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. Analyzed spectra showed that rats with diabetes presented higher concentration of phospholipids and cholesterol in comparison with non-diabetic rats. Moreover, the analysis of second (IInd) derivative spectra showed no structural changes in lipids. Machine learning methods showed higher accuracy for IInd derivative spectra (from 65 % to 89 %) than for absorbance FTIR spectra (53-65 %). Moreover, it was possible to identify significant wavelength intervals from IInd derivative spectra using random forest-based feature selection algorithm, which further increased the accuracy of the classification (up to 92 % for phospholipid region). Moreover decision tree based on the selected features showed, that peaks at 1016 cm-1 and 2936 cm-1 can be good candidates of lipids marker of diabetes.

Crystal Clear: Metal-Organic Frameworks Pioneering the Path to Future Drug Detox.

The growing number of acute drug abuse overdoses demands the development of innovative detoxification strategies for emergency purposes. In this study, an innovative approach for the application of porous Zr-based metal-organic frameworks for the treatment of acute overdoses of popular drugs of abuse including amphetamine, methamphetamine, cocaine, and MDMA is presented. A comprehensive approach determining the efficacy and the kinetics of drug removal, considering dosage, adsorption time, and adsorption mechanisms, was tested and corroborated with density functional theory (DFT) modeling. The experimental results showed high removal efficiency reaching up to 90% in the case of the application of the NU-1000 metal-organic framework. The difference Raman spectroscopy method presented in this study corroborated with DFT-based vibrational analysis allows the detection of drug adsorbed in the MOF framework even with as low a concentration as 5 mg/g. Additionally, the drug adsorption mechanisms were modeled with DFT, showing the π-π stacking in a vast majority of considered cases. The performance and influence on the living organisms were evaluated throughout the in vitro and in vivo experiments, indicating that Zr-based MOFs could serve as efficient, organic, safe drug adsorbents.