Le projet personnel des internes de Médecine Générale de Marseille après la fin du 3ème cycle

Introduction : la démographie médicale et l’accessibilité aux soins font parties des grandes lignes directrices des gouvernements depuis quelques années. L’accent est mis sur le cursus universitaire qui est, de par sa sélection très stricte, souvent pointé du doigt en regard d’une demande médicale croissante. Le médecin généraliste se voit attribué un rôle central dans la coordination des soins de la population. Rôle qu’il a de plus en plus de mal à jouer du fait de l’augmentation des zones de déserts médicaux. La profession médicale est en proie à une transformation : féminisation des effectifs, modifications de la manière d’exercer, contrôle du temps de travail et importance croissante de la sphère privée/personnelle. Méthode : étude observationnelle descriptive transversale menée à travers trois promotions successives d’anciens internes de médecine générale de Marseille sur une période de juillet à septembre 2020. Le questionnaire diffusé sur les réseaux sociaux portait sur différentes caractéristiques socio démographiques : situations personnelles, zone d’exercice, formations complémentaires, soutenance de thèse, évolution de leurs activités et de leurs projets professionnels. Résultats et discussion : 204 internes ont répondu au questionnaire (31%) dont 63% de femmes. 82% exercent en région PACA/Corse (hors Alpes Maritimes) ; parmi eux 53% étaient d’anciens externes de la faculté d’Aix-Marseille. 57% des participants, dont 58% des femmes, ont fait au moins une formation complémentaire. Leur répartition dans la région suit celle de la population. A un an post internat, ils sont 47% à faire des remplacements, 21% à avoir une activité hospitalière, 16% à avoir une activité mixte, 10% à être installé, 4% à avoir un salariat hors hospitalier et 2% à n’avoir aucune activité. Pour l’activité libérale : 65% exercent au sein d’un regroupement de médecin, 25% dans un centre de santé pluri disciplinaire et 10% seul dans un cabinet. Pour l’activité hospitalière : 50% occupent un poste dans un service d’urgence, 35% dans un service hospitalier et 15% dans un SSR. Concernant leurs carrières : 54% des jeunes médecins jugent avoir manqué d’information durant leur internat mais cela n’impacte pas leur satisfaction personnelle

Minimal datasets for testing DIMet

<p>Two datasets to test the DIMet tool, which is available <a href="https://github.com/cbib/DIMet/">here</a>, the tool documentation is accessible in the <a href="https://github.com/cbib/DIMet/wiki/">DIMet wiki page</a>, and also in Galaxy.</p> <p>Depending on the user case, download one .zip file:</p> <ul> <li>Users of the <strong>Galaxy </strong>version of DIMet: download the file 'minimal_examples_DIMet_Galaxy.zip', decompress it and use the files as indicated in the documentation accompanying each 'dimet_' module in the Galaxy site.</li> <li>Users of the <strong>command line</strong> version of DIMet: download the file 'minimal_examples_DIMet_command_line.zip', decompress it and follow the instructions in the <a href="https://github.com/cbib/DIMet/wiki/">DIMet wiki page</a>.</li> </ul&gt

Datasets of the DIMet manuscript

<p>Datasets for reproducing the results of the manuscript "<em>DIMet : An open-source tool for Differential analysis of targeted Isotope-labeled Metabolomics data". </em>DIMet tool is available <a href="https://github.com/cbib/DIMet/">here</a>, and the tool documentation is accessible in the <a href="https://github.com/cbib/DIMet/wiki/">DIMet wiki page </a>and in its Galaxy site.</p> <p><strong>  </strong>Users of the <strong>Galaxy version of DIMet:</strong></p> <ul> <li>download and decompress (unzip) the .zip file.</li> <li>within the 'datasets_manuscript_DIMet/' there is a sub-folder <strong><em>data/</em></strong>, preserve.</li> <li>within 'datasets_manuscript_DIMet/' there is a sub-folder <em>config/,</em> the user can delete it as it is not used in the Galaxy version.</li> <li>use the .csv files that are provided in <strong><em>data/</em></strong> . The specific .csv files to be given as input are explained in each 'dimet_' module in Galaxy.</li> </ul> <p>  Users of the <strong>command-line</strong> version of <strong>DIMet</strong>:</p> <ul> <li>download, decompress it and follow the instructions of the documentation in the <a href="https://github.com/cbib/DIMet/wiki/">DIMet wiki page</a>.</li> </ul> <p> </p&gt

Datasets of the DIMet manuscript

<p>Datasets for reproducing the results of the manuscript "<em>DIMet : An open-source tool for Differential analysis of targeted Isotope-labeled Metabolomics data". </em>DIMet tool is available <a href="https://github.com/cbib/DIMet/">here</a>, and the tool documentation is accessible in the <a href="https://github.com/cbib/DIMet/wiki/">DIMet wiki page </a>and in its Galaxy site.</p> <p>If running in command-line version, download, decompress it and follow the instructions of the documentation in the <a href="https://github.com/cbib/DIMet/wiki/">DIMet wiki page</a>.</p> <table> <tbody> <tr> <td> <p><strong>  </strong>Users of the <strong>Galaxy version of DIMet:</strong></p> <ul> <li>download and decompress (unzip) the .zip file.</li> <li>within the 'datasets_manuscript_DIMet/' there is a sub-folder <strong><em>data/</em></strong>, preserve.</li> <li>within 'datasets_manuscript_DIMet/' there is a sub-folder <em>config/,</em> the user can delete it as it is not used in the Galaxy version.</li> <li>use the .csv files that are provided in <strong><em>data/</em></strong> . The specific .csv files to be given as input are explained in each 'dimet_' module in Galaxy.</li> </ul> </td> </tr> </tbody> </table> <p> </p&gt

Protein supplementation during an energy-restricted diet induces visceral fat loss and gut microbiota amino acid metabolism activation: a randomized trial

International audienceInteractions between diet and gut microbiota are critical regulators of energy metabolism. The effects of fibre intake have been deeply studied but little is known about the impact of proteins. Here, we investigated the effects of high protein supplementation (Investigational Product, IP) in a double blind, randomised placebo-controled intervention study (NCT01755104) where 107 participants received the IP or an isocaloric normoproteic comparator (CP) alongside a mild caloric restriction. Gut microbiota profiles were explored in a patient subset (n = 53) using shotgun metagenomic sequencing. Visceral fat decreased in both groups (IP group: − 20.8 ± 23.2 cm2; CP group: − 14.5 ± 24.3 cm2) with a greater reduction (p < 0.05) with the IP supplementation in the Per Protocol population. Microbial diversity increased in individuals with a baseline low gene count (p < 0.05). The decrease in weight, fat mass and visceral fat mass significantly correlated with the increase in microbial diversity (p < 0.05). Protein supplementation had little effects on bacteria composition but major differences were seen at functional level. Protein supplementation stimulated bacterial amino acid metabolism (90% amino-acid synthesis functions enriched with IP versus 13% in CP group (p < 0.01)). Protein supplementation alongside a mild energy restriction induces visceral fat mass loss and an activation of gut microbiota amino-acid metabolism

Transcriptomic Landscape of Prurigo Nodularis Lesional Skin CD3+ T Cells Using Single-Cell RNA Sequencing

International audienceNo abstract availabl

Shotgun metagenomics and systemic targeted metabolomics highlight indole-3-propionic acid as a protective gut microbial metabolite against influenza infection

International audienceThe gut-to-lung axis is critical during respiratory infections, including influenza A virus (IAV) infection. In the present study, we used high-resolution shotgun metagenomics and targeted metabolomic analysis to characterize influenza-associated changes in the composition and metabolism of the mouse gut microbiota. We observed several taxonomic-level changes on day (D)7 post-infection, including a marked reduction in the abundance of members of the Lactobacillaceae and Bifidobacteriaceae families, and an increase in the abundance of Akkermansia muciniphila. On D14, perturbation persisted in some species. Functional scale analysis of metagenomic data revealed transient changes in several metabolic pathways, particularly those leading to the production of short-chain fatty acids (SCFAs), polyamines, and tryptophan metabolites. Quantitative targeted metabolomics analysis of the serum revealed changes in specific classes of gut microbiota metabolites, including SCFAs, trimethylamine, polyamines, and indole-containing tryptophan metabolites. A marked decrease in indole-3-propionic acid (IPA) blood level was observed on D7. Changes in microbiota-associated metabolites correlated with changes in taxon abundance and disease marker levels. In particular, IPA was positively correlated with some Lactobacillaceae and Bifidobacteriaceae species (Limosilactobacillus reuteri, Lactobacillus animalis) and negatively correlated with Bacteroidales bacterium M7, viral load, and inflammation markers. IPA supplementation in diseased animals reduced viral load and lowered local (lung) and systemic inflammation. Treatment of mice with antibiotics targeting IPA-producing bacteria before infection enhanced viral load and lung inflammation, an effect inhibited by IPA supplementation. The results of this integrated metagenomic-metabolomic analysis highlighted IPA as an important contributor to influenza outcomes and a potential biomarker of disease severity