Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Automated Microbial Diagnostics01:24

Automated Microbial Diagnostics

77
Automated diagnostic analyzers have transformed clinical microbiology by providing rapid and reliable methods for pathogen identification and antibiotic susceptibility testing. Among these systems, the Vitek 2 is widely used because it automates the traditionally labor-intensive processes of microbial identification (ID) and antibiotic susceptibility testing (AST), delivering standardized and timely results that are essential for effective patient care.Microbial Identification with ID CardsThe...
77

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Lymph Node Metastasis Pattern and Influencing Factors in Esophageal Squamous Cell Carcinoma.

Aging medicine (Milton (N.S.W))·2026
Same author

PregMedNet: Multifaceted maternal medication impacts on neonatal complications.

Nature communications·2026
Same author

Control of static levitation attitude based on CNF-LADRC for distributed electric-drive maglev car.

ISA transactions·2026
Same author

PulmoX-Net: a channel-attention enhanced deep learning model for multi-class pulmonary pathology classification in chest radiography.

Frontiers in medicine·2026
Same author

Non-gene-edited, CD19-targeted, allogeneic CAR-T cell therapy for relapsed or refractory B-cell acute lymphoblastic leukemia: an open-label, single-arm phase 1 study.

Bone marrow transplantation·2026
Same author

TNFSF10: a promising prognostic biomarker and therapeutic target for immunotherapy in testicular germ cell tumors.

Frontiers in immunology·2026

相关实验视频

Updated: Apr 30, 2026

Multiplex Detection of Bacteria in Complex Clinical and Environmental Samples using Oligonucleotide-coupled Fluorescent Microspheres
11:09

Multiplex Detection of Bacteria in Complex Clinical and Environmental Samples using Oligonucleotide-coupled Fluorescent Microspheres

Published on: October 23, 2011

16.7K

基于金纳米集群的传感器阵列与PCA辅助的模式识别,用于在不同复杂的生物样本中区分多种抗生素.

Pingping Liu1, Jiaheng Shi2, Ying Wang3

  • 1Zhengzhou Tobacco Research Institute, CNTC, Zhengzhou 450000, P. R. China.

Analytical chemistry
|January 8, 2026
PubMed
概括

这项研究引入了一种使用金纳米集群的新型传感器阵列,用于快速识别生物样本中的抗生素. 该系统简化了传感器设计,并实现了高精度,有利于健康监测和食品安全.

更多相关视频

Bacterial Detection & Identification Using Electrochemical Sensors
09:30

Bacterial Detection & Identification Using Electrochemical Sensors

Published on: April 23, 2013

28.9K
Electronic Tongue Generating Continuous Recognition Patterns for Protein Analysis
08:46

Electronic Tongue Generating Continuous Recognition Patterns for Protein Analysis

Published on: September 16, 2014

8.2K

相关实验视频

Last Updated: Apr 30, 2026

Multiplex Detection of Bacteria in Complex Clinical and Environmental Samples using Oligonucleotide-coupled Fluorescent Microspheres
11:09

Multiplex Detection of Bacteria in Complex Clinical and Environmental Samples using Oligonucleotide-coupled Fluorescent Microspheres

Published on: October 23, 2011

16.7K
Bacterial Detection & Identification Using Electrochemical Sensors
09:30

Bacterial Detection & Identification Using Electrochemical Sensors

Published on: April 23, 2013

28.9K
Electronic Tongue Generating Continuous Recognition Patterns for Protein Analysis
08:46

Electronic Tongue Generating Continuous Recognition Patterns for Protein Analysis

Published on: September 16, 2014

8.2K

科学领域:

  • 纳米技术 纳米技术
  • 分析化学 分析化学
  • 生物医学工程 生物医学工程

背景情况:

  • 传统的抗生素识别方法复杂且昂贵.
  • 需要在健康监测和食品安全方面进行快速,准确的检测.

研究的目的:

  • 开发一个简化的,无标签的传感器阵列,以快速区分抗生素.
  • 实现可编程传感器设计,使用最小的联结物调制.

主要方法:

  • 使用金纳米集群 (AuNCs) 作为核心传感器材料.
  • 在Au NC表面上采用连接物调制,用于抗生素识别.
  • 应用主要组件分析 (PCA) 用于模式识别.
  • 进行光共振能量转移 (FRET) 和分子对接研究.

主要成果:

  • 在复杂的生物矩阵中实现了多种抗生素的快速区分.
  • 在对人体血清,尿液和牛奶进行的盲测试中证明了100%的准确性.
  • 通过调制仅四个配体分子,使抗生素识别成为可能.
  • 通过PCA辅助的识别,可以在3分钟内对抗生素存在进行区分.

结论:

  • 开发的Au NC传感器阵列为抗生素检测提供了简化和有效的方法.
  • 该系统显示了生命科学和食品安全领域实时应用的巨大潜力.
  • 这种方法解决了在复杂样本中直接识别抗生素的关键需求.