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

Deformation-Invariant Sparse Coding for Modeling Spatial Variability of Functional Patterns in the Brain

Abstract. For a given cognitive task such as language processing, the location of corresponding functional regions in the brain may vary across subjects relative to anatomy. We present a probabilistic generative model that accounts for such variability as observed in fMRI data. We relate our approach to sparse coding that estimates a basis consisting of functional regions in the brain. Individual fMRI data is represented as a weighted sum of these functional regions that undergo deformations. We demonstrate the proposed method on a language fMRI study. Our method identified activation regions that agree with known literature on language processing and established correspondences among activation regions across subjects, producing more robust group-level effects than anatomical alignment alone.

Multi-task learning for left atrial segmentation on GE-MRI

Segmentation of the left atrium (LA) is crucial for assessing its anatomy in both pre-operative atrial fibrillation (AF) ablation planning and post-operative follow-up studies. In this paper, we present a fully automated framework for left atrial segmentation in gadolinium-enhanced magnetic resonance images (GE-MRI) based on deep learning. We propose a fully convolutional neural network and explore the benefits of multi-task learning for performing both atrial segmentation and pre/post ablation classification. Our results show that, by sharing features between related tasks, the network can gain additional anatomical information and achieve more accurate atrial segmentation, leading to a mean Dice score of 0.901 on a test set of 20 3D MRI images. Code of our proposed algorithm is available at https://github.com/cherise215/atria_segmentation_2018/