The Experts below are selected from a list of 279 Experts worldwide ranked by ideXlab platform
Susanne Nussbaumer - One of the best experts on this subject based on the ideXlab platform.
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wipe Sampling Procedure coupled to lc ms ms analysis for the simultaneous determination of 10 cytotoxic drugs on different surfaces
Analytical and Bioanalytical Chemistry, 2012Co-Authors: Susanne Nussbaumer, Laurent Geiser, F Sadeghipour, Denis F Hochstrasser, Pascal Bonnabry, Jeanluc Veuthey, Sandrine FleurysouverainAbstract:A simple wipe Sampling Procedure was developed for the surface contamination determination of ten cytotoxic drugs: cytarabine, gemcitabine, methotrexate, etoposide phosphate, cyclophosphamide, ifosfamide, irinotecan, doxorubicin, epirubicin and vincristine. Wiping was performed using Whatman filter paper on different surfaces such as stainless steel, polypropylene, polystyrol, glass, latex gloves, computer mouse and coated paperboard. Wiping and desorption Procedures were investigated: The same solution containing 20% acetonitrile and 0.1% formic acid in water gave the best results. After ultrasonic desorption and then centrifugation, samples were analysed by a validated liquid chromatography coupled to tandem mass spectrometry (LC–MS/MS) in selected reaction monitoring mode. The whole analytical strategy from wipe Sampling to LC–MS/MS analysis was evaluated to determine quantitative performance. The lowest limit of quantification of 10 ng per wiping sample (i.e. 0.1 ng cm−2) was determined for the ten investigated cytotoxic drugs. Relative standard deviation for intermediate precision was always inferior to 20%. As recovery was dependent on the tested surface for each drug, a correction factor was determined and applied for real samples. The method was then successfully applied at the cytotoxic production unit of the Geneva University Hospitals pharmacy.
Sandrine Fleurysouverain - One of the best experts on this subject based on the ideXlab platform.
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wipe Sampling Procedure coupled to lc ms ms analysis for the simultaneous determination of 10 cytotoxic drugs on different surfaces
Analytical and Bioanalytical Chemistry, 2012Co-Authors: Susanne Nussbaumer, Laurent Geiser, F Sadeghipour, Denis F Hochstrasser, Pascal Bonnabry, Jeanluc Veuthey, Sandrine FleurysouverainAbstract:A simple wipe Sampling Procedure was developed for the surface contamination determination of ten cytotoxic drugs: cytarabine, gemcitabine, methotrexate, etoposide phosphate, cyclophosphamide, ifosfamide, irinotecan, doxorubicin, epirubicin and vincristine. Wiping was performed using Whatman filter paper on different surfaces such as stainless steel, polypropylene, polystyrol, glass, latex gloves, computer mouse and coated paperboard. Wiping and desorption Procedures were investigated: The same solution containing 20% acetonitrile and 0.1% formic acid in water gave the best results. After ultrasonic desorption and then centrifugation, samples were analysed by a validated liquid chromatography coupled to tandem mass spectrometry (LC–MS/MS) in selected reaction monitoring mode. The whole analytical strategy from wipe Sampling to LC–MS/MS analysis was evaluated to determine quantitative performance. The lowest limit of quantification of 10 ng per wiping sample (i.e. 0.1 ng cm−2) was determined for the ten investigated cytotoxic drugs. Relative standard deviation for intermediate precision was always inferior to 20%. As recovery was dependent on the tested surface for each drug, a correction factor was determined and applied for real samples. The method was then successfully applied at the cytotoxic production unit of the Geneva University Hospitals pharmacy.
Yijie Peng - One of the best experts on this subject based on the ideXlab platform.
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WSC - Dynamic Sampling Procedure for Decomposable Random Networks
2019 Winter Simulation Conference (WSC), 2019Co-Authors: Haidong Li, Yijie Peng, Xiaoyun Xu, Chun-hung Chen, Bernd F. HeidergottAbstract:This research studies the problem of node ranking in a random network. Specifically, we consider a Markov chain with several ergodic classes and unknown transition probabilities which can be estimated by Sampling. The objective is to select all of the best nodes in each ergodic class. A Sampling Procedure is proposed to decompose the Markov chain and maximize a weighted probability of correct selection of the best nodes in each ergodic class. Numerical results demonstrate the efficiency of the proposed Sampling Procedure.
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Dynamic Sampling Procedure for Decomposable Random Networks
2019 Winter Simulation Conference (WSC), 2019Co-Authors: Haidong Li, Yijie Peng, Xiaoyun Xu, Chun-hung Chen, Bernd F. HeidergottAbstract:This research studies the problem of node ranking in a random network. Specifically, we consider a Markov chain with several ergodic classes and unknown transition probabilities which can be estimated by Sampling. The objective is to select all of the best nodes in each ergodic class. A Sampling Procedure is proposed to decompose the Markov chain and maximize a weighted probability of correct selection of the best nodes in each ergodic class. Numerical results demonstrate the efficiency of the proposed Sampling Procedure.
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Efficient Sampling Procedure for Selecting the Largest Stationary Probability of a Markov Chain
2018 IEEE 14th International Conference on Automation Science and Engineering (CASE), 2018Co-Authors: Haidong Li, Yijie Peng, Xiaoyun Xu, Chun-hung ChenAbstract:This study considers the problem of selecting the alternative with the largest stationary probability of a Markov chain. Specifically, we assume that a Markov chain is constructed in accordance with Google's PageRank. The stationary probabilities are determined by the transition probabilities of the Markov chain, and the transition probabilities are unknown but can be estimated by Sampling (or collecting data through real-time web page monitoring). Sensitivity analysis is conducted to capture the marginal influence of transition probability estimation errors on the estimation of stationary probabilities. A dynamic sample allocation Procedure is proposed, which uses not only posterior means and variances of the estimated transition probabilities but also scaling factors based on the sensitivity analysis. Numerical experiment results demonstrate that the proposed Procedure is significantly more efficient than all compared methods.
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Gradient-Based Myopic Allocation Policy: An Efficient Sampling Procedure in a Low-Confidence Scenario
IEEE Transactions on Automatic Control, 2018Co-Authors: Yijie Peng, Chun-hung Chen, Michael C. Fu, Jian-qiang HuAbstract:In this note, we study a simulation optimization problem of selecting the alternative with the best performance from a finite set, or a so-called ranking and selection problem, in a special low-confidence scenario. The most popular Sampling allocation Procedures in ranking and selection do not perform well in this scenario, because they all ignore certain induced correlations that significantly affect the probability of correct selection in this scenario. We propose a gradient-based myopic allocation policy that takes the induced correlations into account, reflecting a tradeoff between the induced correlation and the two factors (mean-variance) found in the optimal computing budget allocation formula. Numerical experiments substantiate the efficiency of the new Procedure in the low-confidence scenario.
Pascal Bonnabry - One of the best experts on this subject based on the ideXlab platform.
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wipe Sampling Procedure coupled to lc ms ms analysis for the simultaneous determination of 10 cytotoxic drugs on different surfaces
Analytical and Bioanalytical Chemistry, 2012Co-Authors: Susanne Nussbaumer, Laurent Geiser, F Sadeghipour, Denis F Hochstrasser, Pascal Bonnabry, Jeanluc Veuthey, Sandrine FleurysouverainAbstract:A simple wipe Sampling Procedure was developed for the surface contamination determination of ten cytotoxic drugs: cytarabine, gemcitabine, methotrexate, etoposide phosphate, cyclophosphamide, ifosfamide, irinotecan, doxorubicin, epirubicin and vincristine. Wiping was performed using Whatman filter paper on different surfaces such as stainless steel, polypropylene, polystyrol, glass, latex gloves, computer mouse and coated paperboard. Wiping and desorption Procedures were investigated: The same solution containing 20% acetonitrile and 0.1% formic acid in water gave the best results. After ultrasonic desorption and then centrifugation, samples were analysed by a validated liquid chromatography coupled to tandem mass spectrometry (LC–MS/MS) in selected reaction monitoring mode. The whole analytical strategy from wipe Sampling to LC–MS/MS analysis was evaluated to determine quantitative performance. The lowest limit of quantification of 10 ng per wiping sample (i.e. 0.1 ng cm−2) was determined for the ten investigated cytotoxic drugs. Relative standard deviation for intermediate precision was always inferior to 20%. As recovery was dependent on the tested surface for each drug, a correction factor was determined and applied for real samples. The method was then successfully applied at the cytotoxic production unit of the Geneva University Hospitals pharmacy.
Bernd F. Heidergott - One of the best experts on this subject based on the ideXlab platform.
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WSC - Dynamic Sampling Procedure for Decomposable Random Networks
2019 Winter Simulation Conference (WSC), 2019Co-Authors: Haidong Li, Yijie Peng, Xiaoyun Xu, Chun-hung Chen, Bernd F. HeidergottAbstract:This research studies the problem of node ranking in a random network. Specifically, we consider a Markov chain with several ergodic classes and unknown transition probabilities which can be estimated by Sampling. The objective is to select all of the best nodes in each ergodic class. A Sampling Procedure is proposed to decompose the Markov chain and maximize a weighted probability of correct selection of the best nodes in each ergodic class. Numerical results demonstrate the efficiency of the proposed Sampling Procedure.
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Dynamic Sampling Procedure for Decomposable Random Networks
2019 Winter Simulation Conference (WSC), 2019Co-Authors: Haidong Li, Yijie Peng, Xiaoyun Xu, Chun-hung Chen, Bernd F. HeidergottAbstract:This research studies the problem of node ranking in a random network. Specifically, we consider a Markov chain with several ergodic classes and unknown transition probabilities which can be estimated by Sampling. The objective is to select all of the best nodes in each ergodic class. A Sampling Procedure is proposed to decompose the Markov chain and maximize a weighted probability of correct selection of the best nodes in each ergodic class. Numerical results demonstrate the efficiency of the proposed Sampling Procedure.
