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Biosensor research is a swiftly growing field for developing rapid and precise analytical devices for biomedical, pharmaceutical, and industrial use and beyond. Herein, we propose a phage-based biosensor method to develop a sensitive and specific system for biomedical detection. Our method is based on in vitro selected phages and their interaction with the targeted analytes as well as on optical properties that change according to the concentration of the model analyte. The green fluorescent protein (GFP) was chosen as our model analyte as it has its own well-known optical properties. Brilliant green was used as a reporter component for the sensor. Its presence enables a color intensity (absorbance) change when the analyte is present in the solution. Furthermore, the reporter dye functioned as a quencher for an additional lanthanide label in our assay. It mediated the specific phage-derived interference in the signal measured with the time-resolved luminescence. Most importantly, our results confirmed that the presented bifunctional phage with its liquid crystal properties enabled the measurement of GFP in a concentration-dependent, quantitative manner with a limit of detection of 0.24 µg/mL. In the future, our novel method to develop phage-based biosensors may provide highly sensitive and specific biosensors for biomedical or otherwise-relevant targets.

The Human Exposome Assessment Platform (HEAP) is a research resource for the integrated and efficient management and analysis of human exposome data. The project will provide the complete workflow for obtaining exposome actionable knowledge from population-based cohorts. HEAP is a state-of-the-science service composed of computational resources from partner institutions, accessed through a software framework that provides the world’s fastest Hadoop platform for data warehousing and applied artificial intelligence (AI). The software, will provide a decision support system for researchers and policymakers. All the data managed and processed by HEAP, together with the analysis pipelines, will be available for future research. In addition, the platform enables adding new data and analysis pipelines. HEAP’s final product can be deployed in multiple instances to create a network of shareable and reusable knowledge on the impact of exposures on public health.

Healthcare workers (HCWs) are a risk group for SARS-CoV-2 infection, but which healthcare work that conveys risk and to what extent such risk can be prevented is not clear. Starting on April 24th, 2020, all employees at work (n = 15,300) at the Karolinska University Hospital, Stockholm, Sweden were invited and 92% consented to participate in a SARS-CoV-2 cohort study. Complete SARS-CoV-2 serology was available for n = 12,928 employees and seroprevalences were analyzed by age, sex, profession, patient contact, and hospital department. Relative risks were estimated to examine the association between type of hospital department as a proxy for different working environment exposure and risk for seropositivity, adjusting for age, sex, sampling week, and profession. Wards that were primarily responsible for COVID-19 patients were at increased risk (adjusted OR 1.95 (95% CI 1.65–2.32) with the notable exception of the infectious diseases and intensive care units (adjusted OR 0.86 (95% CI 0.66–1.13)), that were not at increased risk despite being highly exposed. Several units with similar types of work varied greatly in seroprevalences. Among the professions examined, nurse assistants had the highest risk (adjusted OR 1.62 (95% CI 1.38–1.90)). Although healthcare workers, in particular nurse assistants, who attend to COVID-19 patients are a risk group for SARS-CoV-2 infection, several units caring for COVID-19 patients had no excess risk. Large variations in seroprevalences among similar units suggest that healthcare work-related risk of SARS-CoV-2 infection may be preventable.