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简易生物电化学微传感器:卵母细胞质量预测及细胞膜电生理特性分析。

Simple bioelectrical microsensor: oocyte quality prediction membrane electrophysiological characterization.

机构信息

Department of Electrical and Computer Engineering, Tarbiat Modares University (TMU), Tehran, Iran.

Department of Embryology, Reproductive Biomedicine Research Center, Academic Center for Education, Culture and Research (ACECR), Royan Institute for Reproductive Biomedicine, Tehran, Iran.

出版信息

Lab Chip. 2024 Aug 6;24(16):3909-3929. doi: 10.1039/d3lc01120h.

DOI:10.1039/d3lc01120h
PMID:38985018
Abstract

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.

摘要

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,而这不可避免地容易出现人为错误。一种潜在的方法可能是使用基于电的方法作为改良的替代方法。在这里,我们开发了一种简单的电微传感器来表征小鼠卵母细胞。该传感器的设计类似于胚胎培养皿,胚胎学家很熟悉。为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。最后制造了微传感器。提出了一种基于细胞存在/不存在的差分测量技术。我们使用三个电特性、卵母细胞半径和透明带厚度来预测卵母细胞质量,并且还将这些预测与胚胎学家的评估进行了比较。卵母细胞膜电容的评估作为一种电生理特性,被发现是预测具有受精和囊胚形成成功能力的卵母细胞的更可靠方法。它分别实现了 94%和 58%的预测准确率,超过了其他方法,并且误差更低。这项开创性的研究代表了该领域的首例,我们希望这将是提高治疗准确性的一步。

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Machine learning and microfluidic integration for oocyte quality prediction.用于卵母细胞质量预测的机器学习与微流体集成
Sci Rep. 2025 Jul 22;15(1):26532. doi: 10.1038/s41598-025-11810-4.