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MS SPIDOC is a novel sample delivery system designed for single (isolated) particle imaging at X-ray Free-Electron Lasers
that is adaptable towards most large-scale facility beamlines. Biological samples can range from small proteins to MDa
particles. Following nano-electrospray ionization, ionic samples can be m/z-filtered and structurally separated before being
oriented at the interaction zone. Here, we present the simulation package developed alongside this prototype. The first part
describes how the front-to-end ion trajectory simulations have been conducted. Highlighted is a quadrant lens; a simple but
efficient device that steers the ion beam within the vicinity of the strong DC orientation field in the interaction zone to ensure
spatial overlap with the X-rays. The second part focuses on protein orientation and discusses its potential with respect to
diffractive imaging methods. Last, coherent diffractive imaging of prototypical T = 1 and T = 3 norovirus capsids is shown.
We use realistic experimental parameters from the SPB/SFX instrument at the European XFEL to demonstrate that low-
resolution diffractive imaging data (q < 0.3 nm −1 ) can be collected with only a few X-ray pulses. Such low-resolution data
are sufficient to distinguish between both symmetries of the capsids, allowing to probe low abundant species in a beam if
MS SPIDOC is used as sample delivery.
Background: Most clinical studies report the symptoms experienced by those infected with coronavirus disease 2019 (COVID-19) via patients already hospitalized. Here we analyzed the symptoms experienced outside of a hospital setting.
Methods: The Vienna Social Fund (FSW; Vienna, Austria), the Public Health Services of the City of Vienna (MA15) and the private company Symptoma collaborated to implement Vienna's official online COVID-19 symptom checker. Users answered 12 yes/no questions about symptoms to assess their risk for COVID-19. They could also specify their age and sex, and whether they had contact with someone who tested positive for COVID-19. Depending on the assessed risk of COVID-19 positivity, a SARS-CoV‑2 nucleic acid amplification test (NAAT) was performed. In this publication, we analyzed which factors (symptoms, sex or age) are associated with COVID-19 positivity. We also trained a classifier to correctly predict COVID-19 positivity from the collected data.
Results: Between 2 November 2020 and 18 November 2021, 9133 people experiencing COVID-19-like symptoms were assessed as high risk by the chatbot and were subsequently tested by a NAAT. Symptoms significantly associated with a positive COVID-19 test were malaise, fatigue, headache, cough, fever, dysgeusia and hyposmia. Our classifier could successfully predict COVID-19 positivity with an area under the curve (AUC) of 0.74.
Conclusion: This study provides reliable COVID-19 symptom statistics based on the general population verified by NAATs.
Keywords: Chatbot; Machine learning; Self-reported; Symptom assessment; Symptom checker.