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深度学习辅助的PfAgo可编程遗传电路用于超敏感的视觉检测食品传播病原体的单管

Yating Han¹, Junping Wen², Zhi Zhu¹

¹College of Food Science and Technology, Huazhong Agricultural University, Wuhan, 420070, Hubei, China; State Key Laboratory of Marine Food Processing and Safety Control and Academy of Food Interdisciplinary Science, School of Food Science and Technology, Dalian Polytechnic University, Dalian, Liaoning, 116034, China.

Biosensors & bioelectronics

|December 19, 2025

概括

使用Pyrococcus furiosus Argonaute (PfAgo) 和V型PCR (VPCR) 的新型检测方法可以快速,超灵敏地检测食源性病原体. 这种方法提高了敏感性,并加快了改善食品安全查的结果.

科学领域:

分子生物学分子生物学
生物技术是生物技术.
食品科学食品科学食品科学

背景情况:

食物传播病原体对公共卫生构成重大风险,需要快速和敏感的检测方法.
目前基于Pyrococcus furiosus Argonaute (PfAgo) 的病原体检测方法面临着长时间分析和低裂解效率等挑战.

研究的目的:

开发一种超灵敏的视觉测试,以提高速度和效率检测食源性病原体.
整合一个可编程的基因电路与超快的热循环PCR和深度学习,以增强病原体检测.

主要方法:

设计了一个基于PfAgo的双探头可编程基因电路,与V型PCR (VPCR) 兼容.
使用超快速热循环V型PCR (VPCR) 进行一管检测过程.
开发了基于深度学习的光图像识别技术 (DL-FIR),用于快速,高通量结果分析.

主要成果:

达到1CFU/mL的检测极限,显著提高了灵敏度.
放大光信号超过200%,提高了检测能力.
与顺序切割方法相比,检测时间减少了三分之一,使得查速度更快.

结论:

开发的测试方法简单,灵敏,并且适用于检测食物传播病原体的通用性.
在爆发期间,PfAgo,VPCR和DL-FIR的整合为大规模的现场查提供了一个强大的工具.
这一进步有助于改善食品安全和公共卫生保护.

关键词:

深度学习是一种深度学习.一个一管的管.这是PfAgo.可编程的遗传电路

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