Sigmund, K, Stewart, B, Taborda, B, Tang, HL, Thomson, C, Thorpe, J, Waiteman, M, Wheatley, M, Willy, R, Vicenzino, BT, Rathleff, MS, Holden, S, Maclachlan, L, Smith, BE, de Oliveira Silva, D, van Middelkoop, M, Arendt, E, Arvinen-Barrow, MM, Azevedo, F, Barton, C, Bazett-Jones, D, Boudreau, S, Briani, RV, Collins, NJ, Cowan, S, Crossley, K, Domenech, J, Dye, SF, Earl-Boehm, J, Esculier, JF, Glaviano, NR, Kedroff, L, Lack, S, Matthews, M, McConnell, J, O’Donovan, J, Pazzinatto, M, Robertson, C, Sanchis-Alfonso, V, Selfe, J, Selhorst, M, Thompson, C, Tobin, L, van der Heijden, R, van Linschoten, R, Alsaleh, S, Baellow, A, Belvedere, C, Bolgla, L, Boling, M, Callaghan, M, Ferreira, A, Fox, A, Giblin, N, Hart, H, Hill, J, Ho, KY, Hoglund, L, Lally, E, Macri, E, Powers, C, Salsich, G, Sigmund, K, Stewart, B, Taborda, B, Tang, HL, Thomson, C, Thorpe, J, Waiteman, M, Wheatley, M, and Willy, R

Abstract

Objective: To decide clinical and research priorities on pain features and psychological factors in persons with patellofemoral pain. Design: Consensus development process. Methods: We undertook a 3-stage process consisting of (1) updating 2 systematic reviews on quantitative sensory testing of pain features and psychological factors in patellofemoral pain, (2) an online survey of health care professionals and persons with patellofemoral pain, and (3) a consensus meeting with expert health care professionals. Participants responded that they agreed, disagreed, or were unsure that a pain feature or psychological factor was important in clinical practice or as a research priority. Greater than 70% participant agreement was required for an item to be considered important in clinical practice or a research priority. Results: Thirty-five health care professionals completed the survey, 20 of whom attended the consensus meeting. Thirty persons with patellofemoral pain also completed the survey. The review identified 5 pain features and 9 psychological factors—none reached 70% agreement in the patient survey, so all were considered at the meeting. After the meeting, pain catastrophizing, fear-avoidance beliefs, and pain self-efficacy were the only factors considered clinically important. All but the thermal pain tests and 3 psychological factors were considered research priorities. Conclusion: Pain catastrophizing, pain self-efficacy, and fear-avoidance beliefs were factors considered important in treatment planning, clinical examination, and prognostication. Quantitative sensory tests for pain were not regarded as clinically important but were deemed to be research priorities, as were most psychological factors.

Published
2022

18. The KNee OsteoArthritis prediction (KNOAP2020) challenge:an image analysis challenge to predict incident symptomatic radiographic knee osteoarthritis from MRI and X-ray images

Author

Hirvasniemi, J. (J.), Runhaar, J. (J.), van der Heijden, R. (R.A.), Zokaeinikoo, M. (M.), Yang, M. (M.), Li, X. (X.), Tan, J. (J.), Rajamohan, H. (H.R.), Zhou, Y. (Y.), Deniz, C. (C.M.), Caliva, F. (F.), Iriondo, C. (C.), Lee, J. (J.J.), Liu, F. (F.), Martinez, A. (A.M.), Namiri, N. (N.), Pedoia, V. (V.), Panfilov, E. (E.), Bayramoglu, N. (N.), Nguyen, H. (H.H.), Nieminen, M. (M.T.), Saarakkala, S. (S.), Tiulpin, A. (A.), Lin, E. (E.), Li, A. (A.), Li, V. (V.), Dam, E. (E.B.), Chaudhari, A. (A.S.), Kijowski, R. (R.), Bierma-Zeinstra, S. (S.), Oei, E. (E.H.G.), Klein, S. (S.), Hirvasniemi, J. (J.), Runhaar, J. (J.), van der Heijden, R. (R.A.), Zokaeinikoo, M. (M.), Yang, M. (M.), Li, X. (X.), Tan, J. (J.), Rajamohan, H. (H.R.), Zhou, Y. (Y.), Deniz, C. (C.M.), Caliva, F. (F.), Iriondo, C. (C.), Lee, J. (J.J.), Liu, F. (F.), Martinez, A. (A.M.), Namiri, N. (N.), Pedoia, V. (V.), Panfilov, E. (E.), Bayramoglu, N. (N.), Nguyen, H. (H.H.), Nieminen, M. (M.T.), Saarakkala, S. (S.), Tiulpin, A. (A.), Lin, E. (E.), Li, A. (A.), Li, V. (V.), Dam, E. (E.B.), Chaudhari, A. (A.S.), Kijowski, R. (R.), Bierma-Zeinstra, S. (S.), Oei, E. (E.H.G.), and Klein, S. (S.)

Abstract

Objectives: The KNee OsteoArthritis Prediction (KNOAP2020) challenge was organized to objectively compare methods for the prediction of incident symptomatic radiographic knee osteoarthritis within 78 months on a test set with blinded ground truth. Design: The challenge participants were free to use any available data sources to train their models. A test set of 423 knees from the Prevention of Knee Osteoarthritis in Overweight Females (PROOF) study consisting of magnetic resonance imaging (MRI) and X-ray image data along with clinical risk factors at baseline was made available to all challenge participants. The ground truth outcomes, i.e., which knees developed incident symptomatic radiographic knee osteoarthritis (according to the combined ACR criteria) within 78 months, were not provided to the participants. To assess the performance of the submitted models, we used the area under the receiver operating characteristic curve (ROCAUC) and balanced accuracy (BACC). Results: Seven teams submitted 23 entries in total. A majority of the algorithms were trained on data from the Osteoarthritis Initiative. The model with the highest ROCAUC (0.64 (95% confidence interval (CI): 0.57–0.70)) used deep learning to extract information from X-ray images combined with clinical variables. The model with the highest BACC (0.59 (95% CI: 0.52–0.65)) ensembled three different models that used automatically extracted X-ray and MRI features along with clinical variables. Conclusions: The KNOAP2020 challenge established a benchmark for predicting incident symptomatic radiographic knee osteoarthritis. Accurate prediction of incident symptomatic radiographic knee osteoarthritis is a complex and still unsolved problem requiring additional investigation.

Published
2022

19. Detecting hip osteoarthritis on clinical CT:a deep learning application based on 2-D summation images derived from CT

Author

Gebre, R. K. (R. K.), Hirvasniemi, J. (J.), van der Heijden, R. A. (R. A.), Lantto, I. (I.), Saarakkala, S. (S.), Leppilahti, J. (J.), Jämsä, T. (T.), Gebre, R. K. (R. K.), Hirvasniemi, J. (J.), van der Heijden, R. A. (R. A.), Lantto, I. (I.), Saarakkala, S. (S.), Leppilahti, J. (J.), and Jämsä, T. (T.)

Abstract

Summary: We developed and compared deep learning models to detect hip osteoarthritis on clinical CT. The CT-based summation images, CT-AP, that resemble X-ray radiographs can detect radiographic hip osteoarthritis and in the absence of large training data, a reliable deep learning model can be optimized by combining CT-AP and X-ray images. Introduction: In this study, we aimed to investigate the applicability of deep learning (DL) to assess radiographic hip osteoarthritis (rHOA) on computed tomography (CT). Methods: The study data consisted of 94 abdominopelvic clinical CTs and 5659 hip X-ray images collected from Cohort Hip and Cohort Knee (CHECK). The CT slices were sequentially summed to create radiograph-like 2-D images named CT-AP. X-ray and CT-AP images were classified as rHOA if they had osteoarthritic changes corresponding to Kellgren-Lawrence grade 2 or higher. The study data was split into 55% training, 30% validation, and 15% test sets. A pretrained ResNet18 was optimized for a classification task of rHOA vs. no-rHOA. Five models were trained using (1) X-rays, (2) downsampled X-rays, (3) combination of CT-AP and X-ray images, (4) combination of CT-AP and downsampled X-ray images, and (5) CT-AP images. Results: Amongst the five models, Model-3 and Model-5 performed best in detecting rHOA from the CT-AP images. Model-3 detected rHOA on the test set of CT-AP images with a balanced accuracy of 82.2% and was able to discriminate rHOA from no-rHOA with an area under the receiver operating characteristic curve (ROC AUC) of 0.93 [0.75–0.99]. Model-5 detected rHOA on the test set at a balanced accuracy of 82.2% and classified rHOA from no-rHOA with an ROC AUC of 0.89 [0.67–0.97]. Conclusion: CT-based summation images that resemble radiographs can be used to detect rHOA. In addition, in the absence of large training data, a reliable DL model can be optimized by combining CT-AP and X-ray images.

Published
2022

20. Detecting hip osteoarthritis on clinical CT:a deep learning application based on 2-D summation images derived from CT

Author

Gebre, R. K., Hirvasniemi, J., van der Heijden, R. A., Lantto, I., Saarakkala, S., Leppilahti, J., Jämsä, T., Gebre, R. K., Hirvasniemi, J., van der Heijden, R. A., Lantto, I., Saarakkala, S., Leppilahti, J., and Jämsä, T.

Abstract

Summary: We developed and compared deep learning models to detect hip osteoarthritis on clinical CT. The CT-based summation images, CT-AP, that resemble X-ray radiographs can detect radiographic hip osteoarthritis and in the absence of large training data, a reliable deep learning model can be optimized by combining CT-AP and X-ray images. Introduction: In this study, we aimed to investigate the applicability of deep learning (DL) to assess radiographic hip osteoarthritis (rHOA) on computed tomography (CT). Methods: The study data consisted of 94 abdominopelvic clinical CTs and 5659 hip X-ray images collected from Cohort Hip and Cohort Knee (CHECK). The CT slices were sequentially summed to create radiograph-like 2-D images named CT-AP. X-ray and CT-AP images were classified as rHOA if they had osteoarthritic changes corresponding to Kellgren-Lawrence grade 2 or higher. The study data was split into 55% training, 30% validation, and 15% test sets. A pretrained ResNet18 was optimized for a classification task of rHOA vs. no-rHOA. Five models were trained using (1) X-rays, (2) downsampled X-rays, (3) combination of CT-AP and X-ray images, (4) combination of CT-AP and downsampled X-ray images, and (5) CT-AP images. Results: Amongst the five models, Model-3 and Model-5 performed best in detecting rHOA from the CT-AP images. Model-3 detected rHOA on the test set of CT-AP images with a balanced accuracy of 82.2% and was able to discriminate rHOA from no-rHOA with an area under the receiver operating characteristic curve (ROC AUC) of 0.93 [0.75–0.99]. Model-5 detected rHOA on the test set at a balanced accuracy of 82.2% and classified rHOA from no-rHOA with an ROC AUC of 0.89 [0.67–0.97]. Conclusion: CT-based summation images that resemble radiographs can be used to detect rHOA. In addition, in the absence of large training data, a reliable DL model can be optimized by combining CT-AP and X-ray images.

Published
2022

21. Osteoarthritis year in review 2021:imaging

Author

Oei, E. H.G., Hirvasniemi, J., van Zadelhoff, T. A., van der Heijden, R. A., Oei, E. H.G., Hirvasniemi, J., van Zadelhoff, T. A., and van der Heijden, R. A.

Abstract

Purpose: To provide a narrative review of original articles on imaging of osteoarthritis (OA) published between January 1, 2020 and March 31, 2021, with a special focus on imaging of inflammation, imaging of bone, cartilage and bone-cartilage interactions, imaging of peri-articular tissues, imaging scoring methods for OA, and artificial intelligence (AI) applied to OA imaging. Methods: The Embase, Pubmed, Medline, Cochrane databases were searched for original research articles in the English language on human, in vivo, imaging of OA published between January 1, 2020 and March 31, 2021. Search terms related to osteoarthritis combined with all imaging modalities and artificial intelligence were applied. A selection of articles reporting on one of the focus topics was discussed further. Results: The search resulted in 651 articles, of which 214 were deemed relevant to human OA imaging. Among the articles included, the knee joint (69%) and magnetic resonance imaging (MRI) (52%) were the predominant anatomical area and imaging modality studied. There were also a substantial number of papers (n = 46) reporting on AI applications in the field of OA imaging. Conclusion: Imaging continues to play an important role in the assessment of OA. Recent advances in OA imaging include quantitative, non-contrast, and hybrid imaging techniques for improved characterization of multiple tissue processes in OA. In addition, an increasing effort in AI techniques is undertaken to enhance OA imaging acquisition and analysis.

Published
2022

23. Monkeys in Measles Research

Author

Van Binnendijk, R. S., Van Der Heijden, R. W. J., Osterhaus, A. D. M. E., Capron, A., editor, Compans, R. W., editor, Cooper, M., editor, Koprowski, H., editor, McConnell, I., editor, Melchers, F., editor, Oldstone, M., editor, Olsnes, S., editor, Potter, M., editor, Saedler, H., editor, Vogt, P. K., editor, Wagner, H., editor, Wilson, I., editor, ter Meulen, Volker, editor, and Billeter, Martin A., editor

Published
1995
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27. Prevalence of small osteophytes on knee MRI in several large clinical and population-based studies of various age groups and OA risk factors

Author

De Kanter, J. L.M., Oei, E. H.G., Schiphof, D., Van Meer, B. L., Van Middelkoop, M., Reijman, M., Bierma-Zeinstra, S. M.A., Runhaar, J., Van der Heijden, R. A., De Kanter, J. L.M., Oei, E. H.G., Schiphof, D., Van Meer, B. L., Van Middelkoop, M., Reijman, M., Bierma-Zeinstra, S. M.A., Runhaar, J., and Van der Heijden, R. A.

Abstract

Objective: Osteophytes, also small ones, are an important imaging feature of OA. However, due to their high prevalence on MR, the question has arisen whether these are truly pathophysiologic features of early OA, a result of physiologic aging, or rather a merely transient phenomenon. The aim of this study was to explore the prevalence of osteophytes on MR in various locations of the knee, with special emphasis on small osteophytes, across multiple large studies conducted in our institution comprising a wide range of subjects at different ages. Method: Retrospective explorative study of the prevalence of osteophytes, particularly grade 1 according to MOAKS, among four studies with a wide variety in age and OA risk factors. Results: A large number of grade 1 osteophytes were found in all four studies. The largest number of osteophytes were present in the youngest age group of <30 years (69.6%) compared to 36.8% in the age group of ≥30 ​< ​50 years and 54,3% when aged ≥50 years, of which most were grade 1 osteophytes. Conclusion: Small osteophytes are highly prevalent among populations with varying age and OA risk factors, in particular among young subjects without other OA features. This might suggest that these “osteophytes” do not necessarily represent early OA, but rather indicate a transient physiologic phenomenon.

Published
2021

34. Scalable wafer-to-fiber transfer method for lab-on-fiber sensing

Author

Picelli, L., primary, van Klinken, A., additional, Lindgren, G., additional, Hakkel, K. D., additional, Pagliano, F., additional, Fiaschi, N., additional, Sersic-Vollenbroek, I., additional, van Veldhoven, P. J., additional, van der Heijden, R. W., additional, and Fiore, A., additional

Published
2020
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42. Abstracts of papers and posters 19th LOF-Symposium on Pharmacognosy and Natural Products Chemistry

Author

Anton, R., Meier, B., Bauer, R., Evans, Fred J., Uehleke, B., Moreno, Paulo R. H., Verpoorte, Robert, Gerardi, C., Bonqaerts, R. J. M., Hoge, J. H. C., Andavani, R., van der Heijden, R., Duine, J. A., Schulte, A. E., Horsten, S. F. A. J., van den Berg, A. J. J., Kettenes-van den Bosch, J. J., Beukelman, C. J., Kroes, B. H., van Dijk, H., Labadie, R. P., De Groot, A., Marek, R., Dommisse, R., Lemière, G., Lepoivre, J., Boers, Frank, Lemière, Guy, Lepoivre, J. Arsène, De Groot, Alex, Suys, D., Lepoivre, J. A., Lemière, G., Li, O. M., Van den Heuvel, H., Shahat, A., Pieters, L. A. C., Vlietinck, A. J., Claeys, M., Woerdenbag, H. J., Merfort, I., Paßreiter, C. M., Schmidt, Th. J., Willuhn, G., Kampinga, H. H., Konings, A. W. T., Hu, J. P., Calomme, M., Van Poel, B., Pieters, L., Vlietinck, A., Vanden Berghe, D. A., Cimanga, K., Lasure, A., De Bruyne, T., Pieters, L., Vanden Berghe, D., Vlientinck, A. J., Dochez, C., Francois, G., Timperman, G., De Bruvne, T., Hammouda, F. M., Ismail, S. I., Azzam, A., Shahat, A. A., DeBruvne, T., Azzam, S., Huang, Y., Berghe, D. Vanden, de Boer, R. L. Tinberaen, van Ufford, H. C. Quarles, Thung, W. M. L., Samel, D., de Witte, P., Vandenbogaerde, A., Hendriks, H., Bos, R., De Cooman, L., Everaert, E., De Keukeleire, D., Schmidt, T. J., Alfermann, A. W., Jaziri, M., Vanhaelen-Fastré, R., Diallo, B., Guo, Y., Homès, J., Vanhaelen, M., Verstraeten, V., Baas, Wim J., Sindambiwe, J. B., Vanden Berghe, P., Quetin-Leclercq, J., Favel, A., Balansard, G., Regli, P., Angenot, L., Kanyinda, B., Vanhaelen-Faslré, R., Ossor, A., van Hulle, C., Nash, R. J., Cao, Y., Neef, H., Declercq, P., Laekeman, G., Van Puyvelde, L., Van Damme, P. L. J., Van den Evnden, V., and De Wolf, J.

Published
1994
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43. Stikstof uit de stal strippen : de technische middelen om uitstoot te verminderen zijn er

Author

Heijden, R. van der and Heijden, R. van der

Abstract

Ondanks een spectaculaire daling van de uitstoot van ammoniak uit de landbouw in de afgelopen dertig jaar moet die verder omlaag. Van het koetoilet tot stikstofstrippers, de technische middelen om dat te doen lijken voorhanden. Welke oplossingen zijn volgens een wetenschapper en een boer levensvatbaar in de praktijk?

Published
2020

44. Abstracts of posters

Author

Arroo, R. R. J., de Brouwer, A. P. M., Jacobs, J. J. M. R., Croes, A. F., Wullems, G. J., Bassetti, Lucilla, Tramper, Johannes, Beekman, A. C., Woerdenbaq, H. J., Kampinga, H. H., Konings, A. W. T., Beukelman, C. J., Kroes, B. H., Aarts, W. H. C., van den Berg, A. J. J., Labadie, R. P., Boers, Frank, Lemière, Guy, Lepoivre, J. Arsène, De Groot, Alex, Boersma, H. H., Woerdenbag, H. J., Bauer, J., Scheithauer, W., Kamoinga, H. H., Bos, R., Scheffer, J. J. C., Carrier, J., van der Heijden, R., Verpoorte, R., Cimanga, K., De Bruyne, T., Lasure, A., Van Poel, B., Pieters, L., Vanden Berghe, D., Vlietinck, A. J., De Keukeleire, Denis, Wann, Xuan, Fung, S. Y., Zuurbier, K. W. M., Gao, M., Lemière, G., De Groot, A., Dommisse, R., Lepoivre, J., Hermans-Lokkerbol, A. C. J., Hisham, A., Sreekala, U., Van den Heuvel, H., Kosasi, S., Bolland, P., Wetering, C. v. d., Drost, R., Verrips, A., van Hall, J., van Ufford, H. C. Quarles, Luijendijk, T. J. C., Manns, Detief, Moreno, Paulo R. H., van der Heijden, Robert, Verpoorte, Robert, Mosen, Wolfgang, Zdunek, Katja, Woerdenbag, Herman J., Pras, Niesko, Alfermann, A. Wilhelm, Neef, H., Deckmijn, H., De Revs, S., De Clerck, P., Laekeman, G., De Clercq, P., Nijs, G., Spiessens, C., da Witte, P., Geboes, K., PaBreiter, C. M., Wendisch, D., Gondol, D., Schlatmann, J. E., Moreno, P. R. H., Hoopen, H. J. G. Ien, Heijnen, J. J., ten Hoopen, H. J. G., Schulte, A. E., Nasiri, A., Siwon, J., Smit, H. F., Singh, R. H., Meulenbeld, G. J., Zwaving, J. H., Horsten, S. F. A. J., Kettenes- van den Bosch, J. J., Vandenbooaerde, A., de Witte, P., van den Dungen, F. M., Souren, J. E. M., van Wijk, R., Van den Eeckhout, E. G., Van Per Mijnsbrugge, K., Bauw, G., Inze, D., Van Montagu, M., Esmans, E., van der Heiiden, R., Duine, J. A., van Uden, W., Oeij, K. Holidi, Pras, N., van Dijken, A. M. A., Woerdenbao, H. J., Wildeboer, M., Hendriks, H., Bootsma, H. P. R., de Weerdt, C. J., Merfort, I., Paßreiter, CM., Schmidt, T. J., Willuhn, G., Chan, Nguyen Gia, Bang, Bui Thi, Uden, W. van, Van Lpham Y., Van Boi, Nguyen, Batterman, S., and Lugt, C. B.

Published
1993
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46. Cut your energy bills: a European olefins plant used tailor-made, integrated solutions that reduced energy costs with minimal capital expenditure

Author

van der Heijden, R. and Thompson, I.

Subjects
Shell Chemical Co. -- Energy use, Shell Chemical Co. -- Management, Chemical industry -- Energy use, Chemical industry -- Management, Herbicides -- Energy use, Herbicides -- Management, Pesticides industry -- Energy use, Pesticides industry -- Management, Company business management, Business, Petroleum, energy and mining industries
Abstract

Shell Chemical Companies (Shell Chemicals) use efficient cutting-edge technologies in their European petrochemical plants. However, improving the energy efficiency at one site in particular to keep it competitive became a [...]

Published
2005

47. Effect of Sitagliptin on Cardiovascular Outcomes in Type 2 Diabetes

Author

Green JB, Bethel MA, Armstrong PW, Buse JB, Engel SS, Garg J, Josse R, Kaufman KD, Koglin J, Korn S, Lachin JM, McGuire DK, Pencina MJ, Standl E, Stein PP, Suryawanshi S, Van de Werf F, Peterson ED, Holman RR, Josse RG, Califf RM, Goldstein BJ, Shapiro DR, Silverman R, Bethel A, Green J, Hayden S, Hannan K, Quintero K, Rorick T, Berdan L, Leloudis D, Califf S, Wilson M, McFarron D, Trollinger K, Pesarchick J, Eskenazi L, Campbell C, Townes O, Tolsma D, Keenan J, Milton J, Athwal R, Darbyshire J, Doran Z, Kennedy I, Gregory V, Lokhnygina Y, Prather K, Wolfley A, Usman M, Tajjar A, Gray R, Pfeffer MA, Gerstein HC, Groop L, McMurray JJ, Pocock SJ, Clayton T, Sinay I, Brieger D, Stranks S, Scheen A, Lopes R, Tankova T, Hramiak I, Grado CR, Wenying Y, Ge J, Aschner P, Skrha J, Ambos A, Strandberg T, Travert F, Hanefeld M, Riefflin A, Chan JC, Ofner P, Reddy NK, Christopher J, Mathur A, Arambam P, Mittal S, Manchanda M, Wainstein J, Ambrosio G, Pirags V, Jakuboniene N, Mohamed M, Scott R, White H, Cornel J, Halvorsen S, Tykarski A, Veresiu IA, Dreval AV, Misinkova I, Tai E, Krahulec B, Distiller L, Park Y, Rovira A, Alversson M, Chuang LM, Delibasi T, Adler A, Rodbard HW, Marre M, Goff D, Chacra A, DeVore A, Beaven A, Shah B, Hirsch B, Batch B, Bushnell C, Patel C, Melloni C, Henshaw C, Kong D, Bernecki G, Tillman H, Kang HJ, Hawes J, Strickler J, Piccini J, Wilder J, Alexander K, Mahaffey K, Patel K, Hyland K, Newby K, Jackson L, Cooper L, Armaganijan L, Szczeh L, Koshizaka M, Roe M, Morse M, Guimaraes P, Hess P, Tricoci P, Mehta R, Mathews R, Kociol R, Harrison R, Mentz R, Pokorney S, Leblanc T, Lazzarini V, Eapen Z, Truffa A, Fosbol E, Brito F, Katz M, Bahit M, Santos M, Barros P, Bernardez S, Alvarisqueta AF, Arias P, Cagide AL, Calella PR, Cantero MC, Canella JP, Cipullo MA, de Loredo L, Gelersztein ES, Gorban de Lapertosa SB, Klyver MI, Maffei LE, Maldonado N, Oviedo AI, Piskorz DL, Ridruejo MC, Saavedra SS, Sessa HA, Sinay IR, Sposetti GD, Ulla MR, Vico ML, Waitman JN, Binnekamp M, Carroll P, Cheung W, Colman P, Davis T, De Looze F, dEmden M, Fulcher G, Gerstman M, Hamilton A, Lehman S, Moses R, Proietto J, Roberts A, Shaw J, Simpson R, Sinha A, Tan Y, Topliss D, Vora P, Waites J, Crenier L, Descamps O, Keymeulen B, Mathieu C, Nobels F, Van den Bruel A, Van Gaal L, Borges JL, Costa e Forti A, Eliaschewitz FG, Felício JS, Griz LH, Hissa MN, Leite S, Panarotto D, Pimentel Filho P, Rassi N, Saraiva JK, Sgarbi JA, Silva RP, Tambascia M, Weber Silva DM, Bobeva R, Bostandzhieva R, Cinlikov I, Georgieva M, Iliev D, Ilieva E, Kovacheva S, Liubenova L, Nikitov Z, SHeinikova G, Slavcheva A, Spasova V, Temelkova-Kurktschiev T, Velichka D, Yakov A, Carpentier A, Chiasson JL, Constance C, Dumas R, Filteau P, Garceau C, Huynh T, Kaiser S, Kornder J, Leiter L, Mereu L, Miller D, Pandey S, Punthakee Z, Rabasa-Lhoret R, Robitaille Y, Saunders K, Sigal R, Sigalas J, Vizel S, Weisnagel S, Woo V, Yale JF, Yared K, Zinman B, Bunster Balocchi LB, Escobar Cerda EE, Garces Flores EE, Lanas Zanetti FT, Larrazabal Miranda Adel P, Morales Alvarado JM, Olivares Cañon CM, Potthoff Cárdenas SH, Raffo Grado CA, Rodriguez Venegas ME, Saavedra Gajardo VA, Westerberg Maldonado BH, Chen LL, Dong J, Guo X, Li QM, Shi B, Tang XL, Yang T, Yang WY, Zheng SX, Aschner Montoya P, Botero Lopez R, Coronel Arroyo JA, Cure CA, Gómez Medina AM, Molina DI, Perez Amador GA, Reyes Rincon A, Urina Triana MA, Valenzuela Rincon A, Vélez Pelaez S, Yupanqui Lozno H, Brabec T, Brychta T, Hasalova Zapletalova J, Havelkova J, Hejnicova K, Hola O, Hornackova M, Hrdina T, Kafkova D, Kellnerova I, Krystl T, Kutejova V, Mikulkova I, Nevrla J, Pantlikova C, Petr M, Racicka E, Sarbochova R, Smolenakova K, Turcinek R, Urbancova K, Vejvodova J, Vondrakova M, Zachoval R, Alt I, Kaasik Ü, Kiiroja K, Lanno R, Märtsin K, Past M, Vides H, Viitas L, Kantola I, Nieminen S, Perhonen M, Strand J, Valle T, Clergeot A, Couffinhal T, Courreges JP, Gouet D, Moulin P, Ziegler O, Badenhoop K, Behnke T, Bender G, Braun M, Dshabrailov J, Hamann A, Himpel-Boenninghoff A, Kamke W, Kasperk C, Luedemann J, Mayr P, Merkel M, Oerter EM, Ohlow MA, Ott P, Overhoff U, Paschen B, Remppis R, Rose L, Schumm-Draeger PM, Segiet T, Strotmann HJ, Stuchlik G, Stürmer W, Thinesse-Mallwitz M, Tytko A, Wendisch U, Wurziger J, Ho AY, Kam G, Kong AP, Lam YY, Lau EY, Lee S, Siu SC, Tomlinson B, Tsang CC, Yeung VT, Dezső E, Dudás M, Földesi I, Fülöp T, Késmárki N, Koranyi L, Nagy K, Oroszlán T, Pécsvárady Z, Ples Z, Taller A, Agarwal P, Ambulkar S, Aravind S, Balaji V, Kalra S, Kesavadev J, Kudalkar H, Kumar A, Misra A, Mithal A, Mohan V, Pitale S, Ramu M, Reddy N, Shah S, Shamanna P, Sharda A, Sharma A, Shunmugavelu M, Srikanta S, Suryaprakash G, Abramov G, Adawi F, Bashkin A, Darawsha M, Fuchs S, Harman-Boehm I, Hayek T, Jaffe A, Knobler H, Minuchin O, Mosseri M, Shechter M, Shimon I, Stern N, Tsur A, Vishlitzky V, Alfonsi F, Cavalot F, Del Vecchio L, Frisinghelli A, Gambardella S, Lauro D, Lembo G, Leotta S, Mondillo S, Novo S, Pedrinelli R, Piatti P, Salvioni A, Tritto I, Zavaroni DZ, Ahn KJ, Choi KM, Chung C, Han SJ, Kim DM, Kim IJ, Kim MH, Lee IK, Nam M, Park IeB, Park KS, Park TS, Rhee EJ, Yoo SJ, Andersone I, Balode A, Eglite R, Gersamija A, Kakurina N, Jegere B, Leitane I, Pastare S, Stalte V, Teterovska D, Baltramonaitiene K, Barsiene L, Ceponis J, Lasiene J, Levinger A, Sirutaviciene A, Sulskiene M, Urbanaviciene L, Valius L, Varanauskiene E, Velickiene D, Mahendran KA, Abu Hassan MR, Aziz NA, Hussein Z, Ismail IS, Kamaruddin NA, Nordin Z, Nayar SK, Ramanathan GR, Sothiratnam R, Beijerbacht H, Breedveld R, Cornel JH, Den Hartog F, Hermans W, Kietselaer B, Kooy A, Lenderink T, Nierop P, Remmen J, Rojas Lingan G, Ronner E, Van der Heijden R, Van Hessen M, van Kempen W, Voors-Pette C, Westendorp I, Baker J, Benatar J, Cutfield R, Krebs J, Leikis R, Lunt H, Manning P, Williams M, Birkeland K, Claudi T, Istad H, Karlsson T, Ossum Gronert J, Arciszewska M, Artemiuk E, Blach E, Blicharski T, Cypryk K, Dabrowska M, Górny G, Górska M, Jakubowska I, Jazwinska-Tarnawska E, Karczmarczyk A, Kitowska-Koterla J, Koltowski L, Krzyzagorska E, Pasternak D, Pentela-Nowicka J, Piesiewicz W, Przekwas-Jaruchowska M, Rajzer M, Salamon-Ferenc A, Sawicki A, Skowron T, Śmiałowski A, Albota A, Alexandru C, Crisan C, Dumitrescu A, Ferariu IE, Lupusoru DA, Munteanu M, Negru D, Nicolau A, Pintiliei E, Popescu A, Serban G, Voitec M, Babenko A, Barbarash O, Bondar I, Chizhov P, Demin A, Dora S, Dreval A, Ershova O, Gratsiansky N, Ketova G, Kotelnikov M, Levashov S, Morugova T, Mustafina S, Pekarskiy S, Raskina T, Rechkova E, Samoylova Y, Sazonova O, Sherenkov A, Shilkina N, Stetsyuk O, Tretyakova T, Turova E, Valeeva F, Zadionchenko V, Dalan R, Tan RS, Tay L, Buganova I, Fabry J, Jan C, Toserova E, Zak R, Zimanova J, Badat A, Bester F, Burgess L, De Jong D, Ellis G, Fouche L, Govender P, Govind U, Naidoo V, Nieuwoudt G, Nortje H, Rheeder P, Robertson L, Siddique N, Stapelberg AM, Trinder Y, Van Der Merwe A, Van Zyl L, Viljoen M, Wilhase A, Botella M, Civeira Murillo F, de Teresa L, Del Cañizo FJ, Extremera BG, Gimeno EJ, Martin-Hidalgo A, Morales C, Nubiola A, Tinahones Madueño F, Tranche S, Trescolí Serrano C, Alvarsson M, Eizyk E, Gillblad A, Johansson P, Löndahl M, Ohlsson-Önerud Å, Rautio A, Sundström U, Torstensson I, Chen JF, Chou CW, Ho LT, Hsieh IC, Huang BH, Huang CL, Huang CN, Lai WT, Lo PH, Pei D, Sheu WH, Wang SY, Araz M, Bakiner O, Comlekci A, Guler S, Sahin I, Sarac F, Tarkun I, Ukinc K, Yilmaz M, Abdulhakim E, Abraham P, Adamson K, Blagden M, Bundy C, Daly M, Davies M, Deshpande M, Gillings S, Harvey P, Horvathova V, Hristova D, Jaap A, Johnson A, Jones H, Kerrane J, Kilvert A, Ko T, Kumar J, Lindsay R, Litchfield J, McCrimmon R, McKnight J, Millward B, Oyesile B, Purewal T, Ravikumar C, Robinson A, Sathyapalan T, Simpson H, Thomas H, Turner W, Weaver J, Wilding J, Wiles P, Adkins K, Akpunonu B, Albu J, Anagnostis G, Anastasi L, Argoud G, Aroda V, Azizad M, Banerji MA, Bartkowiak A Jr, Bays H, Behn P, Bergenstal R, Bhargava A, Bias D, Bolster E, Buchanan P, Busch R, Chadha C, Chang M, Cheng C, Cohen A, Cohen J, Cole B, Connery L, Cooperman M, Cushman W, DAgostino R, Dayamani P, De Lemos J, De Meireles M, Dean J, DeHart D, Detweiler R, Donovan D, Dugano-Daphnis P, Dulin M, Dunn F, Eaton C, Erickson B, Estevez R, Feinglos M, Fonseca V, Force R, Forker A, Fox D, Gabriel J, Garcia R, Garvey T, Gaudiani L, Getaneh A, Goldberg A, Goldman S, Hairston K, Harris R, Haught W, Hidalgo H Jr, Higgins A, Houchin V, Ison R, Jacobs G, Jaffrani N, Jafry B, Kapsner P, Kaye W, Labroo A, Levinson L, Lewis S, Lillestol M, Luttrell L, Madu I, McNeill R, Merrick B, Metzger F, Nadar V, Nagelberg S, Nash S, Oparil S, Osei K, Papademetriou V, Patel N, Pedley C, Prentiss A, Radbill M, Raisinghani A, Rassouli N, Reddy R, Rees P, Rendell M, Robbins D, Rodbard H, Rohlf J, Roseman H, Rudolph L, Sadler L, Schnall A, Schramm R, Schubart U, Seneviratne T, Shanik M, Snyder H, Sorli C, Stich M, Sweeney ME, Tsao J, Ukwade P, Viswanath D, Vo A, Vogel C, Voyce S, Weintraub H, White J, Wood M, Wu P, Wysham C, Zimmerman R, Pathology/molecular and cellular medicine, Diabetes Pathology & Therapy, and Green JB, Bethel MA, Armstrong PW, Buse JB, Engel SS, Garg J, Josse R, Kaufman KD, Koglin J, Korn S, Lachin JM, McGuire DK, Pencina MJ, Standl E, Stein PP, Suryawanshi S, Van de Werf F, Peterson ED, Holman RR, Holman RR, Peterson ED, Holman RR, Peterson ED, Armstrong PW, Buse JB, Josse RG, Kaufman KD, Koglin J, Korn S, Lachin JM, McGuire DK, Standl E, Stein PP, Suryawanshi S, Van de Werf F, Engel SS, Califf RM, Goldstein BJ, Shapiro DR, Silverman R, Bethel A, Green J, Hayden S, Hannan K, Quintero K, Rorick T, Berdan L, Leloudis D, Califf S, Wilson M, McFarron D, Trollinger K, Pesarchick J, Eskenazi L, Campbell C, Townes O, Tolsma D, Keenan J, Milton J, Athwal R, Darbyshire J, Doran Z, Kennedy I, Gregory V, Garg J, Lokhnygina Y, Prather K, Wolfley A, Usman M, Tajjar A, Gray R, Pfeffer MA, Gerstein HC, Groop L, McMurray JJ, Pocock SJ, Clayton T, Sinay I, Brieger D, Stranks S, Scheen A, Lopes R, Tankova T, Hramiak I, Grado CR, Wenying Y, Ge J, Aschner P, Skrha J, Ambos A, Strandberg T, Travert F, Hanefeld M, Riefflin A, Chan JC, Ofner P, Reddy NK, Christopher J, Mathur A, Arambam P, Mittal S, Manchanda M, Wainstein J, Ambrosio G, Pirags V, Jakuboniene N, Mohamed M, Scott R, White H, Cornel J, Halvorsen S, Tykarski A, Veresiu IA, Dreval AV, Misinkova I, Tai E, Krahulec B, Distiller L, Park Y, Rovira A, Alversson M, Chuang LM, Delibasi T, Adler A, Rodbard HW, Marre M, Goff D, Chacra A, DeVore A, Beaven A, Shah B, Hirsch B, Batch B, Bushnell C, Patel C, Melloni C, Henshaw C, Kong D, McFarron D, Bernecki G, Tillman H, Kang HJ, Green J, Hawes J, Strickler J, Piccini J, Wilder J, Alexander K, Mahaffey K, Patel K, Hyland K, Newby K, Jackson L, Cooper L, Armaganijan L, Szczeh L, Koshizaka M, Roe M, Morse M, Guimaraes P, Hess P, Tricoci P, Mehta R, Lopes R, Mathews R, Kociol R, Harrison R, Mentz R, Pokorney S, Leblanc T, Lazzarini V, Eapen Z, Truffa A, Fosbol E, Brito F, Katz M, Bahit M, Santos M, Barros P, Bernardez S, Alvarisqueta AF, Arias P, Cagide AL, Calella PR, Cantero MC, Canella JP, Cipullo MA, de Loredo L, Gelersztein ES, Gorban de Lapertosa SB, Klyver MI, Maffei LE, Maldonado N, Oviedo AI, Piskorz DL, Ridruejo MC, Saavedra SS, Sessa HA, Sinay IR, Sposetti GD, Ulla MR, Vico ML, Waitman JN, Binnekamp M, Carroll P, Cheung W, Colman P, Davis T, De Looze F, dEmden M, Fulcher G, Gerstman M, Hamilton A, Lehman S, Moses R, Proietto J, Roberts A, Shaw J, Simpson R, Sinha A, Stranks S, Tan Y, Topliss D, Vora P, Waites J, Crenier L, Descamps O, Keymeulen B, Mathieu C, Nobels F, Scheen A, Van den Bruel A, Van Gaal L, Borges JL, Costa e Forti A, Eliaschewitz FG, Felício JS, Griz LH, Hissa MN, Leite S, Panarotto D, Pimentel Filho P, Rassi N, Saraiva JK, Sgarbi JA, Silva RP, Tambascia M, Weber Silva DM, Bobeva R, Bostandzhieva R, Cinlikov I, Georgieva M, Iliev D, Ilieva E, Kovacheva S, Liubenova L, Nikitov Z, SHeinikova G, Slavcheva A, Spasova V, Tankova T, Temelkova-Kurktschiev T, Velichka D, Yakov A, Carpentier A, Chiasson JL, Constance C, Dumas R, Filteau P, Garceau C, Hramiak I, Huynh T, Kaiser S, Kornder J, Leiter L, Mereu L, Miller D, Pandey S, Punthakee Z, Rabasa-Lhoret R, Robitaille Y, Saunders K, Sigal R, Sigalas J, Vizel S, Weisnagel S, Woo V, Yale JF, Yared K, Zinman B, Bunster Balocchi LB, Escobar Cerda EE, Garces Flores EE, Lanas Zanetti FT, Larrazabal Miranda Adel P, Morales Alvarado JM, Olivares Cañon CM, Potthoff Cárdenas SH, Raffo Grado CA, Rodriguez Venegas ME, Saavedra Gajardo VA, Westerberg Maldonado BH, Chen LL, Dong J, Guo X, Li QM, Shi B, Tang XL, Yang T, Yang WY, Zheng SX, Aschner Montoya P, Botero Lopez R, Coronel Arroyo JA, Cure CA, Gómez Medina AM, Molina DI, Perez Amador GA, Reyes Rincon A, Urina Triana MA, Valenzuela Rincon A, Vélez Pelaez S, Yupanqui Lozno H, Brabec T, Brychta T, Hasalova Zapletalova J, Havelkova J, Hejnicova K, Hola O, Hornackova M, Hrdina T, Kafkova D, Kellnerova I, Krystl T, Kutejova V, Mikulkova I, Nevrla J, Pantlikova C, Petr M, Racicka E, Sarbochova R, Skrha J, Smolenakova K, Turcinek R, Urbancova K, Vejvodova J, Vondrakova M, Zachoval R, Alt I, Ambos A, Kaasik Ü, Kiiroja K, Lanno R, Märtsin K, Past M, Vides H, Viitas L, Kantola I, Nieminen S, Perhonen M, Strand J, Strandberg T, Valle T, Clergeot A, Couffinhal T, Courreges JP, Gouet D, Moulin P, Travert F, Ziegler O, Badenhoop K, Behnke T, Bender G, Braun M, Dshabrailov J, Hamann A, Hanefeld M, Himpel-Boenninghoff A, Kamke W, Kasperk C, Luedemann J, Mayr P, Merkel M, Oerter EM, Ohlow MA, Ott P, Overhoff U, Paschen B, Remppis R, Riefflin A, Rose L, Schumm-Draeger PM, Segiet T, Strotmann HJ, Stuchlik G, Stürmer W, Thinesse-Mallwitz M, Tytko A, Wendisch U, Wurziger J, Ho AY, Kam G, Kong AP, Lam YY, Lau EY, Lee S, Siu SC, Tomlinson B, Tsang CC, Yeung VT, Dezső E, Dudás M, 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Jakuboniene N, Lasiene J, Levinger A, Sirutaviciene A, Sulskiene M, Urbanaviciene L, Valius L, Varanauskiene E, Velickiene D, Mahendran KA, Abu Hassan MR, Aziz NA, Hussein Z, Ismail IS, Kamaruddin NA, Mohamed M, Nordin Z, Nayar SK, Ramanathan GR, Sothiratnam R, Beijerbacht H, Breedveld R, Cornel JH, Den Hartog F, Hermans W, Kietselaer B, Kooy A, Lenderink T, Nierop P, Remmen J, Rojas Lingan G, Ronner E, Van der Heijden R, Van Hessen M, van Kempen W, Voors-Pette C, Westendorp I, Baker J, Benatar J, Cutfield R, Krebs J, Leikis R, Lunt H, Manning P, Scott R, Williams M, Birkeland K, Claudi T, Halvorsen S, Istad H, Karlsson T, Ossum Gronert J, Arciszewska M, Artemiuk E, Blach E, Blicharski T, Cypryk K, Dabrowska M, Górny G, Górska M, Jakubowska I, Jazwinska-Tarnawska E, Karczmarczyk A, Kitowska-Koterla J, Koltowski L, Krzyzagorska E, Pasternak D, Pentela-Nowicka J, Piesiewicz W, Przekwas-Jaruchowska M, Rajzer M, Salamon-Ferenc A, Sawicki A, Skowron T, Śmiałowski A, Tykarski A, Albota A, 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Subjects
Oral, medicine.medical_specialty, Heart diseases, Glycosylated, Administration, Oral, heart failure, Type 2 diabetes, Dipeptidyl peptidase-4 inhibitor, Kaplan-Meier Estimate, Placebo, Sitagliptin Phosphate, Sitagliptin, Cardiovascular Outcomes, chemistry.chemical_compound, Drug Therapy, Double-Blind Method, Internal medicine, Diabetes Mellitus, medicine, Humans, Hypoglycemic Agents, Glycated Hemoglobin, Hemoglobin A, Glycosylated, Cardiovascular Diseases, Diabetes Mellitus, Type 2, Drug Therapy, Combination, Follow-Up Studies, Heart Diseases, Heart Failure, Hospitalization, Pyrazines, Triazoles, Medicine (all), business.industry, Semaglutide, Hemoglobin A, General Medicine, ta3121, medicine.disease, Surgery, Cardiovascular diseases, chemistry, Sitagliptin, Administration, Combination, Glycated hemoglobin, business, Type 2, Alogliptin, medicine.drug
Abstract

BACKGROUND: Data are lacking on the long-term effect on cardiovascular events of adding sitagliptin, a dipeptidyl peptidase 4 inhibitor, to usual care in patients with type 2 diabetes and cardiovascular disease. METHODS: In this randomized, double-blind study, we assigned 14,671 patients to add either sitagliptin or placebo to their existing therapy. Open-label use of antihyperglycemic therapy was encouraged as required, aimed at reaching individually appropriate glycemic targets in all patients. To determine whether sitagliptin was noninferior to placebo, we used a relative risk of 1.3 as the marginal upper boundary. The primary cardiovascular outcome was a composite of cardiovascular death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for unstable angina. RESULTS: During a median follow-up of 3.0 years, there was a small difference in glycated hemoglobin levels (least-squares mean difference for sitagliptin vs. placebo, -0.29 percentage points; 95% confidence interval [CI], -0.32 to-0.27). Overall, the primary outcome occurred in 839 patients in the sitagliptin group (11.4%; 4.06 per 100 person-years) and 851 patients in the placebo group (11.6%; 4.17 per 100 person-years). Sitagliptin was noninferior to placebo for the primary composite cardiovascular outcome (hazard ratio, 0.98; 95% CI, 0.88 to 1.09; P

Published
2015
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