I-Corps: Chemometric fluorescence microscopic imaging and virtual staining for rapid label-free histopathology

I-Corps:化学计量荧光显微成像和虚拟染色,用于快速无标记组织病理学

基本信息

  • 批准号:
    2017396
  • 负责人:
  • 金额:
    $ 5万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2020
  • 资助国家:
    美国
  • 起止时间:
    2020-05-15 至 2022-12-31
  • 项目状态:
    已结题

项目摘要

The broader impact/commercial potential of this I-Corps project is to enable robust, rapid, and label-free biological sample evaluation. Cancer is a worldwide public health problem, and its diagnosis currently depends on evaluation of specimens of biological material. Time-efficient and objective alternative methods are urgently needed to address several shortcomings of the existing process, which is time-consuming, labor-intensive, and subject to interpreter variations. The proposed project is a validated method using light on unprocessed or minimally processed cell and tissue specimens. The proposed method particularly meets the demands of rapid and accurate diagnosis in surgical suites and telepathology laboratories. This I-Corps project is to advance the translation of chemometric fluorescence microscopic imaging and virtual staining (CFM-VS) on unstained cell and tissue specimens. Using endogenous cellular fluorescence, CFM produces 2D images revealing both subcellular morphology and function, visually differentiating specific cell properties including structure, cellular metabolism, and protein production. One unique advantage of CFM is the quantification of the absolute concentration of the endogenous fluorescent biomolecules, enabling reliable and accurate diagnosis. CFM and the derived virtual staining (CFM-VS) have been successfully applied to differentiate and diagnose lung and prostate cancers. The virtually stained images for unstained histological slides not only share the morphology of traditional hematoxylin and eosin (H&E) stained image counterparts, but also indicate the biochemical alterations due to cancer. Attractive features of CFM-VS include: ability to image unprocessed or minimally processed cell and tissue sections in close to real-time; yields virtual H&E stained images familiar to pathologists; eliminates distortions introduced in tissue processing; and robust diagnosis is achieved and may be improved through adaption of the algorithm from learning with the accumulation of data.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
这个I-Corps项目的更广泛的影响/商业潜力是能够进行稳健、快速和无标签的生物样品评价。癌症是一个全球性的公共卫生问题,其诊断目前依赖于对生物材料样本的评估。迫切需要时间有效和客观的替代方法来解决现有过程的几个缺点,这些缺点是耗时,劳动密集型,并受到解释者的变化。 拟定项目是一种在未处理或最低限度处理的细胞和组织标本上使用光照的经确认方法。所提出的方法,特别是满足快速和准确的诊断在手术室和远程病理学实验室的需求。 该I-Corps项目旨在推进未染色细胞和组织标本的化学计量荧光显微成像和虚拟染色(CFM-VS)的翻译。使用内源性细胞荧光,CFM产生2D图像,揭示亚细胞形态和功能,视觉上区分特定的细胞特性,包括结构,细胞代谢和蛋白质生产。CFM的一个独特优势是对内源性荧光生物分子的绝对浓度进行定量,从而实现可靠和准确的诊断。CFM及其衍生的虚拟染色(CFM-VS)已成功应用于肺癌和前列腺癌的鉴别诊断。未染色的组织切片的虚拟染色图像不仅共享传统苏木精和伊红(HE)染色图像对应物的形态,而且指示由于癌症引起的生化改变。CFM-VS的吸引人的特征包括:能够接近实时地对未处理或最小处理的细胞和组织切片进行成像;产生&病理学家熟悉的虚拟HE染色图像;消除组织处理中引入的失真;该奖项反映了NSF的法定使命,被认为是值得的通过使用基金会的知识价值和更广泛的影响审查标准进行评估,

项目成果

期刊论文数量(0)
专著数量(0)
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会议论文数量(0)
专利数量(0)

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Min Xu其他文献

Aero-structural optimization of supersonic wing under thermal environment using adjoint-based optimization algorithm
基于伴随优化算法的热环境下超音速机翼气动结构优化
Simultaneous measurement of double surfaces of transparent lenses with phase measuring deflectometry
使用相位测量偏转法同时测量透明透镜的双面
  • DOI:
    10.1016/j.optlaseng.2020.106356
  • 发表时间:
    2021-02
  • 期刊:
  • 影响因子:
    4.6
  • 作者:
    Junqiang Ye;Zhenqi Niu;Xiangchao Zhang;Wei Wang;Min Xu
  • 通讯作者:
    Min Xu
Outcome of split‐liver transplantation from pediatric donors weighing 25 kg or less
体重 25 公斤或以下的儿科捐献者的分割式肝移植的结果
  • DOI:
  • 发表时间:
    2022
  • 期刊:
  • 影响因子:
    4.6
  • 作者:
    Zhen Wang;W. Gao;C. Dong;Chao Sun;Kai Wang;Wei Zhang;Zhuolun Song;H. Qin;C. Han;Yang Yang;Fubo Zhang;Min Xu;W. Zheng;Z. Shen
  • 通讯作者:
    Z. Shen
Design and Implementation of Power System Grounding Grid Fault System Based on Fuzzy Control Algorithm
基于模糊控制算法的电力系统接地网故障系统的设计与实现
(Invited) ALD High-k as a Common Gate Stack Solution for Nanoelectronics
(特邀)ALD High-k 作为纳米电子学的共栅堆栈解决方案
  • DOI:
    10.1149/1.3372563
  • 发表时间:
    2010
  • 期刊:
  • 影响因子:
    0
  • 作者:
    P. Ye;J. Gu;Yanqing Wu;Min Xu;Y. Xuan;T. Shen;A. Neal
  • 通讯作者:
    A. Neal

Min Xu的其他文献

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{{ truncateString('Min Xu', 18)}}的其他基金

CAREER: Cryo-electron tomography derived multiscale integrative modeling of subcellular organization
职业:冷冻电子断层扫描衍生的亚细胞组织多尺度综合模型
  • 批准号:
    2238093
  • 财政年份:
    2023
  • 资助金额:
    $ 5万
  • 项目类别:
    Continuing Grant
Data-driven selection of a convex loss function via shape-constrained estimation
通过形状约束估计来数据驱动选择凸损失函数
  • 批准号:
    2311299
  • 财政年份:
    2023
  • 资助金额:
    $ 5万
  • 项目类别:
    Standard Grant
Collaborative Research: III: Medium: Systematic De Novo Identification of Macromolecular Complexes in Cryo-Electron Tomography Images
合作研究:III:介质:冷冻电子断层扫描图像中大分子复合物的系统从头识别
  • 批准号:
    2211597
  • 财政年份:
    2022
  • 资助金额:
    $ 5万
  • 项目类别:
    Standard Grant
Inferring the Past on Markovian Models of Networks
根据马尔可夫网络模型推断过去
  • 批准号:
    2113671
  • 财政年份:
    2021
  • 资助金额:
    $ 5万
  • 项目类别:
    Continuing Grant
IIBR Informatics: Reducing the training data annotation cost for learning-based macromolecule identification in cellular electron cryo-tomography
IIBR 信息学:降低细胞电子冷冻断层扫描中基于学习的大分子识别的训练数据注释成本
  • 批准号:
    1949629
  • 财政年份:
    2020
  • 资助金额:
    $ 5万
  • 项目类别:
    Standard Grant
III: Small: Improving automation and speed of macromolecule recognition and localization in cryo-electron tomography using unsupervised deep learning
III:小:使用无监督深度学习提高冷冻电子断层扫描中大分子识别和定位的自动化程度和速度
  • 批准号:
    2007595
  • 财政年份:
    2020
  • 资助金额:
    $ 5万
  • 项目类别:
    Standard Grant
RUI: Cell Growth Laws and Quantitative Microscopy for Cancer Aggressiveness Imaging
RUI:细胞生长规律和癌症侵袭性成像的定量显微镜
  • 批准号:
    1920617
  • 财政年份:
    2018
  • 资助金额:
    $ 5万
  • 项目类别:
    Standard Grant
RUI: Cell Growth Laws and Quantitative Microscopy for Cancer Aggressiveness Imaging
RUI:细胞生长规律和癌症侵袭性成像的定量显微镜
  • 批准号:
    1607664
  • 财政年份:
    2017
  • 资助金额:
    $ 5万
  • 项目类别:
    Standard Grant

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