Study of Methionine Choline Deficient Diet-Induced Steatosis in Mice Using Endogenous Fluorescence Spectroscopy

Gut Microbiota Targeted Approach in the Management of Chronic Liver Diseases

The liver is directly connected to the intestines through the portal vein, which enables the gut microbiota and gut-derived products to influence liver health. There is accumulating evidence of decreased gut flora diversity and alcohol sensitivity in patients with various chronic liver diseases, including non-alcoholic/alcoholic liver disease, chronic hepatitis virus infection, primary sclerosing cholangitis and liver cirrhosis. Increased intestinal mucosal permeability and decline in barrier function were also found in these patients. Followed by bacteria translocation and endotoxin uptake, these will lead to systemic inflammation. Specific microbiota and microbiota-derived metabolites are altered in various chronic liver diseases studies, but the complex interaction between the gut microbiota and liver is missing. This review article discussed the bidirectional relationship between the gut and the liver, and explained the mechanisms of how the gut microbiota ecosystem alteration affects the pathogenesis of chronic liver diseases. We presented gut-microbiota targeted interventions that could be the new promising method to manage chronic liver diseases.

Combined methods of optical spectroscopy and artificial intelligence in the assessment of experimentally induced non-alcoholic fatty liver

BACKGROUND AND OBJECTIVE

Due to the existing prevalence of nonalcoholic fatty liver disease (NAFLD) and its relation to the epidemic of obesity in the general population, it is imperative to develop detection and evaluation methods of the early stages of the disease with improved efficacy over the current diagnostic approaches. We aimed to obtain an improved diagnosis, combining methods of optical spectroscopy -diffuse reflectance and fluorescence- with statistical data analysis applied to detect early stages of NAFLD.

METHODS

Statistical analysis scheme based on quadratic discriminant analysis followed by canonical discriminant analysis were applied to the diffuse reflectance data combined with endogenous fluorescence spectral data excited at one of these wavelengths: 330, 365, 385, 405 or 415 nm. The statistical scheme was also applied to the combinations of fluorescence spectrum (405 nm) with each one of the other fluorescence spectra. Details of the developed software, including the application of machine learning algorithms to the combination of spectral data followed by classification statistical schemes, are discussed.

RESULTS

Steatosis progression was differentiated with little classification error (≤1.3%) by using diffuse reflectance and endogenous fluorescence at different wavelengths. Similar results were obtained using fluorescence at 405 nm and one of the other fluorescence spectra (classification error ≤1.0%). Adding the corresponding areas under the curves to the above combinations of spectra diminished errors to 0.6% and 0.3% or less, respectively. The best results for the compounded reflectance-plus-fluorescence spectra were obtained with fluorescence spectra excited at 415 nm with a total classification error of 0.2%; for the combination of the 405nm-excited fluorescence spectrum with another fluorescence spectrum, the best results were achieved for 385 nm, for which total relative classification error amounted 0.4%. The consideration of the area under the spectral curves further improved both classifiers, reducing the error to 0.0% in both cases.

CONCLUSION

Spectrometric techniques combined with statistical processing are a promising tool to improve steatosis classification through a label free approach. However, statistical schemes here applied, might result complex for the everyday medical practice, the designed software including machine learning algorithms is able to render automatic classification of samples according to their steatosis grade with low error.

Photobiology and Endogenous Fluorophore Based Applications, from Natural Environment to Biomedicine to Improve Human Life

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