Machine learning-driven Raman spectroscopy: A novel approach to lipid profiling in diabetic kidney disease.

Diabetes mellitus is a chronic metabolic disease that increasingly affects people every year. It is known that with its progression and poor management, metabolic changes can lead to organ dysfunctions, including kidneys. The study aimed to combine Raman spectroscopy and biochemical lipid profiling, complemented by machine learning (ML) techniques to evaluate chemical composition changes in kidneys induced by Type 2 Diabetes mellitus (T2DM).

Lipids balance as a spectroscopy marker of diabetes. Analysis of FTIR spectra by 2D correlation and machine learning analyses.

The number of people suffering from type 2 diabetes has rapidly increased. Taking into account, that elevated intracellular lipid concentrations, as well as their metabolism, are correlated with diminished insulin sensitivity, in this study we would like to show lipids spectroscopy markers of diabetes. For this purpose, serum collected from rats (animal model of diabetes) was analyzed using Fourier Transformed Infrared-Attenuated Total Reflection (FTIR-ATR) spectroscopy.

[Evaluation of lipid metabolism in patients with chronic, active hepatitis (HBsAg positive)].

Selected parameters of the lipid metabolism were evaluated in patients with HBsAg-positive chronic, active hepatitis. A decrease in alpha-lipoproteins and an increase in plasma biliary acids level were found.

[Trace elements: zinc, copper and magnesium in patients with chronic persisting hepatitis--evaluation of changes during therapy].

Serum Zn2+, Cu2+, and Mg2+ were assayed in patients with chronic persisting hepatitis HBsAg-positive. Significantly decreased serum magnesium with increased serum copper levels were noted.