Serum estradiol: oocyte ratio as a predictor of reproductive outcome: an analysis of data from >9000 IVF cycles in the Republic of Ireland

Purpose The purpose of this study was to evaluate the serum estradiol (E2) per oocyte ratio (EOR) as a function of selected embryology events and reproductive outcomes with IVF. Methods This retrospective analysis included all IVF cycles where oocyte collection and fresh transfer occurred between January 2001 and November 2012 at a single institution. Patients were divided by three age groups (<35, 35–39, and ≥40 years) and further stratified into nine groups based on EOR (measured in pmol/L/oocyte). Terminal serum E2 (pmol/mL) was recorded on day of hCG trigger administration, and fertilization rate, cleavage rate, number of good quality embryos, and reproductive outcomes were recorded for each IVF cycle. Results During the study interval, 9109 oocyte retrievals were performed for 5499 IVF patients (mean = 1.7 cycles/patient). A total of 63.4 % of transfers were performed on day 3 (n = 4926), while 36.6 % were carried out on day 5 (n = 2843). Clinical pregnancy rates were highest in patients with EOR of 250–750 and declined as this ratio increased, independent of patient age. While the odds ratio (OR) for clinical pregnancy where EOR = 250–750 vs. EOR > 1500 was 3.4 (p < 0.001; 95 % CI 2.67–4.34), no statistically significant correlation was seen in fertilization, cleavage rates or number of good quality embryos as a function of EOR. Conclusions Predicting reproductive outcomes with IVF has great utility both for patients and providers. The former have the opportunity to build realistic expectations, and the latter are better able to counsel according to measured clinical parameters. A better understanding of follicular dynamics and ovarian response to gonadotropin stimulation could optimize IVF treatments going forward.

Context-aware Approach for Determining the Threshold Price in Name-Your-Own-Price Channels

Key feature of a context-aware application is the ability to adapt based on the change of context. Two approaches that are widely used in this regard are the context-action pair mapping where developers match an action to execute for a particular context change and the adaptive learning where a context-aware application refines its action over time based on the preceding action’s outcome. Both these approaches have limitation which makes them unsuitable in situations where a context-aware application has to deal with unknown context changes. In this paper we propose a framework where adaptation is carried out via concurrent multi-action evaluation of a dynamically created action space. This dynamic creation of the action space eliminates the need for relying on the developers to create context-action pairs and the concurrent multi-action evaluation reduces the adaptation time as opposed to the iterative approach used by adaptive learning techniques. Using our reference implementation of the framework we show how it could be used to dynamically determine the threshold price in an e-commerce system which uses the name-your-own-price (NYOP) strategy.