关键词: Raman spectroscopy artificial intelligence fertility light microscopy live and fixed cell imaging machine learning microfluidics non-invasive oocyte screening oocyte imaging oocyte mitochondria polarized light microscopy

Mesh : Oocytes / physiology Humans Animals Female Fertilization in Vitro / methods Reproductive Techniques, Assisted

来  源:   DOI:10.1093/biolre/ioae080   PDF(Pubmed)

Abstract:
Determining egg quality is the foremost challenge in assisted reproductive technology (ART). Although extensive advances have been made in multiple areas of ART over the last 40 years, oocyte quality assessment tools have not much evolved beyond standard morphological observation. The oocyte not only delivers half of the nuclear genetic material and all of the mitochondrial DNA to an embryo but also provides complete developmental support during embryonic growth. Oocyte mitochondrial numbers far exceed those of any somatic cell, yet little work has been done to evaluate the mitochondrial bioenergetics of an oocyte. Current standard oocyte assessment in in vitro fertilization (IVF) centers include the observation of oocytes and their surrounding cell complex (cumulus cells) via stereomicroscope or inverted microscope, which is largely primitive. Additional oocyte assessments include polar body grading and polarized light meiotic spindle imaging. However, the evidence regarding the aforementioned methods of oocyte quality assessment and IVF outcomes is contradictory and non-reproducible. High-resolution microscopy techniques have also been implemented in animal and human models with promising outcomes. The current era of oocyte imaging continues to evolve with discoveries in artificial intelligence models of oocyte morphology selection albeit at a slow rate. In this review, the past, current, and future oocyte imaging techniques will be examined with the goal of drawing attention to the gap which limits our ability to assess oocytes in real time. The implications of improved oocyte imaging techniques on patients undergoing IVF will be discussed as well as the need to develop point of care oocyte assessment testing in IVF labs.
摘要:
确定卵子质量是辅助生殖技术(ART)的首要挑战。尽管在过去的四十年中,ART的多个领域取得了广泛的进展,卵母细胞质量评估工具的发展并没有超出标准的形态学观察。卵母细胞不仅将一半的核遗传物质和所有的线粒体DNA传递给胚胎,而且在胚胎生长过程中提供完整的发育支持。卵母细胞线粒体数量远远超过任何体细胞,然而,在评估卵母细胞的线粒体生物能量学方面几乎没有做任何工作。体外受精(IVF)中心目前的标准卵母细胞评估包括通过立体显微镜或倒置显微镜观察卵母细胞及其周围的细胞复合物(卵丘细胞)。这在很大程度上是原始的。其他卵母细胞评估包括极体分级和偏振光减数分裂纺锤体成像。然而,关于上述卵母细胞质量评估方法和IVF结局的证据相互矛盾且不可重复.高分辨率显微镜技术也已在动物和人体模型中实施,具有有希望的结果。当前的卵母细胞成像时代随着卵母细胞形态选择的人工智能模型的发现而继续发展,尽管速度很慢。在这次审查中,过去,电流,未来的卵母细胞成像技术将被研究,目的是提请注意限制我们实时评估卵母细胞能力的差距。将讨论改进的卵母细胞成像技术对接受IVF的患者的影响,以及在IVF实验室中开发护理卵母细胞评估测试的必要性。
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