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复杂还是不太复杂?下一代微生理系统并非一刀切。

Complex or not too complex? One size does not fit all in next generation microphysiological systems.

作者信息

Bersini Simone, Arrigoni Chiara, Talò Giuseppe, Candrian Christian, Moretti Matteo

机构信息

Regenerative Medicine Technologies Lab, Laboratories for Translational Research, Ente Ospedaliero Cantonale, via Chiesa 5, 6500 Bellinzona, Switzerland.

Service of Orthopaedics and Traumatology, Department of Surgery, Ente Ospedaliero Cantonale, via Tesserete 46, 6900 Lugano, Switzerland.

出版信息

iScience. 2024 Feb 12;27(3):109199. doi: 10.1016/j.isci.2024.109199. eCollection 2024 Mar 15.

Abstract

In the attempt to overcome the increasingly recognized shortcomings of existing and models, researchers have started to implement alternative models, including microphysiological systems. First examples were represented by 2.5D systems, such as microfluidic channels covered by cell monolayers as blood vessel replicates. In recent years, increasingly complex microphysiological systems have been developed, up to multi-organ on chip systems, connecting different 3D tissues in the same device. However, such an increase in model complexity raises several questions about their exploitation and implementation into industrial and clinical applications, ranging from how to improve their reproducibility, robustness, and reliability to how to meaningfully and efficiently analyze the huge amount of heterogeneous datasets emerging from these devices. Considering the multitude of envisaged applications for microphysiological systems, it appears now necessary to tailor their complexity on the intended purpose, being academic or industrial, and possibly combine results deriving from differently complex stages to increase their predictive power.

摘要

为了克服现有模型日益凸显的缺点,研究人员已开始采用包括微生理系统在内的替代模型。最初的例子是2.5D系统,比如由细胞单层覆盖的微流控通道作为血管复制品。近年来,越来越复杂的微生理系统不断被开发出来,直至芯片上的多器官系统,可在同一设备中连接不同的3D组织。然而,模型复杂性的这种增加引发了关于其在工业和临床应用中的开发与实施的若干问题,从如何提高其可重复性、稳健性和可靠性到如何有意义且高效地分析这些设备产生的大量异质数据集。考虑到微生理系统的众多设想应用,现在看来有必要根据预期目的(无论是学术还是工业目的)调整其复杂性,并可能将来自不同复杂阶段的结果结合起来以提高其预测能力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/87c5/10904982/807655a12b70/fx1.jpg

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