Multiple myeloma may develop resistance to certain drugs during chemotherapy, which have a fatal impact on treatment efficacy. At present, the drug resistance detection methods for multiple myeloma, such as proteomic identification and clone formation analysis, are relatively complex, and the accuracy and detection time are not ideal. In our work, laser tweezers Raman spectroscopy was used to collect 412 groups of spectra of two kinds of cells, namely, MM.1R and MM.1S, which were respectively resistant to dexamethasone and sensitive to dexamethasone. We selected support vector machine, random forest, linear discriminant analysis and other algorithms to train the pretreated Raman spectra, and the recognition accuracy on the test set was above 95%. This result shows that the combination of laser tweezers Raman spectroscopy and artificial intelligence algorithm can quickly detect drug resistance of cancer cells.
Multi-species blood identification is especially useful in animal quarantine, import and export, criminal cases, forensic examination, and wildlife conservation. Raman spectroscopy is a non-destructive, label-free, and highly specific method for providing chemical information on materials and serving as an analytical tool to characterize biological samples. Machine learning approaches in conjunction with Raman tweezers are proposed to extract the Raman spectral characteristics of single red blood cells (RBCs), then the cells based on the spectral characteristics are classified, and some classification prediction models to achieve single cell identification of different blood species are achieved finally.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.