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Context: Alzheimer's disease (AD) is the leading cause of dementia around the world, totaling about 55 million cases, with an estimated growth to 74.7 million cases in 2030, which makes its treatment widely desired. Several studies and strategies are being developed considering the main theories regarding its origin since it is not yet fully understood. Among these strategies, the 5-HT 6 receptor antagonism emerges as an auspicious and viable symptomatic treatment approach for AD. The 5-HT 6 receptor belongs to the G protein-coupled receptor (GPCR) family and is closely implicated in memory loss processes. As a serotonin receptor, it plays an important role in cognitive function. Consequently, targeting this receptor presents a compelling therapeutic opportunity. By employing antagonists to block its activity, the 5-HT 6 receptor's functions can be effectively modulated, leading to potential improvements in cognition and memory., Methods: Addressing this challenge, our research explored a promising avenue in drug discovery for AD, employing Artificial Neural Networks-Quantitative Structure-Activity Relationship (ANN-QSAR) models. These models have demonstrated great potential in predicting the biological activity of compounds based on their molecular structures. By harnessing the capabilities of machine learning and computational chemistry, we aimed to create a systematic approach for analyzing and forecasting the activity of potential drug candidates, thus streamlining the drug discovery process. We assembled a diverse set of compounds targeting this receptor and utilized density functional theory (DFT) calculations to extract essential molecular descriptors, effectively representing the structural features of the compounds. Subsequently, these molecular descriptors served as input for training the ANN-QSAR models alongside corresponding biological activity data, enabling us to predict the potential efficacy of novel compounds as 5-hydroxytryptamine receptor 6 (5-HT 6 ) antagonists. Through extensive analysis and validation of ANN-QSAR models, we identified eight new promising compounds with therapeutic potential against AD., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The aim of this study was to evaluate the effects of photobiomodulation (PBM) by dual-wavelength low-power lasers on the healing and bacterial bioburden of pressure ulcer (PU) models. Twenty-five male Swiss mice were divided into five equal groups. Ischemia reperfusion cycles were employed to cause PU formation by the external application of magnetic plates. Immediately after wounding, a suspension of Pantoea agglomerans was applied at the base of all the wounds of the infected groups, using a calibrated pipette. PBM (simultaneous emission at 660 and 808 nm, 142.8 J/cm 2 , in continuous wave emission mode) was applied to the PUs for 14 sessions. The animals were euthanized 14 days after PU induction, and their tissues were analyzed for wound contraction and reepithelialization, epidermis thickness, bacterial survival, and IL-1β and IL-10 mRNA level evaluations. The PU areas appeared larger in the mice from the infected groups than in those in the laser group 4 days after PU induction and presented incomplete reepithelialization 14 days after PU induction. However, the PBM accelerated the wound healing in the infected + laser group compared with the infected group 11 and 14 days following the PU induction. The infected and irradiated PUs exhibited a thinner neo-epidermis than those in the infected group, and the bacterial survival decreased in the laser group; the relative expression IL-1β mRNA levels demonstrated an increasing tendency while the relative expression IL-10 mRNA levels demonstrated a decreasing tendency in the infected + laser and laser groups. These results suggest that PBM improves healing by killing or inhibiting bacteria in PUs as well as by accelerating the wound healing, resulting in tissue repair.

While many bacteria have been used as probiotics by industries, only two yeasts, Saccharomyces cerevisiae var. boulardii and Kluyveromyces fragilis (B0399), have been used for this purpose. In the present work, a total of 116 yeasts isolated from Brazilian indigenous fermented food, cocoa fermentation, and kefir were in vitro characterized for probiotic attributes. From 116 isolates, 36 were tolerant to gastrointestinal conditions evaluated by tolerance to pH 2.0, bile salts (0.3% w/v), and 37 °C temperature. From those, 15 isolates showed a similar or higher percentage (P < 0.05) of hydrophobicity, autoaggregation, and coaggregation with E. coli than the reference strain S. boulardii. All these strains showed a high percentage of adhesion to Caco-2 cells (> 63%) and antioxidant activity (ranging from 18 to 62%). Phytate hydrolysis was evaluated for these yeasts and 13 strains showed positive results, which is important for nutrient availability in plant-based foods. These results are important insights for characterization of novel probiotic yeast strains as well as to aggregate functional value to these food products.