用于快速精确量化隐球菌亚型的深度学习增强型微孔阵列生物芯片

IF 9.7 4区 医学 Q1 MATERIALS SCIENCE, BIOMATERIALS VIEW Pub Date : 2024-07-18 DOI:10.1002/viw.20240032
Yihang Tong, Yu Zeng, Yinuo Lu, Yemei Huang, Zhiyuan Jin, Zhiying Wang, Yusen Wang, Xuelei Zang, Lingqian Chang, Wei Mu, Xinying Xue, Zaizai Dong
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引用次数: 0

摘要

隐球菌是一种传染性极强的病原体,可导致多种症状,尤其对接受免疫缺陷治疗或药物治疗的患者构成威胁。快速识别隐球菌亚型并准确量化其含量仍是感染控制和及时治疗的迫切需要。然而,传统的检测技术严重依赖于昂贵的专业仪器,大大影响了其在大规模人群筛查中的适用性。在这项工作中,我们报告了一种便携式微孔阵列芯片平台,该平台集成了基于深度学习的图像识别程序,能够快速、精确地量化隐球菌的特定亚型。该平台具有四个预装了亚型靶向 CRISPR-Cas12a 系统的微孔阵列区,避免了对仪器介导的缓慢靶向扩增的依赖,实现了快速(10 分钟)、高特异性地识别隐球菌序列。基于深度学习的图像识别程序利用片段任何模型(SAM)大大提高了识别目标浓度的自动化程度和准确性,最终实现了个人智能手机的超低检测限(0.5 pM)。该平台可进一步定制,以适应临床环境中的各种情况。
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Deep learning-enhanced microwell array biochip for rapid and precise quantification of Cryptococcus subtypes
Cryptococcus is a family of strongly infectious pathogens that results in a wide variety of symptoms, particularly threatening the patients undergoing the immune-deficiency or medical treatment. Rapidly identifying Cryptococcus subtypes and accurately quantifying their contents remain urgent needs for infection control and timely therapy. However, traditional detection techniques heavily rely on expensive, specialized instruments, significantly compromising their applicability for large-scale population screening. In this work, we report a portable microwell array chip platform integrated with a deep learning-based image recognition program, which enables rapid, precise quantification of the specific subtypes of Cryptococcus. The platform features four zones of microwell arrays preloaded with the subtype-targeted CRISPR–Cas12a system that avoid dependence on slow, instrumental-mediated target amplification, achieving rapid (10 min), high specificity for identifying the sequence of Cryptococcus. The deep learning-based image recognition program utilizing segment anything model (SAM) significantly enhances automation and accuracy in identifying target concentrations, which eventually achieves ultra-low limit of detection (0.5 pM) by personal smartphones. This platform can be further customized to adapt to various scenarios in clinical settings.
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来源期刊
VIEW
VIEW Multiple-
CiteScore
12.60
自引率
2.30%
发文量
0
审稿时长
10 weeks
期刊介绍: View publishes scientific articles studying novel crucial contributions in the areas of Biomaterials and General Chemistry. View features original academic papers which go through peer review by experts in the given subject area.View encourages submissions from the research community where the priority will be on the originality and the practical impact of the reported research.
期刊最新文献
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