Bortezomib is one of the most researched proteasome inhibitor drug in cancer cell research. Studying its effects, measuring and monitoring treatment response and effectiveness is a widely developing area in cancer research. The introduction of non-invasive measurement tools into the this research is a very important and desirable development, as it is a promising alternative to existing chemical tests. In our work we presented multimodal methodology connecting multiple non-invasive and label-free techniques to study effects of bortezomib on RPMI8226 cells. We connected digital holographic microscopy and holographic tomography with chemical specificity from Raman micro spectroscopy and we showed that treatment with bortezomib caused decrease of RI in the cells and their nucleolus and that changes in chemical compositions after treatment indicate cell apoptosis.
We present a stimulated Raman scattering (SRS) microscope integrated with a novel fiber-based light source. Our light source provides two synchronized pulse trains with 100 mW average power each, independently tunable in the range of 913 to 930 nm and 1024 to 1034 nm, respectively, thus enabling SRS measurements across the 990 to 1300 cm-1 spectral range with a spectral resolution of 15 cm-1. We demonstrate the SRS imaging of leukemic cells recorded in a few seconds. Our system may find potential application in biomedicine, in particular, helping to accelerate the diagnostics and follow-up treatment of leukemia patients.
Raman spectroscopy is a label-free, non-invasive spectroscopic technique, which can be utilized for many biomedical and diagnostic investigations. To do so, chemometric modelling strategies are used, but they lead to a low generalizability of the models. To tackle this issue we investigated transfer learning (TL) approaches for deep learning (DL) based modelling of Raman spectra for classification of three bacterial spore species. In initial test we found that TL can facilitate the usage of DL for time-consuming measurement modalities, because it can help to deal with low dataset sizes.
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