A combined X-ray fluorescence and infrared microspectroscopy study for new insights into elemental-biomolecular obesity-induced changes in rat brain structures.

The objective of our research was to determine the brain changes at the molecular and elemental levels typical of early-stage obesity. Therefore a combined approach using Fourier transform infrared micro-spectroscopy (FTIR-MS) and synchrotron radiation induced X-ray fluorescence (SRXRF) was introduced to evaluate some brain macromolecular and elemental parameters in high-calorie diet (HCD)- induced obese rats (OB, n = 6) and in their lean counterparts (L, n = 6). A HCD was found to alter the lipid- and protein- related structure and elemental composition of the certain brain areas important for energy homeostasis. The increased lipid unsaturation in the frontal cortex and ventral tegmental area, the increased fatty acyl chain length in the lateral hypothalamus and substantia nigra as well as the decreased both protein α helix to protein ß- sheet ratio and the percentage fraction of ß-turns and ß-sheets in the nucleus accumbens were revealed in the OB group reflecting obesity-related brain biomolecular aberrations. In addition, the certain brain elements including P, K and Ca were found to differentiate the lean and obese groups at the best extent. We can conclude that HCD-induced obesity triggers lipid- and protein- related structural changes as well as elemental redistribution within various brain structures important for energy homeostasis. In addition, an approach applying combined X-ray and infrared spectroscopy was shown to be a reliable tool for identifying elemental-biomolecular rat brain changes for better understanding the interplay between the chemical and structural processes involved in appetite control.

An Approach to Automatic Hard Exudate Detection in Retina Color Images by a Telemedicine System Based on the d-Eye Sensor and Image Processing Algorithms.

Hard exudates are one of the most characteristic and dangerous signs of diabetic retinopathy. They can be marked during the routine ophthalmological examination and seen in color fundus photographs (i.e., using a fundus camera). The purpose of this paper is to introduce an algorithm that can extract pathological changes (i.e., hard exudates) in diabetic retinopathy. This was a retrospective, nonrandomized study. A total of 100 photos were included in the analysis-50 sick and 50 normal eyes. Small lesions in diabetic retinopathy could be automatically diagnosed by the system with an accuracy of 98%. During the experiments, the authors used classical image processing methods such as binarization or median filtration, and data was read from the d-Eye sensor. Sixty-seven patients (39 females and 28 males with ages ranging between 50 and 64) were examined. The results have shown that the proposed solution accuracy level equals 98%. Moreover, the algorithm returns correct classification decisions for high quality images and low quality samples. Furthermore, we consider taking retina photos using mobile phones rather than fundus cameras, which is more practical. The paper presents an innovative approach. The results are introduced and the algorithm is described.