Non-invasive strategy in assessing asthma through biofluids metabolomics exploration: exhaled breath and urine potentialities

Asthma is a significant health issue in the pediatric population with a noteworthy growth over the years. The proposed challenge for this PhD thesis was the development of advanced methodologies to establish metabolomic patterns in urine and exhaled breath associated with asthma whose applicability was subsequently exploited to evaluate the disease state, the therapy adhesion and effect and for diagnostic purposes. The volatile composition of exhaled breath was studied combining headspace solid phase microextraction (HS-SPME) with gas chromatography coupled to mass spectrometry or with comprehensive two-dimensional gas chromatography coupled to mass spectrometry with a high resolution time of flight analyzer (GC×GC–ToFMS). These methodologies allowed the identification of several hundred compounds from different chemical families. Multivariate analysis (MVA) led to the conclusion that the metabolomic profile of asthma individuals is characterized by higher levels of compounds associated with lipid peroxidation, possibly linked to oxidative stress and inflammation (alkanes and aldehydes) known to play an important role in asthma. For future applications in clinical settings a set of nine compounds was defined and the clinical applicability was proven in monitoring the disease status and in the evaluation of the effect and / or adherence to therapy. The global volatile metabolome of urine was also explored using an HSSPME/GC×GC–ToFMS method and c.a. 200 compounds were identified. A targeted analysis was performed, with 78 compounds related with lipid peroxidation and consequently to oxidative stress levels and inflammation. The urinary non-volatile metabolomic pattern of asthma was established using proton nuclear magnetic resonance (1H NMR). This analysis allowed identifying central metabolic pathways such as oxidative stress, amino acid and lipid metabolism, gut microflora alterations, alterations in the tricarboxylic acid (TCA) cycle, histidine metabolism, lactic acidosis, and modification of free tyrosine residues after eosinophil stimulation. The obtained results allowed exploring and demonstrating the potential of analyzing the metabolomic profile of exhaled air and urine in asthma. Besides the successful development of analysis methodologies, it was possible to explore through exhaled air and urine biochemical pathways affected by asthma, observing complementarity between matrices, as well as, verify the clinical applicability.

Color measurement using a smartphone applied to Madeira wines

Computer vision is a field that uses techniques to acquire, process, analyze and understand images from the real world in order to produce numeric or symbolic information in the form of decisions [1]. This project aims to use computer vision to prepare an app to analyze a Madeira Wine and characterize it (identify its variety) by its color. Dry or sweet wines, young or old wines have a specific color. It uses techniques to compare histograms in order to analyze the images taken from a test sample inside a special container designed for this purpose. The color analysis from a wine sample using an image captured by a smartphone can be difficult. Many factors affect the captured image such as, light conditions, the background of the sample container due to the many positions the photo can be taken (different to capture facing a white wall or facing the floor for example). Using new technologies such as 3D printing it was possible to create a prototype that aims to control the effect of those external factors on the captured image. The results for this experiment are good indicators for future works. Although it’s necessary to do more tests, the first tests had a success rate of 80% to 90% of correct results. This report documents the development of this project and all the techniques and steps required to execute the tests.