Rapid antimicrobial susceptibility testing (AST) is urgently needed for informing treatment decisions and preventing the spread of antimicrobial resistance resulting from the misuse and overuse of antibiotics. To date, no phenotypic AST exists that can be performed within a single patient visit (30 min) directly from clinical samples. We show that AST results can be obtained by using digital nucleic acid quantification to measure the phenotypic response of Escherichia coli present within clinical urine samples exposed to an antibiotic for 15 min. We performed this rapid AST using our ultrafast (~7 min) digital real-time loop-mediated isothermal amplification (dLAMP) assay [area under the curve (AUC), 0.96] and compared the results to a commercial (~2 hours) digital polymerase chain reaction assay (AUC, 0.98). The rapid dLAMP assay can be used with SlipChip microfluidic devices to determine the phenotypic antibiotic susceptibility of E. coli directly from clinical urine samples in less than 30 min. With further development for additional pathogens, antibiotics, and sample types, rapid digital AST (dAST) could enable rapid clinical decision-making, improve management of infectious diseases, and facilitate antimicrobial stewardship.

We show here that computer game players can build high-quality crystal structures. Introduction of a new feature into the computer game Foldit allows players to build and real-space refine structures into electron density maps. To assess the usefulness of this feature, we held a crystallographic model-building competition between trained crystallographers, undergraduate students, Foldit players and automatic model-building algorithms. After removal of disordered residues, a team of Foldit players achieved the most accurate structure. Analysing the target protein of the competition, YPL067C, uncovered a new family of histidine triad proteins apparently involved in the prevention of amyloid toxicity. From this study, we conclude that crystallographers can utilize crowdsourcing to interpret electron density information and to produce structure solutions of the highest quality.

The importance of 1,5‐anhydroglucitol (1,5‐AG) as an intermediate biomarker for diabetic pregnancy is multi‐fold: (1) it serves as a reliable indicator of moderate‐level glycemic control, especially during early gestation; (2) it has been associated with increased risk of diabetes, independent of HbA1c and fasting glucose; and (3) it is an independent risk factor for the development of eclampsia during pregnancy. However, the clinical use of this biomarker during pregnancy has been underutilized due to physiological changes in glomerular filtration rate, plasma volume, and other hemodynamic parameters which have been hypothesized to bias gestational serum 1,5‐AG concentrations. Here, we develop an in‐silico model of gestational 1,5‐AG by combining pre‐existing physiological data in the literature with a two‐compartment mathematical model, building off of a previous kinetic model described by Stickle and Turk (1997) Am. J. Physiol., 273, E821. Our model quantitatively characterizes how renal and hemodynamic factors impact measured 1,5‐AG during normal pregnancy and during pregnancy with gestational diabetes and diabetes mellitus. During both normal and diabetic pregnancy, we find that a simple two‐compartment model of 1,5‐AG kinetics, with all parameters but reabsorption fraction adjusted for time in pregnancy, efficiently models 1,5‐AG kinetics throughout the first two trimesters. Allowing reabsorption fraction to decrease after 25 weeks permits parameters closer to expected physiological values during the last trimester. Our quantitative model of 1,5‐AG confirms the involvement of hypothesized renal and hemodynamic mechanisms during pregnancy, clarifying the expected trends in 1,5‐AG to aid clinical interpretation. Further research and data may elucidate biological changes during the third trimester that account for the drop in 1,5‐AG concentrations, and clarify physiological differences between diabetes subtypes during pregnancy.