Possible scenarios for the spread of mpox outside the endemic focus in Africa.

The recent expansion of mpox in Africa is characterized by a dramatic increase in zoonotic transmission (clade Ia) and the emergence of a new clade Ib that is transmitted from human to human by close contact. Clade Ia does not pose a threat in areas without zoonotic reservoirs. But clade Ib may spread widely, as did clade IIb which has spread globally since 2022 among men who have sex with men.

Thresholds for conjugated hyperbilirubinaemia and hepatobiliary scintigraphy in biliary atresia: A 12-year national follow-up.

Aim: To investigate liver biochemistry in infants screened for biliary atresia (BA) at the time of hepatobiliary scintigraphy (HS) and to evaluate the effect of change in threshold for HS.

Methods: Infants born from 2010 to 2021, who underwent HS <6 months postpartum for BA, were included and data sourced from electronic medical records. The change in threshold in 2018 from ≥20 (and/or if conjugated bilirubin exceeds 20% of total bilirubin) to ≥17 μM (regardless of total bilirubin) was evaluated.

Enhancing RNA encapsulation quantification in lipid nanoparticles: Sustainable alternatives to Triton X-100 in the RiboGreen assay.

To quantify concentration and encapsulation efficiency (EE) of mRNA in lipid nanoparticles (LNPs) the RiboGreen assay is extensively used. As part of this assay, a surfactant is used to release mRNA from LNPs for detection with the RiboGreen dye. So far, the surfactant of choice has been Triton X-100, which is harmful to human health and the environment.

Predicting Patient No-Shows: Situated Machine Learning with Imperfect Data.

Patients who do not show up for scheduled appointments are a considerable cost and concern in healthcare. In this study, we predict patient no-shows for outpatient surgery at the endoscopy ward of a hospital in Denmark. The predictions are made by training machine leaning (ML) models on available data, which have been recorded for purposes other than ML, and by doing situated work in the hospital setting to understand local data practices and fine-tune the models.