Danilo Lofaro

Gut dysbiosis refers to an imbalance in gut microbiota composition and function. Opuntia ficus-indica extract has been shown to modulate gut microbiota by improving SCFA production in vivo and gastrointestinal discomfort (GD) in humans. The aim of this study was to demonstrate the efficacy of OdiliaTM on gastrointestinal health by changing the microbial diversity of species involved in inflammation, immunity, oxidation, and the brain–gut–muscle axis. A randomized, double-blind clinical trial was conducted in 80 adults with gut dysbiosis. The intervention consisted of a 300 mg daily intake of OdiliaTM (n = 40) or maltodextrin as a placebo (n = 40), administered for 8 weeks. Intervention effect was evaluated using 16S metagenomics and GIQLI/GSAS scores at baseline, at 4 and 8 weeks. Eight weeks of OdiliaTM supplementation positively modulates gut microbiota composition with a significant reduction in the Firmicutes to Bacteroidetes ratio (p = 0.0012). Relative abundances of beneficial bacteria (Bacteroides and Clostridium_XIVa) were significantly increased (p < 0.001), in contrast to a significant reduction in pro-inflammatory bacteria (p < 0.001). Accordingly, GIQLI and GSAS scores revealed successful improvement in GD. OdiliaTM may represent an effective and well-tolerated treatment in subjects with gut dysbiosis.

Support vector machines (SVMs) are well-known machine learning algorithms for classification and regression applications. In the healthcare domain, they have been used for a variety of tasks including diagnosis, prognosis, and prediction of disease outcomes. This review is an extensive survey on the current state-of-the-art of SVMs developed and applied in the medical field over the years. Many variants of SVM-based approaches have been developed to enhance their generalisation capabilities. We illustrate the most interesting SVM-based models that have been developed and applied in healthcare to improve performance metrics on benchmark datasets, including hybrid classification methods that combine, for instance, optimization algorithms with SVMs. We even report interesting results found in medical applications related to real-world data. Several issues around SVMs, such as selection of hyperparameters and learning from data of questionable quality, are discussed as well. The several variants developed and introduced over the years could be useful in designing new methods to improve performance in critical fields such as healthcare, where accuracy, specificity, and other metrics are crucial. Finally, current research trends and future directions are underlined.