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Meconium metabolome in human infants is associated with early-life gut microbiota composition
Petersen C, Dai D, Boutin R, et al. A rich meconium metabolome in human infants is associated with early-life gut microbiota composition and reduced allergic sensitization. Cell Medicine 2021; doi: 10.1016/j.xcrm.2021.100260
Atopic disorders, including eczema (atopic dermatitis), food allergy, asthma and allergic rhinitis, are estimated to affect up to 30% of the population, with many children experiencing a lifelong burden. Microbiota maturation and immune development occur in parallel with, and are implicated in, allergic diseases, and research has begun to demonstrate the importance of prenatal influencers on both. Using the Canadian Healthy Infant Longitudinal Development Cohort study, the authors examined microbiota maturation in children who developed atopy immunoglobulin E-mediated allergic sensitisation at 1 year of life compared with healthy controls. To further understand whether the neonatal niche in the guts of atopic infants was associated with the detected changes in their early-life microbiota and susceptibility to atopy the authors performed a global metabolomics analysis of meconium samples in a subset of 100 infants. Analysis revealed that those with atopy by 1 year of age have a less-diverse gut metabolome at birth, and specific metabolic clusters are associated with both protection against atopy and the abundance of key taxa driving microbiota maturation. These metabolic signatures, when coupled with early-life microbiota and clinical factors, increase the ability to accurately predict atopy development in infants.
Early detection of oesophageal adenocarcinoma using deep learning artificial intelligence (AI)
Gehrung M, Crispin-Ortuzar M, Berman A, et al. Triage-driven diagnosis of Barrett’s oesophagus for early detection of oesophageal adenocarcinoma using deep learning. Nat Medicine 2021; doi: 10.1038/s41591-021-01287-9
AI is having an increasingly greater impact on clinical protocols. Pathology-based diagnostics have been identified as areas where deep learning can improve patient care. Here, deep learning networks were used to determine the diagnostic potential of AI compared with pathologists reporting cytosponge samples taken from patients with Barrett’s oesophagus …
Funding The author has not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.
Competing interests None declared.
Provenance and peer review Commissioned; internally peer reviewed.
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