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相关概念视频

MALDI-TOF Mass Spectrometry01:19

MALDI-TOF Mass Spectrometry

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Mass spectrometry is a powerful characterization technique that can identify and separate a wide variety of compounds ranging from chemical to biological entities, based on their mass-to-charge ratio (m/z). The instruments that allow this detection, known as mass spectrometers, have three components: an ion source, a mass analyzer, and a detector. These spectrometers differ based on the nature of their ion source and analyzers.Matrix-assisted laser desorption ionization (MALDI) is a commonly...
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Tandem Mass Spectrometry01:21

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Tandem mass spectrometry is a technique that uses multiple mass analyzers in series to obtain a higher selectivity and reduce chemical noise during analyte detection. Instruments with multiple analyzers separated by an interaction cell enable secondary fragmentation and selected study of the fragment ions.Secondary fragmentations occur in the interaction cell and can be induced by various factors. Fragmentation induced by collision with inert gases, such as N2, Ar, He, etc., is called...
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Peptide Identification Using Tandem Mass Spectrometry01:33

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Tandem mass spectrometry, also known as MS/MS or MS2, is an analytical technique that employs two mass analyzers. Essentially it is a series of mass spectrometers that helps isolate a particular biomolecule and then helps study its chemical properties.
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相关实验视频

Updated: Jan 10, 2026

Optimal Preparation of Formalin Fixed Samples for Peptide Based Matrix Assisted Laser Desorption/Ionization Mass Spectrometry Imaging Workflows
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用于质谱成像的自动组织检测.

James Denholm1,2, Lucy E Flint1, Jack Richings1

  • 1Integrated Bioanlaysis, Clinical Pharmacology and Safety Sciences (CPSS), AstraZeneca R&D, Cambridge CB4 0WG, U.K.

Journal of the American Society for Mass Spectrometry
|November 25, 2025
PubMed
概括
此摘要是机器生成的。

我们开发了一种用于在质谱成像 (MSI) 中检测组织的自动化方法. 这种方法使用组织图像和卷积神经网络,显著改善MSI分析的组织划分.

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Imaging of Biological Tissues by Desorption Electrospray Ionization Mass Spectrometry

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科学领域:

  • 生物医学成像技术 生物医学成像技术
  • 计算病理学计算病理学
  • 分析化学 分析化学

背景情况:

  • 质谱成像 (MSI) 能够在组织中绘制生物分子的空间映射.
  • 在MSI中手动组织划分是耗时且主观的.
  • 需要自动化方法来简化MSI数据预处理.

研究的目的:

  • 提出一个端到端的方法,用于在质谱图像 (MSI) 中自动检测组织.
  • 开发一个可靠的模型,用于MSI数据中准确的组织细分.
  • 减少MSI分析工作流程中的手动干预.

主要方法:

  • 利用来自同一组织部分的配对MSI和组织图像.
  • 使用QuPath软件进行注释的组织组织面膜.
  • 通过使用地标的亲属转换将组织学面具映射到MSI空间.
  • 开发了代谢物独立的MSI表示 (总离子电流,根-平均-平方,香农).
  • 训练了一个卷积神经网络 (CNN) 用于组织检测.

主要成果:

  • 实现了高交叉验证性能:准确性 (0.953),精度 (0.939),回忆 (0.923),以及索伦森-迪斯 (0.930).
  • 在各种研究的未见测试数据上验证了模型,产生准确性 (0.945),精度 (0.965),回忆 (0.915),和Sørensen-Dice (0.935).
  • 在各种组织类型,有机体和空间分辨率中证明了模型的稳定性.

结论:

  • 提出的基于CNN的方法有效地自动化了MSI中的组织检测.
  • 这种方法显著提高了MSI数据预处理的效率和客观性.
  • 开发的技术适用于各种MSI数据集,促进更广泛的研究应用.