Towards an Interpretable Classifier for Characterization of Endoscopic Mayo Scores in Ulcerative Colitis Using Raman Spectroscopy.

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.

Nonlinear Multimodal Imaging Characteristics of Early Septic Liver Injury in a Mouse Model of Peritonitis.

Sepsis constitutes a life-threatening organ failure caused by a deregulated host response to infection. Identifying early biomolecular indicators of organ dysfunction may improve clinical decision-making and outcome of patients. Herein we utilized label-free nonlinear multimodal imaging, combining coherent anti-Stokes Raman scattering (CARS), two-photon excited autofluorescence (TPEF), and second-harmonic generation (SHG) to investigate the consequences of early septic liver injury in a murine model of polymicrobial abdominal infection. Liver tissue sections from mice with and without abdominal sepsis were analyzed using multimodal nonlinear microscopy, immunofluorescence, immunohistochemistry, and quantitative reverse transcription polymerase chain reaction (qRT-PCR). Twenty-four hours after the induction of sepsis, hepatic mRNA of inflammatory cytokines and acute phase proteins was upregulated, and liver-infiltrating myeloid cells could be visualized alongside hepatocellular cytoplasmic translocation of high mobility group box 1. According to the statistical analysis based on texture feature extraction followed by the combination of dimension reduction and linear discriminant analysis, CARS (AUC = 0.93) and TPEF (AUC = 0.83) showed an excellent discrimination between liver sections from septic mice and sham-treated mice in contrast to SHG (AUC = 0.49). Spatial analysis revealed no major differences in the distribution of sepsis-associated changes between periportal and pericentral zones. These data suggest early alterations in hepatic lipid distribution and metabolism during liver injury and confirm nonlinear multimodal imaging as a promising complementary method for the real-time, label-free study of septic liver damage.

Toward food analytics: fast estimation of lycopene and ß-carotene content in tomatoes based on surface enhanced Raman spectroscopy (SERS).

Carotenoids are molecules that play important roles in both plant development and in the well-being of mammalian organisms. Therefore, various studies have been performed to characterize carotenoids' properties, distribution in nature and their health benefits upon ingestion. Nevertheless, there is a gap regarding a fast detection of them at the plant phase. Within this contribution we report the results obtained regarding the application of surface enhanced Raman spectroscopy (SERS) toward the differentiation of two carotenoid molecules (namely, lycopene and ß-carotene) in tomato samples. To this end, an e-beam lithography (EBL) SERS-active substrate and a 488 nm excitation source were employed, and a relevant simulated matrix was prepared (by mixing the two carotenoids in defined percentages) and measured. Next, carotenoids were extracted from tomato plants and measured as well. Finally, a combination of principal component analysis and partial least squares regression (PCA-PLSR) was applied to process the data, and the obtained results were compared with HPLC measurements of the same extracts. A good agreement was obtained between the HPLC and the SERS results for most of the tomato samples.