工程体外模型:人工智能类器官生物打印。

IF 10.5 Q1 ENGINEERING, BIOMEDICAL
Hyungseok Lee
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引用次数: 3

摘要

在过去的十年中,类器官在开发微型器官方面获得了广泛的应用,以支持器官发生、疾病建模和药物筛选的研究进展,并随后支持新疗法的开发。迄今为止,这种培养物已被用于复制器官的组成和功能,如肾、肝、脑和胰腺。然而,根据实验者的不同,培养环境和细胞条件可能略有不同,从而产生不同的类器官;这一因素显著影响其在新药开发中的应用,特别是在定量过程中。这方面的标准化可以通过生物打印技术来实现,这是一种先进的技术,可以在所需的位置打印各种细胞和生物材料。这项技术有许多优点,包括制造复杂的三维生物结构。因此,除了类器官的标准化之外,类器官工程中的生物打印技术还可以促进制造过程的自动化以及更接近天然器官的模仿。此外,人工智能(AI)目前已成为监测和控制最终开发对象质量的有效工具。因此,类器官、生物打印技术和人工智能可以结合起来,获得高质量的体外模型,用于多种应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Engineering In vitro Models: Bioprinting of Organoids with Artificial Intelligence.

Engineering In vitro Models: Bioprinting of Organoids with Artificial Intelligence.

In the last decade, organoids have gained popularity for developing mini-organs to support advancements in the study of organogenesis, disease modeling, and drug screening and, subsequently, in the development of new therapies. To date, such cultures have been used to replicate the composition and functionality of organs such as the kidney, liver, brain, and pancreas. However, depending on the experimenter, the culture environment and cell conditions may slightly vary, resulting in different organoids; this factor significantly affects their application in new drug development, especially during quantification. Standardization in this context can be achieved using bioprinting technology-an advanced technology that can print various cells and biomaterials at desired locations. This technology offers numerous advantages, including the manufacturing of complex three-dimensional biological structures. Therefore, in addition to the standardization of organoids, bioprinting technology in organoid engineering can facilitate automation in the fabrication process as well as a closer mimicry of native organs. Further, artificial intelligence (AI) has currently emerged as an effective tool to monitor and control the quality of final developed objects. Thus, organoids, bioprinting technology, and AI can be combined to obtain high-quality in vitro models for multiple applications.

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来源期刊
CiteScore
7.70
自引率
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