No abstract

Alzheimer's disease is one of the major causes of dementia in the elderly. The disease is caused by the misfolding of water soluble alpha-helical proteins, which leads to the accumulation of β-sheets in the form of amyloid plaques, which can subsequently affect surrounding tissue areas by oxidative stress neurotoxicity. The aim of the present study was to design a novel methodology to analyze the extent to the neuronal burden around protein-rich Aβ plaques suspected to affect molecular components by oxidative stress induced by inflammatory states. To do so, sagittal brain tissue sections from triple transgenic APPxPSP1xTAU mice were used to carry high magnification FTIR-FPA bench-top chemical imaging. The study used the combination of chemometric procedures involving spectral curve fitting and image processing to study the molecular changes occurring around the plaques. The study shows the performance of the approach by demonstrating its usefulness to co-localize molecular changes to different areas around the plaques. The results, although very preliminary, point to the strong interplay between the distance from the plaque and co-accumulation of molecular components indicative of inflammatory states.

Protein-related changes associated with the development of human brain gliomas are of increasing interest in modern neuro-oncology. It is due to the fact that they might make some of these tumors highly aggressive and difficult to treat. This paper presents a methodology for protein-based analysis of human brain gliomas using synchrotron radiation based Fourier transform infrared spectroscopy (SRFTIR) coupled with artificial neural networks (ANNs). The main goal of this study was to optimize a set of ANNs to predict the secondary structure of proteins (alpha-helices, beta-sheets, beta-turns, bends, random coils) in brain gliomas, based on the amide I-II spectral range. All networks were tested and optimized to reach the standard error of prediction (SEP) lower than 5%. The results indicate that protein-related changes are associated with a tumor's malignancy grade. Particularly, the content of alpha helices increases with increasing malignancy grade, while the content of beta sheets decreases. We also found that proteomic information could be a useful marker to distinguish either between low and high grade tumors or between oligodendroglial- and astrocyte-derived ones. This demonstrates the applicability of FTIR coupled with ANNs to provide clinically relevant information.