Impact of GnRH analogues on oocyte/embryo quality and embryo development in in vitro fertilization/intracytoplasmic sperm injection cycles: a case control study

<p>Abstract</p> <p>Background</p> <p>Despite the clinical outcomes of ovarian stimulation with either GnRH-agonist or GnRH-antagonist analogues for in vitro fertilization (IVF) being well analysed, the effect of analogues on oocyte/embryo quality and embryo development is still not known in detail. The aim of this case-control study was to compare the efficacy of a multiple-dose GnRH antagonist protocol with that of the GnRH agonist long protocol with a view to oocyte and embryo quality, embryo development and IVF treatment outcome.</p> <p>Methods</p> <p>Between October 2001 and December 2008, 100 patients were stimulated with human menopausal gonadotrophin (HMG) and GnRH antagonist in their first treatment cycle for IVF or intracytoplasmic sperm injection (ICSI). One hundred combined GnRH agonist + HMG (long protocol) cycles were matched to the GnRH antagonist + HMG cycles by age, BMI, baseline FSH levels and by cause of infertility. We determined the number and quality of retrieved oocytes, the rate of early-cleavage embryos, the morphology and development of embryos, as well as clinical pregnancy rates. Statistical analysis was performed using Wilcoxon's matched pairs rank sum test and McNemar's chi-square test. P < 0.05 was considered statistically significant.</p> <p>Results</p> <p>The rate of cytoplasmic abnormalities in retrieved oocytes was significantly higher with the use of GnRH antagonist than in GnRH agonist cycles (62.1% vs. 49.9%; P < 0.01). We observed lower rate of zygotes showing normal pronuclear morphology (49.3% vs. 58.0%; P < 0.01), and higher cell-number of preembryos on day 2 after fertilization (4.28 vs. 4.03; P < 0.01) with the use of GnRH antagonist analogues. The rate of mature oocytes, rate of presence of multinucleated blastomers, amount of fragmentation in embryos and rate of early-cleaved embryos was similar in the two groups. Clinical pregnancy rate per embryo transfer was lower in the antagonist group than in the agonist group (30.8% vs. 40.4%) although this difference did not reach statistical significance (P = 0.17).</p> <p>Conclusion</p> <p>Antagonist seemed to influence favourably some parameters of early embryo development dynamics, while other morphological parameters seemed not to be altered according to GnRH analogue used for ovarian stimulation in IVF cycles.</p

Études et optimisation numérique par des modèles de substitution pour la conception de sources d'électrons d'accélérateurs laser-plasma

The optimisation of the plasma target design for high quality beam laser-driven plasma injector electron source relies on numerical parametric studies using Particle in Cell (PIC) codes. The common input parameters to explore are laser characteristics and plasma density profiles extracted from computational fluid dynamic studies compatible with experimental measurements of target plasma density profiles. We demonstrate the construction of surrogate models using machine learning technique for a laser-plasma injector (LPI) electron source based on more than 12000 simulations of a laser wakefield acceleration performed for sparsely spaced input parameters [1]. Surrogate models are very interesting for LPI design and optimisation because they are much faster than PIC simulations. We develop and compare the performance of three surrogate models, namely, Gaussian processes (GP), multilayer perceptron (MLP), and decision trees (DT). We then use the best surrogate model to quickly find optimal working points to get a selected electron beam energy, charge and energy spread using different methods, namely random search, Bayesian optimisation and multi-objective Bayesian optimisationL'optimisation de la conception de la cible de plasma pour une source d'électrons d’un injecteur laser-plasma de haute qualité de faisceau repose sur des études paramétriques numériques utilisant des codes PIC (Particle in Cell). Les paramètres d'entrée communs à explorer sont les caractéristiques du laser et les profils de densité du plasma extraits d'études numeriques de dynamique des fluides compatibles avec les mesures expérimentales des profils de densité de la cible plasma. Nous démontrons la construction de modèles de substitution à l'aide d'une technique d'apprentissage automatique pour un injecteur laser-plasma (LPI) basée sur plus de 12000 de simulations d'accélération d'un champ de sillage laser effectuées pour des paramètres d'entrée peu espacés. Les modèles de substitution sont très intéressants pour la conception et l'optimisation des LPI car ils sont beaucoup plus rapides que les simulations PIC. Nous développons et comparons les performances de trois modèles de substitution, à savoir les processus gaussiens (GP), le perceptron multicouche (MLP) et les arbres de décision (DT). Nous utilisons ensuite le meilleur modèle de substitution pour trouver rapidement les points de travail optimaux afin d'obtenir une énergie de faisceau d'électrons, une charge et une répartition d'énergie recherchées à l'aide de différentes méthodes, à savoir la recherche aléatoire, l'optimisation bayésienne et l'optimisation bayésienne multi-objectifs

Surrogate Models studies for laser-plasma accelerator electron source design through numerical optimisation

International audienceThe optimisation of the plasma target design for high quality beam laser-driven plasma injector electron source relies on numerical parametric studies using Particle in Cell (PIC) codes. The common input parameters to explore are laser characteristics and plasma density profiles extracted from computational fluid dynamic studies compatible with experimental measurements of target plasma density profiles. We demonstrate the construction of surrogate models using machine learning technique for a laser-plasma injector (LPI) electron source based on more than 12000 simulations of a laser wakefield acceleration performed for sparsely spaced input parameters [1]. Surrogate models are very interesting for LPI design and optimisation because they are much faster than PIC simulations. We develop and compare the performance of three surrogate models, namely, Gaussian processes (GP), multilayer perceptron (MLP), and decision trees (DT). We then use the best surrogate model to quickly find optimal working points to get a selected electron beam energy, charge and energy spread using different methods, namely random search, Bayesian optimisation and multi-objective Bayesian optimisatio

Études et optimisation numérique par des modèles de substitution pour la conception de sources d'électrons d'accélérateurs laser-plasma

The optimisation of the plasma target design for high quality beam laser-driven plasma injector electron source relies on numerical parametric studies using Particle in Cell (PIC) codes. The common input parameters to explore are laser characteristics and plasma density profiles extracted from computational fluid dynamic studies compatible with experimental measurements of target plasma density profiles. We demonstrate the construction of surrogate models using machine learning technique for a laser-plasma injector (LPI) electron source based on more than 12000 simulations of a laser wakefield acceleration performed for sparsely spaced input parameters [1]. Surrogate models are very interesting for LPI design and optimisation because they are much faster than PIC simulations. We develop and compare the performance of three surrogate models, namely, Gaussian processes (GP), multilayer perceptron (MLP), and decision trees (DT). We then use the best surrogate model to quickly find optimal working points to get a selected electron beam energy, charge and energy spread using different methods, namely random search, Bayesian optimisation and multi-objective Bayesian optimisationL'optimisation de la conception de la cible de plasma pour une source d'électrons d’un injecteur laser-plasma de haute qualité de faisceau repose sur des études paramétriques numériques utilisant des codes PIC (Particle in Cell). Les paramètres d'entrée communs à explorer sont les caractéristiques du laser et les profils de densité du plasma extraits d'études numeriques de dynamique des fluides compatibles avec les mesures expérimentales des profils de densité de la cible plasma. Nous démontrons la construction de modèles de substitution à l'aide d'une technique d'apprentissage automatique pour un injecteur laser-plasma (LPI) basée sur plus de 12000 de simulations d'accélération d'un champ de sillage laser effectuées pour des paramètres d'entrée peu espacés. Les modèles de substitution sont très intéressants pour la conception et l'optimisation des LPI car ils sont beaucoup plus rapides que les simulations PIC. Nous développons et comparons les performances de trois modèles de substitution, à savoir les processus gaussiens (GP), le perceptron multicouche (MLP) et les arbres de décision (DT). Nous utilisons ensuite le meilleur modèle de substitution pour trouver rapidement les points de travail optimaux afin d'obtenir une énergie de faisceau d'électrons, une charge et une répartition d'énergie recherchées à l'aide de différentes méthodes, à savoir la recherche aléatoire, l'optimisation bayésienne et l'optimisation bayésienne multi-objectifs

Surrogate Models studies for laser-plasma accelerator electron source design through numerical optimisation

International audienceThe optimisation of the plasma target design for high quality beam laser-driven plasma injector electron source relies on numerical parametric studies using Particle in Cell (PIC) codes. The common input parameters to explore are laser characteristics and plasma density profiles extracted from computational fluid dynamic studies compatible with experimental measurements of target plasma density profiles. We demonstrate the construction of surrogate models using machine learning technique for a laser-plasma injector (LPI) electron source based on more than 12000 simulations of a laser wakefield acceleration performed for sparsely spaced input parameters [1]. Surrogate models are very interesting for LPI design and optimisation because they are much faster than PIC simulations. We develop and compare the performance of three surrogate models, namely, Gaussian processes (GP), multilayer perceptron (MLP), and decision trees (DT). We then use the best surrogate model to quickly find optimal working points to get a selected electron beam energy, charge and energy spread using different methods, namely random search, Bayesian optimisation and multi-objective Bayesian optimisatio