Perspectives for self-driving labs in synthetic biology.

Self-driving labs (SDLs) combine fully automated experiments with artificial intelligence (AI) that decides the next set of experiments. Taken to their ultimate expression, SDLs could usher a new paradigm of scientific research, where the world is probed, interpreted, and explained by machines for human benefit. While there are functioning SDLs in the fields of chemistry and materials science, we contend that synthetic biology provides a unique opportunity since the genome provides a single target for affecting the incredibly wide repertoire of biological cell behavior. However, the level of investment required for the creation of biological SDLs is only warranted if directed toward solving difficult and enabling biological questions. Here, we discuss challenges and opportunities in creating SDLs for synthetic biology.

Risk Factors for Chronic Cough in Young Children: A Cohort Study.

Background and Objective: Data on the predictors of chronic cough development in young children are scarce. Our primary objective was to examine the factors associated with young children developing a chronic cough, with a focus on childcare attendance. Methods: A secondary analysis of data collected in a prospective cohort study of children presenting to three emergency departments and three primary healthcare centers in southeast Queensland, Australia. Eligible children where those aged <6-years presenting with cough and without known underlying chronic lung disease other than asthma. Children were followed for 4 weeks to ascertain cough duration. The primary outcome was persistent cough at day-28. Logistic regression models were undertaken to identify independent predictors of chronic cough including sensitivity analyses that accounted for children with unknown cough status at day-28. Results: In 362 children, 95 (26.2%) were classified as having chronic cough. In models that included only children for whom cough status was known at day-28, symptom duration at enrolment, age <12 months [adjusted odds ratio (aOR) 4.5, 95% confidence interval (CI) 1.1, 18.7], gestational age (aOR 3.2, 95%CI 1.4, 7.9), underlying medical conditions (aOR 2.6, 95% CI 1.3, 5.5), a history of wheeze (aOR 2.6, 95% CI 1.4, 4.8) and childcare attendance (aOR 2.3, 95% CI 1.2, 4.4) were independent predictors of chronic cough. Amongst childcare attendees only, 64 (29.8%) had chronic cough at day-28. The strongest predictor of chronic cough amongst childcare attendees was continued attendance at childcare during their illness (aOR = 12.9, 95% CI 3.9, 43.3). Conclusion: Gestational age, underlying medical conditions, prior wheeze and childcare attendance are risk factors for chronic cough in young children. Parents/careers need to be aware of the risks associated with their child continuing to attend childcare whilst unwell and childcare centers should reinforce prevention measures in their facilities.