TY  - JOUR
A1  - Kierspel, Thomas
A1  - Kadek, Alan
A1  - Barran, Perdita
A1  - Bellina, Bruno
A1  - Bijedic, Adi
A1  - Brodmerkel, Maxim N.
A1  - Commandeur, Jan
A1  - Caleman, Carl
A1  - Damjanovic, Tomislav
A1  - Dawod, Ibrahim
A1  - De Santis, Emiliano
A1  - Lekkas, Alexandros
A1  - Lorenzen, Kristina
A1  - López Morillo, Luis
A1  - Mandl, Thomas
A1  - Marklund, Erik G.
A1  - Papanastasiou, Dimitris
A1  - Ramakers, Lennart A. I.
A1  - Schweikhard, Lutz
A1  - Simke, Florian
A1  - Sinelnikova, Anna
A1  - Smyrnakis, Athanasios
A1  - Timneanu, Nicusor
A1  - Uetrecht, Charlotte
T1  - Coherent diffractive imaging of proteins and viral capsids: simulating MS SPIDOC
JF  - Analytical and Bioanalytical Chemistry
N2  - 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.
KW  - SPI
KW  - X-ray
KW  - Native MS
KW  - Protein complex structure
KW  - Simulation
Y1  - 
U6  - http://dx.doi.org/https://doi.org/10.1007/s00216-023-04658-y
VL  - 2023
IS  - 415
SP  - 4209
EP  - 4220
ER  - 
TY  - JOUR
A1  - Munsch, Nicolas
A1  - Gruarin, Stefanie
A1  - Nateqi, Jama
A1  - Lutz, Thomas
A1  - Binder, Michael
A1  - Aberle, Judith H.
A1  - Martin, Alistair
A1  - Knapp, Bernhard
T1  - Symptoms associated with a COVID-19 infection among a non-hospitalized cohort in Vienna
JF  - Wiener Klinische Wochenschrift / The Central European Journal of Medicine
N2  - 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.
KW  - COVID-19
KW  - Chatbot
KW  - Machine learning
KW  - Self-reported
KW  - Symptom assessment
Y1  - 
VL  - 2022
IS  - 134 (9-10)
SP  - 344
EP  - 350
PB  - Springer
ER  -