Classification of Quantitative Light-Induced Fluorescence Images Using Convolutional Neural Network

Images are an important data source for diagnosis and treatment of oral diseases. The manual classification of images may lead to misdiagnosis or mistreatment due to subjective errors. In this paper an image classification model based on Convolutional Neural Network is applied to Quantitative Light-induced Fluorescence images. The deep neural network outperforms other state of the art shallow classification models in predicting labels derived from three different dental plaque assessment scores. The model directly benefits from multi-channel representation of the images resulting in improved performance when, besides the Red colour channel, additional Green and Blue colour channels are used.Comment: Full version of ICANN 2017 submissio

A salivary metabolite signature that reflects gingival host-microbe interactions: instability predicts gingivitis susceptibility

Several proteins and peptides in saliva were shown to stimulate gingival wound repair, but the role of salivary metabolites in this process remains unexplored. In vitro gingival re-epithelialization kinetics were determined using unstimulated saliva samples from healthy individuals collected during an experimental gingivitis study. Elastic net regression with stability selection identified a specific metabolite signature in a training dataset that was associated with the observed re-epithelialization kinetics and enabled its prediction for all saliva samples obtained in the clinical study. This signature encompassed ten metabolites, including plasmalogens, diacylglycerol and amino acid derivatives, which reflect enhanced host-microbe interactions. This association is in agreement with the positive correlation of the metabolite signature with the individual’s gingival bleeding index. Remarkably, intra-individual signature-variation over time was associated with elevated risk for gingivitis development. Unravelling how these metabolites stimulate wound repair could provide novel avenues towards therapeutic approaches in patients with impaired wound healing capacity.</p

Infusion of donor feces affects the gut–brain axis in humans with metabolic syndrome

Objective Increasing evidence indicates that intestinal microbiota play a role in diverse metabolic processes via intestinal butyrate production. Human bariatric surgery data suggest that the gut-brain axis is also involved in this process, but the underlying mechanisms remain unknown. Methods We compared the effect of fecal microbiota transfer (FMT) from post-Roux-en-Y gastric bypass (RYGB) donors vs oral butyrate supplementation on (123I-FP-CIT-determined) brain dopamine transporter (DAT) and serotonin transporter (SERT) binding as well as stable isotope-determined insulin sensitivity at baseline and after 4 weeks in 24 male and female treatment-naïve metabolic syndrome subjects. Plasma metabolites and fecal microbiota were also determined at these time points. Results We observed an increase in brain DAT after donor FMT compared to oral butyrate that reduced this binding. However, no effect on body weight and insulin sensitivity was demonstrated after post-RYGB donor feces transfer in humans with metabolic syndrome. Increases in fecal levels of Bacteroides uniformis were significantly associated with an increase in DAT, whereas increases in Prevotella spp. showed an inverse association. Changes in the plasma metabolites glycine, betaine, methionine, and lysine (associated with the S-adenosylmethionine cycle) were also associated with altered striatal DAT expression. Conclusions Although more and larger studies are needed, our data suggest a potential gut microbiota-driven modulation of brain dopamine and serotonin transporters in human subjects with obese metabolic syndrome. These data also suggest the presence of a gut-brain axis in humans that can be modulated

Treatment with Anaerobutyricum soehngenii: a pilot study of safety and dose–response effects on glucose metabolism in human subjects with metabolic syndrome

Dysbiosis of the intestinal microbiota has been implicated in insulin resistance, although evidence regarding causality in humans is scarce. We performed a phase I/II dose-finding and safety study on the effect of oral intake of the anaerobic butyrogenic strain Anaerobutyricum soehngenii on glucose metabolism in 24 subjects with metabolic syndrome. We found that treatment with A. soehngenii was safe and observed a significant correlation between the measured fecal abundance of administered A. soehngenii and improvement in peripheral insulin sensitivity after 4 weeks of treatment. This was accompanied by an altered microbiota composition and a change in bile acid metabolism. Finally, we show that metabolic response upon administration of A. soehngenii (defined as improved insulin sensitivity 4 weeks after A. soehngenii intake) is dependent on microbiota composition at baseline. These data in humans are promising, but additional studies are needed to reproduce our findings and to investigate long-term effects, as well as other modes of delivery.Peer reviewe

Spectral Clustering co-occurrence plot.

<p>Participants are ordered along both X- and Y-axis according to the co-occurrence score (i.e. the more similar the peptide profiles of any two participants, the higher their tendency to cluster together and the closer they are placed on the axis). Co-occurrence score values range from 0 (for participants who never cluster together) to 1.0 (for participants who always cluster together). The horizontal bar delimits the four clusters.</p

Peaks identified by Unsupervised Feature Selection to be determining the subgroups found by Spectral Clustering. Peaks are listed in decreasing order of their effect on the clustering.

<p>Peaks identified by Unsupervised Feature Selection to be determining the subgroups found by Spectral Clustering. Peaks are listed in decreasing order of their effect on the clustering.</p