The Experts below are selected from a list of 49878 Experts worldwide ranked by ideXlab platform
Matthijs D Kruizinga - One of the best experts on this subject based on the ideXlab platform.
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saliva as a Sampling Matrix for therapeutic drug monitoring of gentamicin in neonates a prospective population pharmacokinetic and simulation study
British Journal of Clinical Pharmacology, 2021Co-Authors: Amadou Samb, Matthijs D Kruizinga, Gertjan J Driessen, Michiel J Van Esdonk, Younes Tallahi, Willemijn Van Heel, Yuma A Bijleveld, Rik StuurmanAbstract:INTRODUCTION Therapeutic drug monitoring (TDM) of gentamicin in neonates is recommended for safe and effective dosing and is currently performed by plasma Sampling, which is an invasive and painful procedure. In this study, feasibility of a non-invasive gentamicin TDM strategy using saliva was investigated. METHODS This was a multicenter, prospective, observational cohort study including 54 neonates. Any neonate treated with intravenous gentamicin was eligible for the study. Up to 8 saliva samples were collected per patient at different time-points. Gentamicin levels in saliva were determined with liquid chromatography tandem mass-spectrometry. A population pharmacokinetic (PK) model was developed using Nonlinear Mixed-Effects Modeling (NONMEM) to describe the relation between gentamicin concentrations in saliva and plasma. Monte Carlo simulations with a representative virtual cohort (N=3000) were performed to evaluate the probability of target attainment with saliva- versus plasma TDM. RESULTS Plasma PK was adequately described with an earlier published model. An additional saliva compartment describing the salivary gentamicin concentrations was appended to the model with first-order input (k13 0.023 h-1 ) and first-order elimination (k30 0.169 h-1 ). Inter-individual variability of k30 was 38%. Postmenstrual age (PMA) correlated negatively with both k13 and k30 . Simulations demonstrated that TDM with 4 saliva samples was accurate in 81% of the simulated cases versus 94% when performed with 2 plasma samples and 87% when performed with 1 plasma sample. CONCLUSION TDM of gentamicin using saliva is feasible and the difference in precision between saliva and plasma TDM may not be clinically relevant, especially for premature neonates.
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theoretical performance of nonlinear mixed effect models incorporating saliva as an alternative Sampling Matrix for therapeutic drug monitoring in pediatrics a simulation study
Therapeutic Drug Monitoring, 2021Co-Authors: Matthijs D Kruizinga, Frederik E Stuurman, Gertjan J Driessen, A F Cohen, Kirsten R Bergmann, Michiel J Van EsdonkAbstract:Background Historically, pharmacokinetic (PK) studies and therapeutic drug monitoring (TDM) have relied on plasma as a Sampling Matrix. Noninvasive Sampling matrices, such as saliva, can reduce the burden on pediatric patients. The variable plasma-saliva relationship can be quantified using population PK models (nonlinear mixed-effect models). However, criteria regarding acceptable levels of variability in such models remain unclear. In this simulation study, the authors aimed to propose a saliva TDM evaluation framework and evaluate model requirements in the context of TDM, with gentamicin and lamotrigine as model compounds. Methods Two population pharmacokinetic models for gentamicin in neonates and lamotrigine in pediatrics were extended with a saliva compartment including a delay constant (kSALIVA), a saliva:plasma ratio, and between-subject variability (BSV) on both parameters. Subjects were simulated using a realistic covariate distribution. Bayesian maximum a posteriori TDM was applied to assess the performance of an increasing number of TDM saliva samples and varying levels of BSV and residual variability. Saliva TDM performance was compared with plasma TDM performance. The framework was applied to a known voriconazole saliva model as a case study. Results TDM performed using saliva resulted in higher target attainment than no TDM, and a residual proportional error 25% for gentamicin and >50% for lamotrigine reduced performance. The simulated target attainment for voriconazole saliva TDM was >90%. Conclusions Saliva as an alternative Matrix for noninvasive TDM is possible using nonlinear mixed-effect models combined with Bayesian optimization. This article provides a workflow to explore TDM performance for compounds measured in saliva and can be used for evaluation during model building.
Gertjan J Driessen - One of the best experts on this subject based on the ideXlab platform.
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saliva as a Sampling Matrix for therapeutic drug monitoring of gentamicin in neonates a prospective population pharmacokinetic and simulation study
British Journal of Clinical Pharmacology, 2021Co-Authors: Amadou Samb, Matthijs D Kruizinga, Gertjan J Driessen, Michiel J Van Esdonk, Younes Tallahi, Willemijn Van Heel, Yuma A Bijleveld, Rik StuurmanAbstract:INTRODUCTION Therapeutic drug monitoring (TDM) of gentamicin in neonates is recommended for safe and effective dosing and is currently performed by plasma Sampling, which is an invasive and painful procedure. In this study, feasibility of a non-invasive gentamicin TDM strategy using saliva was investigated. METHODS This was a multicenter, prospective, observational cohort study including 54 neonates. Any neonate treated with intravenous gentamicin was eligible for the study. Up to 8 saliva samples were collected per patient at different time-points. Gentamicin levels in saliva were determined with liquid chromatography tandem mass-spectrometry. A population pharmacokinetic (PK) model was developed using Nonlinear Mixed-Effects Modeling (NONMEM) to describe the relation between gentamicin concentrations in saliva and plasma. Monte Carlo simulations with a representative virtual cohort (N=3000) were performed to evaluate the probability of target attainment with saliva- versus plasma TDM. RESULTS Plasma PK was adequately described with an earlier published model. An additional saliva compartment describing the salivary gentamicin concentrations was appended to the model with first-order input (k13 0.023 h-1 ) and first-order elimination (k30 0.169 h-1 ). Inter-individual variability of k30 was 38%. Postmenstrual age (PMA) correlated negatively with both k13 and k30 . Simulations demonstrated that TDM with 4 saliva samples was accurate in 81% of the simulated cases versus 94% when performed with 2 plasma samples and 87% when performed with 1 plasma sample. CONCLUSION TDM of gentamicin using saliva is feasible and the difference in precision between saliva and plasma TDM may not be clinically relevant, especially for premature neonates.
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theoretical performance of nonlinear mixed effect models incorporating saliva as an alternative Sampling Matrix for therapeutic drug monitoring in pediatrics a simulation study
Therapeutic Drug Monitoring, 2021Co-Authors: Matthijs D Kruizinga, Frederik E Stuurman, Gertjan J Driessen, A F Cohen, Kirsten R Bergmann, Michiel J Van EsdonkAbstract:Background Historically, pharmacokinetic (PK) studies and therapeutic drug monitoring (TDM) have relied on plasma as a Sampling Matrix. Noninvasive Sampling matrices, such as saliva, can reduce the burden on pediatric patients. The variable plasma-saliva relationship can be quantified using population PK models (nonlinear mixed-effect models). However, criteria regarding acceptable levels of variability in such models remain unclear. In this simulation study, the authors aimed to propose a saliva TDM evaluation framework and evaluate model requirements in the context of TDM, with gentamicin and lamotrigine as model compounds. Methods Two population pharmacokinetic models for gentamicin in neonates and lamotrigine in pediatrics were extended with a saliva compartment including a delay constant (kSALIVA), a saliva:plasma ratio, and between-subject variability (BSV) on both parameters. Subjects were simulated using a realistic covariate distribution. Bayesian maximum a posteriori TDM was applied to assess the performance of an increasing number of TDM saliva samples and varying levels of BSV and residual variability. Saliva TDM performance was compared with plasma TDM performance. The framework was applied to a known voriconazole saliva model as a case study. Results TDM performed using saliva resulted in higher target attainment than no TDM, and a residual proportional error 25% for gentamicin and >50% for lamotrigine reduced performance. The simulated target attainment for voriconazole saliva TDM was >90%. Conclusions Saliva as an alternative Matrix for noninvasive TDM is possible using nonlinear mixed-effect models combined with Bayesian optimization. This article provides a workflow to explore TDM performance for compounds measured in saliva and can be used for evaluation during model building.
Debin Zhao - One of the best experts on this subject based on the ideXlab platform.
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image compressed sensing using convolutional neural network
IEEE Transactions on Image Processing, 2020Co-Authors: Wuzhen Shi, Feng Jiang, Shaohui Liu, Debin ZhaoAbstract:In the study of compressed sensing (CS), the two main challenges are the design of Sampling Matrix and the development of reconstruction method. On the one hand, the usually used random Sampling matrices (e.g., GRM) are signal independent, which ignore the characteristics of the signal. On the other hand, the state-of-the-art image CS methods (e.g., GSR and MH) achieve quite good performance, however with much higher computational complexity. To deal with the two challenges, we propose an image CS framework using convolutional neural network (dubbed CSNet) that includes a Sampling network and a reconstruction network, which are optimized jointly. The Sampling network adaptively learns the Sampling Matrix from the training images, which makes the CS measurements retain more image structural information for better reconstruction. Specifically, three types of Sampling matrices are learned, i.e., floating-point Matrix, {0, 1}-binary Matrix, and {−1, +1}-bipolar Matrix. The last two matrices are specially designed for easy storage and hardware implementation. The reconstruction network, which contains a linear initial reconstruction network and a non-linear deep reconstruction network, learns an end-to-end mapping between the CS measurements and the reconstructed images. Experimental results demonstrate that CSNet offers state-of-the-art reconstruction quality, while achieving fast running speed. In addition, CSNet with {0, 1}-binary Matrix, and {−1, +1}-bipolar Matrix gets comparable performance with the existing deep learning-based CS methods, outperforms the traditional CS methods. Experimental results further suggest that the learned Sampling matrices can improve the traditional image CS reconstruction methods significantly.
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scalable convolutional neural network for image compressed sensing
Computer Vision and Pattern Recognition, 2019Co-Authors: Wuzhen Shi, Feng Jiang, Shaohui Liu, Debin ZhaoAbstract:Recently, deep learning based image Compressed Sensing (CS) methods have been proposed and demonstrated superior reconstruction quality with low computational complexity. However, the existing deep learning based image CS methods need to train different models for different Sampling ratios, which increases the complexity of the encoder and decoder. In this paper, we propose a scalable convolutional neural network (dubbed SCSNet) to achieve scalable Sampling and scalable reconstruction with only one model. Specifically, SCSNet provides both coarse and fine granular scalability. For coarse granular scalability, SCSNet is designed as a single Sampling Matrix plus a hierarchical reconstruction network that contains a base layer plus multiple enhancement layers. The base layer provides the basic reconstruction quality, while the enhancement layers reference the lower reconstruction layers and gradually improve the reconstruction quality. For fine granular scalability, SCSNet achieves Sampling and reconstruction at any Sampling ratio by using a greedy method to select the measurement bases. Compared with the existing deep learning based image CS methods, SCSNet achieves scalable Sampling and quality scalable reconstruction at any Sampling ratio with only one model. Experimental results demonstrate that SCSNet has the state-of-the-art performance while maintaining a comparable running speed with the existing deep learning based image CS methods.
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deep networks for compressed image sensing
International Conference on Multimedia and Expo, 2017Co-Authors: Wuzhen Shi, Feng Jiang, Shengping Zhang, Debin ZhaoAbstract:The compressed sensing (CS) theory has been successfully applied to image compression in the past few years as most image signals are sparse in a certain domain. Several CS reconstruction models have been recently proposed and obtained superior performance. However, there still exist two important challenges within the CS theory. The first one is how to design a Sampling mechanism to achieve an optimal Sampling efficiency, and the second one is how to perform the reconstruction to get the highest quality to achieve an optimal signal recovery. In this paper, we try to deal with these two problems with a deep network. First of all, we train a Sampling Matrix via the network training instead of using a traditional manually designed one, which is much appropriate for our deep network based reconstruct process. Then, we propose a deep network to recover the image, which imitates traditional compressed sensing reconstruction processes. Experimental results demonstrate that our deep networks based CS reconstruction method offers a very significant quality improvement compared against state-of-the-art ones.
Michiel J Van Esdonk - One of the best experts on this subject based on the ideXlab platform.
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saliva as a Sampling Matrix for therapeutic drug monitoring of gentamicin in neonates a prospective population pharmacokinetic and simulation study
British Journal of Clinical Pharmacology, 2021Co-Authors: Amadou Samb, Matthijs D Kruizinga, Gertjan J Driessen, Michiel J Van Esdonk, Younes Tallahi, Willemijn Van Heel, Yuma A Bijleveld, Rik StuurmanAbstract:INTRODUCTION Therapeutic drug monitoring (TDM) of gentamicin in neonates is recommended for safe and effective dosing and is currently performed by plasma Sampling, which is an invasive and painful procedure. In this study, feasibility of a non-invasive gentamicin TDM strategy using saliva was investigated. METHODS This was a multicenter, prospective, observational cohort study including 54 neonates. Any neonate treated with intravenous gentamicin was eligible for the study. Up to 8 saliva samples were collected per patient at different time-points. Gentamicin levels in saliva were determined with liquid chromatography tandem mass-spectrometry. A population pharmacokinetic (PK) model was developed using Nonlinear Mixed-Effects Modeling (NONMEM) to describe the relation between gentamicin concentrations in saliva and plasma. Monte Carlo simulations with a representative virtual cohort (N=3000) were performed to evaluate the probability of target attainment with saliva- versus plasma TDM. RESULTS Plasma PK was adequately described with an earlier published model. An additional saliva compartment describing the salivary gentamicin concentrations was appended to the model with first-order input (k13 0.023 h-1 ) and first-order elimination (k30 0.169 h-1 ). Inter-individual variability of k30 was 38%. Postmenstrual age (PMA) correlated negatively with both k13 and k30 . Simulations demonstrated that TDM with 4 saliva samples was accurate in 81% of the simulated cases versus 94% when performed with 2 plasma samples and 87% when performed with 1 plasma sample. CONCLUSION TDM of gentamicin using saliva is feasible and the difference in precision between saliva and plasma TDM may not be clinically relevant, especially for premature neonates.
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theoretical performance of nonlinear mixed effect models incorporating saliva as an alternative Sampling Matrix for therapeutic drug monitoring in pediatrics a simulation study
Therapeutic Drug Monitoring, 2021Co-Authors: Matthijs D Kruizinga, Frederik E Stuurman, Gertjan J Driessen, A F Cohen, Kirsten R Bergmann, Michiel J Van EsdonkAbstract:Background Historically, pharmacokinetic (PK) studies and therapeutic drug monitoring (TDM) have relied on plasma as a Sampling Matrix. Noninvasive Sampling matrices, such as saliva, can reduce the burden on pediatric patients. The variable plasma-saliva relationship can be quantified using population PK models (nonlinear mixed-effect models). However, criteria regarding acceptable levels of variability in such models remain unclear. In this simulation study, the authors aimed to propose a saliva TDM evaluation framework and evaluate model requirements in the context of TDM, with gentamicin and lamotrigine as model compounds. Methods Two population pharmacokinetic models for gentamicin in neonates and lamotrigine in pediatrics were extended with a saliva compartment including a delay constant (kSALIVA), a saliva:plasma ratio, and between-subject variability (BSV) on both parameters. Subjects were simulated using a realistic covariate distribution. Bayesian maximum a posteriori TDM was applied to assess the performance of an increasing number of TDM saliva samples and varying levels of BSV and residual variability. Saliva TDM performance was compared with plasma TDM performance. The framework was applied to a known voriconazole saliva model as a case study. Results TDM performed using saliva resulted in higher target attainment than no TDM, and a residual proportional error 25% for gentamicin and >50% for lamotrigine reduced performance. The simulated target attainment for voriconazole saliva TDM was >90%. Conclusions Saliva as an alternative Matrix for noninvasive TDM is possible using nonlinear mixed-effect models combined with Bayesian optimization. This article provides a workflow to explore TDM performance for compounds measured in saliva and can be used for evaluation during model building.
Wei Hong - One of the best experts on this subject based on the ideXlab platform.
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efficient two dimensional direction finding via auxiliary variable manifold separation technique for arbitrary array structure
Mathematical Problems in Engineering, 2015Co-Authors: Guang Hua, Hou-xing Zhou, Xicheng Zhu, Wei HongAbstract:A polynomial rooting direction of arrival (DOA) algorithm for multiple plane waves incident on an arbitrary array structure that combines the multipolynomial resultants and Matrix computations is proposed in this paper. Firstly, a new auxiliary-variable manifold separation technique (AV-MST) is used to model the steering vector of arbitrary array structure as the product of a Sampling Matrix (dependent only on the array structure) and two Vandermonde-structured wavefield coefficient vectors (dependent on the wavefield). Then the propagator operator is calculated and used to form a system of bivariate polynomial equations. Finally, the automatically paired azimuth and elevation estimates are derived by polynomial rooting. The presented algorithm employs the concept of auxiliary-variable manifold separation technique which requires no sector by sector array interpolation and thus does not suffer from any mapping errors. In addition, the new algorithm does not need any eigenvalue decomposition of the covariance Matrix and exhausted search over the two-dimensional parameter space. Moreover, the algorithm gives automatically paired estimates, thus avoiding the complex pairing procedure. Therefore, the proposed algorithm shows low computational complexity and high robustness performance. Simulation results are shown to validate the effectiveness of the proposed method.
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Efficient two dimensional direction finding via auxiliary-variable manifold separation technique for arbitrary array structure
2014 IEEE International Conference on Communiction Problem-solving, 2014Co-Authors: Jiu-dong Wu, Hou-xing Zhou, Wei HongAbstract:A polynomial rooting Direction of Arrival (DOA) algorithm for multiple plane waves incident on an arbitrary array structure that combines the multipolynomial resultants and Matrix computations is presented in this paper. Firstly, a new auxiliary-variable manifold separation technique (AV-MST) is proposed to modal the steering vector of arbitrary array structure as the product of a Sampling Matrix (dependent only on the array structure) and two Vandermonde-structured wavefield coefficient vectors (dependent on the wavefield). Then the propagator operator is calculated and used to form a system of bivariate polynomial equations. Finally, the automatically paired azimuth and elevation estimates are derived by polynomial rooting. The presented algorithm employs the concept of auxiliary-variable manifold separation technique which requires no sector by sector array interpolation and thus does not suffer from any mapping errors. In addition, the new algorithm does not need any eigenvalue decomposition of the covariance Matrix and exhausted search over the two dimensional parameter space. Moreover, the algorithm gives automatically paired estimates, thus avoiding the complex pairing procedure. Therefore, the proposed algorithm shows low computational complexity and high robustness performance. Simulation results are shown to validate the effectiveness of the proposed method.
