深度学习辅助无透镜全息集成Pd纳米酶噬菌体用于活菌快速无提取检测。

IF 6.7 1区 化学 Q1 CHEMISTRY, ANALYTICAL
Chen Zhan,Peng Lu,Minjie Han,Xiaoqian Hao,Sanyang Han,Yang Zhou,Shu Wang,Yiping Chen
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引用次数: 0

摘要

细菌感染是对全球公共卫生的严重威胁,往往导致严重的传染病,甚至死亡。预防疫情的最有效方法是实施快速、准确和简单的即时检测。在这里,我们开发了一个深度学习辅助的无透镜全息生物传感平台,集成了Pd纳米酶噬菌体,用于快速和无提取的活菌检测,实现了现场测试。噬菌体专门识别和捕获目标活菌。然后,表面配体工程的Pd纳米酶噬菌体触发酪胺信号扩增反应,从而形成聚苯乙烯微球-细菌-磁性纳米颗粒复合物。因此,随着细菌的捕获,未结合的聚苯乙烯微球的数量减少。此外,Pd纳米酶与噬菌体结合可以就地消灭细菌,同时防止二次交叉污染。便携式无透镜全息显微镜具有超宽视场、低成本和轻量化设计等优点,能够以高通量的方式准确捕获上清液中的微球全息图。基于变压器的深度学习算法与数字全息重建相结合,实现了对探针全息图的高速精确处理。作为概念证明,我们的方法已经成功地在16分钟内以高灵敏度(~ 20 CFU/mL)定量检测活鼠伤寒沙门菌,而无需额外的核酸提取。它已经使用各种真实样品进行了评估,显示出作为下一代生物传感智能生物测定的巨大前景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep Learning-Assisted Lens-Free Holography Integrated Pd Nanozyme-Armed Phages for the Rapid and Extraction-Free Detection of Viable Bacteria.
Bacterial infections represent a serious global threat to public health, often leading to severe infectious diseases and even fatalities. The most effective approach to prevent outbreaks is through the implementation of rapid, accurate, and simple point-of-care testing. Here, we developed a deep learning-assisted lens-free holography biosensing platform integrated with Pd nanozyme-armed phages for the rapid and extraction-free detection of viable bacteria, enabling on-site testing. Phages specifically recognize and capture target viable bacteria. Surface ligand-engineered Pd nanozyme-armed phages then trigger a tyramine signal amplification reaction, resulting in the formation of polystyrene microsphere-bacteria-magnetic nanoparticle complexes. Consequently, the number of unbound polystyrene microspheres decreases with bacterial capture. Additionally, the combination of Pd nanozyme and phage can eliminate bacteria in situ while preventing secondary cross-contamination. Portable lens-free holographic microscope offers several advantages, including an ultrawide field of view, low cost, and a lightweight design, enabling the accurate capture of microsphere holograms in the supernatant through a high-throughput manner. The transformer-based deep learning algorithm integrated with digital holographic reconstruction has been trained to precisely process the probe holograms at high speed. As proof of concept, our approach has successfully enabled the quantitative detection of viable Salmonella typhimurium with high sensitivity (∼20 CFU/mL) in 16 min, without additional nucleic acid extraction. It has been evaluated using various real samples, demonstrating great promise as an intelligent bioassay for next-generation biosensing.
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来源期刊
Analytical Chemistry
Analytical Chemistry 化学-分析化学
CiteScore
12.10
自引率
12.20%
发文量
1949
审稿时长
1.4 months
期刊介绍: Analytical Chemistry, a peer-reviewed research journal, focuses on disseminating new and original knowledge across all branches of analytical chemistry. Fundamental articles may explore general principles of chemical measurement science and need not directly address existing or potential analytical methodology. They can be entirely theoretical or report experimental results. Contributions may cover various phases of analytical operations, including sampling, bioanalysis, electrochemistry, mass spectrometry, microscale and nanoscale systems, environmental analysis, separations, spectroscopy, chemical reactions and selectivity, instrumentation, imaging, surface analysis, and data processing. Papers discussing known analytical methods should present a significant, original application of the method, a notable improvement, or results on an important analyte.
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