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Simple bioelectrical microsensor: oocyte quality prediction via membrane electrophysiological characterization
Lab on a Chip ( IF 6.1 ) Pub Date : 2024-06-29 , DOI: 10.1039/d3lc01120h
Peyman Palay 1 , Davood Fathi 1 , Hassan Saffari 1 , Fatemeh Hassani 2 , Samira Hajiaghalou 2 , Rouhollah Fathi 2
Affiliation  

Oocyte selection is a crucial step of assisted reproductive treatment. The most common approach relies on the embryologist experience which is inevitably prone to human error. One potential approach could be the use of an electrical-based approach as an ameliorative alternative. Here, we developed a simple electrical microsensor to characterize mouse oocytes. The sensor is designed similarly to embryo culture dishes and is familiar to embryologists. Different microelectrode models were simulated for oocyte cells and a more sensitive model was determined. The final microsensor was fabricated. A differential measuring technique was proposed based on the cell presence/absence. We predicted oocyte quality by using three electrical characteristics, oocyte radii, and zona thicknesses, and also these predictions were compared with an embryologist evaluation. The evaluation of the oocyte membrane capacitance, as an electrophysiological characteristic, was found to be a more reliable method for predicting oocytes with fertilization and blastocyst formation success competence. It achieved 94% and 58% prediction accuracies, respectively, surpassing other methods and yielding lower errors. This groundbreaking research represents the first of its kind in this field and we hope that this will be a step towards improving the accuracy of the treatment.

中文翻译:


简单的生物电微传感器:通过膜电生理表征预测卵母细胞质量



卵母细胞选择是辅助生殖治疗的关键步骤。最常见的方法依赖于胚胎学家的经验,这不可避免地容易出现人为错误。一种可能的方法是使用基于电气的方法作为改进的替代方案。在这里,我们开发了一种简单的电子微传感器来表征小鼠卵母细胞。该传感器的设计与胚胎培养皿类似,为胚胎学家所熟悉。对卵母细胞模拟了不同的微电极模型,并确定了更灵敏的模型。最终的微传感器已制作完成。提出了基于细胞存在/不存在的差分测量技术。我们通过使用卵母细胞半径和透明带厚度这三个电特性来预测卵母细胞质量,并将这些预测与胚胎学家的评估进行比较。卵母细胞膜电容的评估作为一种电生理学特征,被发现是预测卵母细胞受精和囊胚形成成功能力的更可靠方法。它分别实现了 94% 和 58% 的预测精度,超越了其他方法并且产生了更低的误差。这项开创性的研究代表了该领域的首次研究,我们希望这将成为提高治疗准确性的一步。
更新日期:2024-06-29
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