The human gut microbiome is linked to many states of human health and disease. 1 The metabolic repertoire of the gut microbiome is vast, but the health implications of these bacterial pathways are poorly understood. In this study, we identify a link between members of the genus Veillonella and exercise performance. We observed an increase Veillonella relative abundance in marathon runners post-marathon and isolated a strain of Veillonella atypica from stool samples. Inoculation of this strain into mice significantly increased exhaustive treadmill runtime. Veillonella utilize lactate as their sole carbon source, which prompted us to perform shotgun metagenomic analysis in a cohort of elite athletes, finding that every gene in a major pathway metabolizing lactate to propionate is at higher relative abundance post-exercise. Using 13 C 3 -labeled lactate in mice we demonstrate that serum lactate crosses the epithelial barrier into the lumen of the gut. We also show that intrarectal instillation of propionate is sufficient to reproduce the increased treadmill runtime performance observed with V. atypica gavage. Taken together, these studies reveal that V. atypica improves runtime via its metabolic conversion of exercise-induced lactate into propionate, thereby identifying a natural, microbiome-encoded enzymatic process that enhances athletic performance.

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Background Puerto Ricans living in the mainland US have substantially higher rates of impairment to cognitive performance as compared to non-Hispanic Whites, with air pollutant exposures a potential risk factor. We investigated whether exposures to specific air pollution sources were associated with performance across several cognitive domains in a cohort of Puerto Rican older adults. Objectives To investigate the association between sources of PM2.5 and cognitive performance in each of five cognitive domains. Methods We obtained demographic, health, and cognitive function data for 1500 elderly participants of the Boston Puerto Rican Health Study (BPRHS). Cognitive function was assessed in each of two waves for five domains: verbal memory, recognition, mental processing, and executive and visuospatial function. To these data, we linked concentrations of fine particulate matter (PM2.5) and its components, black carbon (BC), nickel, sulfur, and silicon, as tracers for PM2.5 from traffic, oil combustion, coal combustion, and resuspended dust, respectively. Associations between each PM2.5 component and cognitive domain were examined using linear mixed models. Results One year moving average exposures to BC were significantly associated with decreased verbal memory (−0.38;95% CI: −0.46,−0.30), recognition (−0.35; 95% CI: −0.46,−0.25), mental processing (−1.14; 95% CI: −1.55,−0.74), and executive function (−0.94; 95% CI: −1.31,−0.56). Similar associations were found for nickel. Associations for sulfur, and silicon, and PM2.5 were generally null, although sulfur (−0.51; 95% CI −0.75,−0.28) silicon (−0.25; 95% CI: −0.36,−0.13) and PM2.5 (−0.35; 95% CI: −0.57,−0.12) were associated with decreased recognition. Conclusion Long-term exposures to BC and nickel, tracers of traffic and oil combustion, respectively, were associated with decreased cognitive function across all domains, except visuospatial function.

Artificial Intelligence (AI) has the potential to greatly improve the delivery of healthcare and other services that advance population health and wellbeing. However, the use of AI in healthcare also brings potential risks that may cause unintended harm. To guide future developments in AI, the High-Level Expert Group on AI set up by the European Commission (EC), recently published ethics guidelines for what it terms “trustworthy” AI. These guidelines are aimed at a variety of stakeholders, especially guiding practitioners toward more ethical and more robust applications of AI. In line with efforts of the EC, AI ethics scholarship focuses increasingly on converting abstract principles into actionable recommendations. However, the interpretation, relevance, and implementation of trustworthy AI depend on the domain and the context in which the AI system is used. The main contribution of this paper is to demonstrate how to use the general AI HLEG trustworthy AI guidelines in practice in the healthcare domain. To this end, we present a best practice of assessing the use of machine learning as a supportive tool to recognize cardiac arrest in emergency calls. The AI system under assessment is currently in use in the city of Copenhagen in Denmark. The assessment is accomplished by an independent team composed of philosophers, policy makers, social scientists, technical, legal, and medical experts. By leveraging an interdisciplinary team, we aim to expose the complex trade-offs and the necessity for such thorough human review when tackling socio-technical applications of AI in healthcare. For the assessment, we use a process to assess trustworthy AI, called 1Z-Inspection® to identify specific challenges and potential ethical trade-offs when we consider AI in practice.