Modulation of Designed Gut Bacterial Communities by Prebiotics and the Impact of Their Metabolites on Intestinal Cells.

The impact of prebiotics on human health is associated with their capacity to modulate microbiota, improving beneficial microbiota-host interactions. Herein, the prebiotic potential of microbial-fructo-oligosaccharides (microbial-FOSs) produced by a co-culture of plus was evaluated on seven- and nine-strain bacterial consortia (7SC and 9SC, respectively), designed to represent the human gut microbiota. The 7SC was composed of , , , , , , and .

Modulation of the Gut Microbiota by Tomato Flours Obtained after Conventional and Ohmic Heating Extraction and Its Prebiotic Properties.

Several studies have supported the positive functional health effects of both prebiotics and probiotics on gut microbiota. Among these, the selective growth of beneficial bacteria due to the use of prebiotics and bioactive compounds as an energy and carbon source is critical to promote the development of healthy microbiota within the human gut. The present work aimed to assess the fermentability of tomato flour obtained after ohmic (SFOH) and conventional (SFCONV) extraction of phenolic compounds and carotenoids as well as their potential impact upon specific microbiota groups.

Evaluation of Microbial-Fructo-Oligosaccharides Metabolism by Human Gut Microbiota Fermentation as Compared to Commercial Inulin-Derived Oligosaccharides.

The prebiotic potential of fructo-oligosaccharides (microbial-FOS) produced by a newly isolated , and purified by YIL162 W, was evaluated. Their chemical structure and functionality were compared to a non-microbial commercial FOS sample. Prebiotics were fermented in vitro by fecal microbiota of five healthy volunteers.

Segmentation of small bowel tumor tissue in capsule endoscopy images by using the MAP algorithm.

State of the art algorithms for diagnosis of the small bowel by using capsule endoscopic images usually rely on the processing of the whole frame, hence no segmentation is usually required. However, some specific applications such as three-dimensional reconstruction of the digestive wall, detection of small substructures such as polyps and ulcers or training of young medical staff require robust segmentation. Current state of the art algorithms for robust segmentation are mainly based on Markov Random Fields (MRF) requiring prohibitive computational resources not compatible with applications that generate a great amount of data as is the case of capsule endoscopy.

Automatic small bowel tumor diagnosis by using multi-scale wavelet-based analysis in wireless capsule endoscopy images.

Background: Wireless capsule endoscopy has been introduced as an innovative, non-invasive diagnostic technique for evaluation of the gastrointestinal tract, reaching places where conventional endoscopy is unable to. However, the output of this technique is an 8 hours video, whose analysis by the expert physician is very time consuming. Thus, a computer assisted diagnosis tool to help the physicians to evaluate CE exams faster and more accurately is an important technical challenge and an excellent economical opportunity.