Project description:We explore whether a low-energy diet intervention for Metabolic dysfunction-associated steatohepatitis (MASH) improves liver disease by means of modulating the gut microbiome. 16 individuals were given a low-energy diet (880 kcal, consisting of bars, soups, and shakes) for 12 weeks, followed by a stepped re-introduction to whole for an additional 12 weeks. Stool samples were obtained at 0, 12, and 24 weeks for microbiome analysis. Fecal microbiome were measured using 16S rRNA gene sequencing. Positive control (Zymo DNA standard D6305) and negative control (PBS extraction) were included in the sequencing. We found that low-energy diet improved MASH disease without lasting alterations to the gut microbiome.
Project description:We obtained peripheral blood samples for women from Utah (USA) and Ontario (Canada) who had a family history of breast cancer (or did not), who carried a BRCA1/2 mutation (or did not), and who had developed breast cancer (or had not). We classified the women into two groups: [1] those who had a family history of breast cancer (irrespective of BRCA1/2 mutation status) and had developed an early-onset breast tumor and [2] those who had a family history of breast cancer but had not developed a breast tumor or who did not have a family history of breast cancer (some of whom had developed sporadic breast cancer and and others who had not). We then used machine-learning methods to assess how well we could classify these women into either group. The Utah samples served as a training set, and the Ontario samples served as an independent validation set from a geographically distinct population.
Project description:We obtained peripheral blood samples for women from Utah (USA) and Ontario (Canada) who had a family history of breast cancer (or did not), who carried a BRCA1/2 mutation (or did not), and who had developed breast cancer (or had not). We classified the women into two groups: [1] those who had a family history of breast cancer (irrespective of BRCA1/2 mutation status) and had developed an early-onset breast tumor and [2] those who had a family history of breast cancer but had not developed a breast tumor or who did not have a family history of breast cancer (some of whom had developed sporadic breast cancer and and others who had not). We then used machine-learning methods to assess how well we could classify these women into either group. The Utah samples served as a training set, and the Ontario samples served as an independent validation set from a geographically distinct population.
Project description:Microbiome ecoregion model is a Named Entity Recognition (NER) model that identifies and annotates the ecoregion of microbiome samples in texts. This is the final model version used to annotate metagenomics publications in Europe PMC and enrich metagenomics studies in MGnify with ecoregion metadata from literature. For more information, please refer to the following blogs: http://blog.europepmc.org/2020/11/europe-pmc-publications-metagenomics-annotations.html https://www.ebi.ac.uk/about/news/service-news/enriched-metadata-fields-mgnify-based-text-mining-associated-publications
Project description:Microbiome host model is a Named Entity Recognition (NER) model that identifies and annotates the host of microbiome samples in texts. This is the final model version used to annotate metagenomics publications in Europe PMC and enrich metagenomics studies in MGnify with host metadata from literature. For more information, please refer to the following blogs: http://blog.europepmc.org/2020/11/europe-pmc-publications-metagenomics-annotations.html https://www.ebi.ac.uk/about/news/service-news/enriched-metadata-fields-mgnify-based-text-mining-associated-publications
Project description:Microbiome site model is a Named Entity Recognition (NER) model that identifies and annotates the site of microbiome samples in texts. This is the final model version used to annotate metagenomics publications in Europe PMC and enrich metagenomics studies in MGnify with site metadata from literature. For more information, please refer to the following blogs: http://blog.europepmc.org/2020/11/europe-pmc-publications-metagenomics-annotations.html https://www.ebi.ac.uk/about/news/service-news/enriched-metadata-fields-mgnify-based-text-mining-associated-publications
Project description:Microbiome sample-material model is a Named Entity Recognition (NER) model that identifies and annotates the material of microbiome samples in texts. This is the final model version used to annotate metagenomics publications in Europe PMC and enrich metagenomics studies in MGnify with sample-material metadata from literature.
For more information, please refer to the following blogs:
http://blog.europepmc.org/2020/11/europe-pmc-publications-metagenomics-annotations.html
https://www.ebi.ac.uk/about/news/service-news/enriched-metadata-fields-mgnify-based-text-mining-associated-publications
Project description:Microbiome collection date model is a Named Entity Recognition (NER) model that identifies and annotates the collection date of microbiome samples in texts. This is the final model version used to annotate metagenomics publications in Europe PMC and enrich metagenomics studies in MGnify with collection date metadata from literature. For more information, please refer to the following blogs: http://blog.europepmc.org/2020/11/europe-pmc-publications-metagenomics-annotations.html https://www.ebi.ac.uk/about/news/service-news/enriched-metadata-fields-mgnify-based-text-mining-associated-publications