Profiling oocytes with neural networks from images and mechanical data.

The success rate of assisted reproductive technologies could be greatly improved by selectively choosing egg cells (oocytes) with the greatest chance of fertilization. The goal of mechanical profiling is, thus, to improve predictive oocyte selection by isolating the mechanical properties of oocytes and correlating them to their reproductive potential. The restrictions on experimental platforms, however - including minimal invasiveness and practicality in laboratory implementation - greatly limits the data that can be acquired from a single oocyte.

A Two-Axis Piezoresistive Force Sensing Tool for Microgripping.

Force sensing has always been an important necessity in making decisions for manipulation. It becomes more appealing in the micro-scale context, especially where the surface forces become predominant. In addition, the deformations happening at the very local level are often coupled, and therefore providing multi-axis force sensing capabilities to microgripper becomes an important necessity.

Separating the contributions of zona pellucida and cytoplasm in the viscoelastic response of human oocytes.

The successful characterization of the mechanical properties of human oocytes and young embryos is of crucial relevance to reduce the risk of pregnancy arrest in in-vitro fertilization processes. Unfortunately, current study has been hindered by the lack of accuracy in describing the mechanical contributions of each structure (zona pellucida, cytoplasm) due to its high heterogeneity. In this work, we present a novel approach to model the oocyte response taking into account the effect of both zona and cytoplasm, as well as different loading conditions.