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。
用绿色荧光标记物标记的 U2OS 细胞
