No abstract

Ulcerative colitis (UC) is one of the main types of chronic inflammatory diseases that affect the bowel, but its pathogenesis is yet to be completely defined. Assessing the disease activity of UC is vital for developing a personalized treatment. Conventionally, the assessment of UC is performed by colonoscopy and histopathology. However, conventional methods fail to retain biomolecular information associated to the severity of UC and are solely based on morphological characteristics of the inflamed colon. Furthermore, assessing endoscopic disease severity is limited by the requirement for experienced human reviewers. Therefore, this work presents a nondestructive biospectroscopic technique, for example, Raman spectroscopy, for assessing endoscopic disease severity according to the four-level Mayo subscore. This contribution utilizes multidimensional Raman spectroscopic data to generate a predictive model for identifying colonic inflammation. The predictive modeling of the Raman spectroscopic data is performed using a one-dimensional deep convolutional neural network (1D-CNN). The classification results of 1D-CNN achieved a mean sensitivity of 78% and a mean specificity of 93% for the four Mayo endoscopic scores. Furthermore, the results of the 1D-CNN are interpreted by a first-order Taylor expansion, which extracts the Raman bands important for classification. Additionally, a regression model of the 1D-CNN model is constructed to study the extent of misclassification and border-line patients. The overall results of Raman spectroscopy with 1D-CNN as a classification and regression model show a good performance, and such a method can serve as a complementary method for UC analysis.

Nanocontainers based on solid materials have great potential for drug delivery applications. However, since nanocontainer-mediated delivery can alter the drug internalization pathways and metabolism, it is important to find out what are the mechanisms of cancer cell death induced by nanocontainers and, moreover, is it possible to regulate them. Here, we report on the detailed investigation of the internalization kinetics and intracellular spatial distribution of porous silicon nanoparticles (PSi NPs) loaded with doxorubicin (DOX) and response of cancer cells to treatment with DOX-PSi NPs as well as studies of nanocontainer biodegradation by applying various microscopy methods, Raman microspectroscopy and biological experiments with cancer cells of different etiology. The obtained results revealed the absence of toxicity of unloaded PSi NPs to cancer cells up to a concentration of 700 μg/mL during the prolonged incubation time. Thus, given the fact that the nanocontainers themselves are not toxic, it is easy to adjust the dose of the drug that they deliver to the cells. It is shown, that the treatment with DOX-loaded PSi NPs more efficiently eliminates cancer cells in comparison with the free DOX. At the same time, the obtained results demonstrate the possibility of regulating the initiation of apoptosis or necrosis in tumor cells after treatment with different concentrations of DOX-PSi NPs, as revealed by the analysis of the caspase-3 processing, the accumulation of sub-G1 cell fraction, and morphological changes determined by electron and light microscopy. The obtained results are important for future applications of porous silicon nanocontainers in drug delivery for apoptotic pathway-targeted cancer therapy.