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    Home > Biochemistry News > Biotechnology News > Cancer Research: Using Raman spectroscopy to predict whether immunotherapy is effective

    Cancer Research: Using Raman spectroscopy to predict whether immunotherapy is effective

    • Last Update: 2021-10-19
    • Source: Internet
    • Author: User
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    Researchers at Johns Hopkins University have recently used non-invasive optical probes for the first time to understand the complex changes that occur in tumors after immunotherapy


    Ishan Barman, an associate professor in the Department of Mechanical Engineering at Johns Hopkins University, said: “Immunotherapy is really as effective as magic and fundamentally changes the way we manage cancer


    The research team used a technique called Raman spectroscopy, which uses light to determine the molecular composition of materials


    Until recently, Raman spectroscopy was optimized for biomedical applications


    He believes that one of the benefits of Raman spectroscopy is that it provides fine molecular specificity


    "We hope to have a more comprehensive understanding of the tumor microenvironment, rather than targeting some suspicious molecules


    The research team used Raman data (approximately 7,500 spectral data points for 25 tumors) to train an algorithm to determine a series of features induced by immunotherapy


    They used data from different mice to build a machine learning classifier and test its performance


    The research team reports that these results show good prospects


    These differences are subtle, but statistically significant, and consistent with the results of proteomic analysis performed on the samples, indicating that the technology is expected to provide some early signs of whether the tumor is responding to immunotherapy


    More research is needed in the future, but the research team believes that their work will pave the way for the development of a way to predict whether patients will respond positively to immunotherapy


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    Santosh Kumar Paidi, Joel Rodriguez Troncoso, Piyush Raj, Paola Monterroso Diaz, Jesse D Ivers, David E Lee, Nathan L Avaritt, Allen J.


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