RESUMO
Different types of milk are used in the production of milk kefir, but little information is available on the release of potentially antioxidant exopolysaccharides (EPS). The aim of this study was to investigate whether the microbial dynamics and EPS release are dependent on the milk substrate. In our study, the inoculated microbial consortium was driven differently by each type of milk (cow, ewe, and goat). This was evident in the sugar consumption, organic acid production, free amino release, and EPS production. The amount and the composition of the secreted EPS varied depending on the milk type, with implications for the structure and functional properties of the EPS. The low EPS yield in ewe's milk was associated with a higher lactic acid production and thus with the use of carbon sources oriented towards energy production. Depending on the milk used as substrate, the EPS showed different monosaccharide and FT-IR profiles, microstructures, and surface morphologies. These differences affected the antiradical properties and reducing power of the EPS. In particular, EPS extracted from cow's milk had a higher antioxidant activity than other milk types, and the antioxidant activity was concentration dependent.
RESUMO
The presented study protocol outlines a comprehensive investigation into the interplay among the human microbiota, volatilome, and disease biomarkers, with a specific focus on Behçet's disease (BD) using methods based on explainable artificial intelligence. The protocol is structured in three phases. During the initial three-month clinical study, participants will be divided into control and experimental groups. The experimental groups will receive a soluble fiber-based dietary supplement alongside standard therapy. Data collection will encompass oral and fecal microbiota, breath samples, clinical characteristics, laboratory parameters, and dietary habits. The subsequent biological data analysis will involve gas chromatography, mass spectrometry, and metagenetic analysis to examine the volatilome and microbiota composition of salivary and fecal samples. Additionally, chemical characterization of breath samples will be performed. The third phase introduces Explainable Artificial Intelligence (XAI) for the analysis of the collected data. This novel approach aims to evaluate eubiosis and dysbiosis conditions, identify markers associated with BD, dietary habits, and the supplement. Primary objectives include establishing correlations between microbiota, volatilome, phenotypic BD characteristics, and identifying patient groups with shared features. The study aims to identify taxonomic units and metabolic markers predicting clinical outcomes, assess the supplement's impact, and investigate the relationship between dietary habits and patient outcomes. This protocol contributes to understanding the microbiome's role in health and disease and pioneers an XAI-driven approach for personalized BD management. With 70 recruited BD patients, XAI algorithms will analyze multi-modal clinical data, potentially revolutionizing BD management and paving the way for improved patient outcomes.
