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Leveraging AI for Efficient Analysis of Cell Transfection
利用 AI 高效分析细胞转染

How to optimize your transfection efficiency measurements for 2D cell culture with AI
如何使用 AI 优化 2D 细胞培养的转染效率测量

AI-based transfection analysis (left) of U2OS cells which were transfected with a fluorescently labelled protein. A fluorescence image of the cells (right) is also shown. The analysis and imaging were performed with Mateo FL. AI-based_analysis_of_U2OS_cells_transfected_with_fluorescently_labelled_protein.jpg

This article explores the pivotal role of artificial intelligence (AI) in optimizing transfection efficiency measurements within the context of 2D cell culture studies. Precise and reliable transfection efficiency measurements for 2D cell culture are key for understanding cellular mechanisms. A high transfection efficiency of the targeted proteins is crucial for experiments including live-cell imaging and protein purification. Manual estimation is inconsistent and unreliable. With the power of AI, efficient and reliable transfection studies can be achieved.
本文探讨了人工智能 (AI) 在 2D 细胞培养研究中优化转染效率测量的关键作用。精确可靠的 2D 细胞培养转染效率测量是理解细胞机制的关键。靶向蛋白的高转染效率对于包括活细胞成像和蛋白纯化在内的实验至关重要。手动估计不一致且不可靠。借助人工智能的力量,可以实现高效可靠的转染研究。

Optimize transfection efficiency measurement with AI
利用 AI 优化转染效率测量

The strategic development of AI algorithms is important for precise transfection efficiency measurements. These algorithms may be either pre-trained with prior knowledge or custom trained for unique experimental conditions. Factors like cell morphology, fluorescence intensity, and background noise should be considered to gain insights on cellular dynamics.
人工智能算法的战略发展对于精确的转染效率测量非常重要。这些算法可以是使用先验知识进行预训练的,也可以针对独特的实验条件进行定制训练。应考虑细胞形态、荧光强度和背景噪声等因素,以深入了解细胞动力学。

AI versus manual evaluation
AI 与人工评估的比较

Cases which show AI effectiveness for transfection-efficiency measurements are discussed. Transfection efficiency was measured using the Mateo FL microscope. AI both elevates measurement precision and streamlines the workflow compared to manual estimations.
本文讨论了显示AI在转染效率测量方面的有效性的案例。使用 Mateo FL 显微镜测量转染效率。与手动估计相比,人工智能既提高了测量精度,又简化了工作流程。

Impact on upstream workflows
对上游工作流的影响

Beyond optimizing transfection efficiency measurements, AI also helps streamline upstream workflows for the purification, isolation, and extraction of proteins, microscopy imaging, and flow cytometry. For example, AI algorithms can predict optimal conditions for protein purification based on data, reducing trial and error. Concerning imaging, AI enables automated analysis of images, resulting in faster extraction of meaningful information.
除了优化转染效率测量外,AI 还有助于简化蛋白质纯化、分离和提取、显微镜成像和流式细胞术的上游工作流程。例如,人工智能算法可以根据数据预测蛋白质纯化的最佳条件,从而减少试错。在成像方面,人工智能可以自动分析图像,从而更快地提取有意义的信息。

Find out how Mateo FL ensures the accurate check of transfection efficiency.
了解 Mateo FL 如何确保准确检查转染效率。

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