The diverse bacterial populations that comprise the commensal microbiome of the human intestine play a central role in health and disease. A method that sustains complex microbial communities in direct contact with living human intestinal cells and their overlying mucus layer in vitro would thus enable investigations of host–microbiome interactions. Here, we show the extended co-culture of living human intestinal epithelium with stable communities of aerobic and anaerobic human gut microbiota, enabled by a microfluidic intestine-on-a-chip that permits the control and real-time assessment of physiologically relevant oxygen gradients. When compared to aerobic co-culture conditions, the establishment of a transluminal hypoxia gradient in the chip increased intestinal barrier function and sustained a physiologically relevant level of microbial diversity, consisting of over 200 unique operational taxonomic units from 11 different genera, and of an abundance of obligate anaerobic bacteria with ratios of Firmicutes and Bacteroidetes similar to those observed in human faeces. The intestine-on-a-chip may serve as a discovery tool for the development of microbiome-related therapeutics, probiotics and nutraceuticals.

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Commercial wearable devices are surfacing as an appealing mechanism to detect COVID-19 and potentially other public health threats, due to their widespread use. To assess the validity of wearable devices as population health screening tools, it is essential to evaluate predictive methodologies based on wearable devices by mimicking their real-world deployment. Several points must be addressed to transition from statistically significant differences between infected and uninfected cohorts to COVID-19 inferences on individuals. We demonstrate the strengths and shortcomings of existing approaches on a cohort of 32,198 individuals who experience influenza like illness (ILI), 204 of which report testing positive for COVID-19. We show that, despite commonly made design mistakes resulting in overestimation of performance, when properly designed wearables can be effectively used as a part of the detection pipeline. For example, knowing the week of year, combined with naive randomised test set generation leads to substantial overestimation of COVID-19 classification performance at 0.73 AUROC. However, an average AUROC of only 0.55 ± 0.02 would be attainable in a simulation of real-world deployment, due to the shifting prevalence of COVID-19 and non-COVID-19 ILI to trigger further testing. In this work we show how to train a machine learning model to differentiate ILI days from healthy days, followed by a survey to differentiate COVID-19 from influenza and unspecified ILI based on symptoms. In a forthcoming week, models can expect a sensitivity of 0.50 (0-0.74, 95% CI), while utilising the wearable device to reduce the burden of surveys by 35%. The corresponding false positive rate is 0.22 (0.02-0.47, 95% CI). In the future, serious consideration must be given to the design, evaluation, and reporting of wearable device interventions if they are to be relied upon as part of frequent COVID-19 or other public health threat testing infrastructures.

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