Vitamin C Supplementation in Healthy Individuals Leads to Shifts of Bacterial Populations in the Gut - A Pilot Study

Gut microbes are crucial to human health, but microbial composition is often disturbed in a number of human diseases. Accumulating evidence points to nutritional modulation of the gut microbiota as a potentially beneficial therapeutic strategy. Vitamin C (ascorbic acid) may be of particular interest as it has known antioxidant and anti-inflammatory properties. In this study, we investigated whether supplementation with high-dose vitamin C may favourably affect the composition of the gut microbiota. In this pilot study, healthy human participants received 1000 mg vitamin C supplementation daily for two weeks. Gut microbiota composition was analysed before and after intervention by performing faecal 16S rRNA gene sequencing. In total, 14 healthy participants were included. Daily supplementation of high-dose vitamin C led to an increase in the relative abundances of Lachnospiraceae (p < 0.05), whereas decreases were observed for Bacteroidetes (p < 0.01), Enterococci (p < 0.01) and Gemmiger formicilis (p < 0.05). In addition, trends for bacterial shifts were observed for Blautia (increase) and Streptococcus thermophilus (decrease). High-dose vitamin C supplementation for two weeks shows microbiota-modulating effects in healthy individuals, with several beneficial shifts of bacterial populations. This may be relevant as these bacteria have anti-inflammatory properties and strongly associate with gut health

Detection of duodenal villous atrophy on endoscopic images using a deep learning algorithm

Background and aims Celiac disease with its endoscopic manifestation of villous atrophy is underdiagnosed worldwide. The application of artificial intelligence (AI) for the macroscopic detection of villous atrophy at routine esophagogastroduodenoscopy may improve diagnostic performance. Methods A dataset of 858 endoscopic images of 182 patients with villous atrophy and 846 images from 323 patients with normal duodenal mucosa was collected and used to train a ResNet 18 deep learning model to detect villous atrophy. An external data set was used to test the algorithm, in addition to six fellows and four board certified gastroenterologists. Fellows could consult the AI algorithm’s result during the test. From their consultation distribution, a stratification of test images into “easy” and “difficult” was performed and used for classified performance measurement. Results External validation of the AI algorithm yielded values of 90 %, 76 %, and 84 % for sensitivity, specificity, and accuracy, respectively. Fellows scored values of 63 %, 72 % and 67 %, while the corresponding values in experts were 72 %, 69 % and 71 %, respectively. AI consultation significantly improved all trainee performance statistics. While fellows and experts showed significantly lower performance for “difficult” images, the performance of the AI algorithm was stable. Conclusion In this study, an AI algorithm outperformed endoscopy fellows and experts in the detection of villous atrophy on endoscopic still images. AI decision support significantly improved the performance of non-expert endoscopists. The stable performance on “difficult” images suggests a further positive add-on effect in challenging cases

Op zoek naar de goede daltonschool.

Visiteurs hebben het regelmatig over een &lsquo;gevoel&rsquo; als je een daltonschool binnenloopt. Ze zeggen de school &lsquo;ademt dalton&rsquo;. Maar wat is dat &lsquo;gevoel&rsquo;? Om welke kenmerken gaat het dan? En wanneer is een daltonschool nu eigenlijk een goede daltonschool? In ons onderzoek gaan we daltonkwaliteit zichtbaar maken voor docenten, schoolleiders en visiteurs van de Nederlandse Dalton Vereniging (NDV). Om antwoord te krijgen hebben we de hulp van de visiteurs van de NDV ingeschakeld.&nbsp;Daarbij zijn enorm waardevolle gesprekken met visiteurs geweest. Visiteurs zijn het opmerkelijk met elkaar eens. Een vijftal kenmerken komen uit onze gesprekken en worden in deze column toegelicht.&nbsp; In: Nieuwbrief Nederlandse Dalton Vereniging (NDV), maart 201

CCDC 1835847: Experimental Crystal Structure Determination

Related Article: Francesca Milocco, Folkert de Vries, Anna Dall'Anese, Vera Rosar, Ennio Zangrando, Edwin Otten, Barbara Milani|2018|Dalton Trans.|47|14445|doi:10.1039/C8DT03130D,An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

CCDC 1835848: Experimental Crystal Structure Determination

Related Article: Francesca Milocco, Folkert de Vries, Anna Dall'Anese, Vera Rosar, Ennio Zangrando, Edwin Otten, Barbara Milani|2018|Dalton Trans.|47|14445|doi:10.1039/C8DT03130D,An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

CCDC 1835846: Experimental Crystal Structure Determination

Related Article: Francesca Milocco, Folkert de Vries, Anna Dall'Anese, Vera Rosar, Ennio Zangrando, Edwin Otten, Barbara Milani|2018|Dalton Trans.|47|14445|doi:10.1039/C8DT03130D,An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.