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Purpose: While more advanced COVID-19 necessitates medical interventions and hospitalization, patients with mild COVID-19 do not require this. Identifying patients at risk of progressing to advanced COVID-19 might guide treatment decisions, particularly for better prioritizing patients in need for hospitalization.
Methods: We developed a machine learning-based predictor for deriving a clinical score identifying patients with asymptomatic/mild COVID-19 at risk of progressing to advanced COVID-19. Clinical data from SARS-CoV-2 positive patients from the multicenter Lean European Open Survey on SARS-CoV-2 Infected Patients (LEOSS) were used for discovery (2020-03-16 to 2020-07-14) and validation (data from 2020-07-15 to 2021-02-16).
Results: The LEOSS dataset contains 473 baseline patient parameters measured at the first patient contact. After training the predictor model on a training dataset comprising 1233 patients, 20 of the 473 parameters were selected for the predictor model. From the predictor model, we delineated a composite predictive score (SACOV-19, Score for the prediction of an Advanced stage of COVID-19) with eleven variables. In the validation cohort (n = 2264 patients), we observed good prediction performance with an area under the curve (AUC) of 0.73 ± 0.01. Besides temperature, age, body mass index and smoking habit, variables indicating pulmonary involvement (respiration rate, oxygen saturation, dyspnea), inflammation (CRP, LDH, lymphocyte counts), and acute kidney injury at diagnosis were identified. For better interpretability, the predictor was translated into a web interface.
Conclusion: We present a machine learning-based predictor model and a clinical score for identifying patients at risk of developing advanced COVID-19.
GABARAP belongs to an evolutionary highly conserved gene family that has a fundamental role in autophagy. There is ample evidence for a crosstalk between autophagy and apoptosis as well as the immune response. However, the molecular details for these interactions are not fully characterized. Here, we report that the ablation of murine GABARAP, a member of the Atg8/LC3 family that is central to autophagosome formation, suppresses the incidence of tumor formation mediated by the carcinogen DMBA and results in an enhancement of the immune response through increased secretion of IL-1β, IL-6, IL-2 and IFN-γ from stimulated macrophages and lymphocytes. In contrast, TGF-β1 was significantly reduced in the serum of these knockout mice. Further, DMBA treatment of these GABARAP knockout mice reduced the cellularity of the spleen and the growth of mammary glands through the induction of apoptosis. Gene expression profiling of mammary glands revealed significantly elevated levels of Xaf1, an apoptotic inducer and tumor-suppressor gene, in knockout mice. Furthermore, DMBA treatment triggered the upregulation of pro-apoptotic (Bid, Apaf1, Bax), cell death (Tnfrsf10b, Ripk1) and cell cycle inhibitor (Cdkn1a, Cdkn2c) genes in the mammary glands. Finally, tumor growth of B16 melanoma cells after subcutaneous inoculation was inhibited in GABARAP-deficient mice. Together, these data provide strong evidence for the involvement of GABARAP in tumorigenesis in vivo by delaying cell death and its associated immune-related response.
Introduction: This study presents our online-teaching material within the k-MED project (Knowledge in Medical Education) at the university of Marburg. It is currently organized in five e-learning modules: cytogenetics, chromosomal aberrations, formal genetics, fundamentals of molecular diagnostics, and congenital abnormalities and syndromes. These are basic courses intended to do the educational groundwork, which will enable academic teachers to concentrate on more sophisticated topics during their lectures. Methods: The e-learning modules have been offered to a large group of about 3300 students during four years at the Faculty of Medicine in Marburg. The group consists of science students (human biology) and medical students in the preclinical or the clinical period, respectively. Participants were surveyed on acceptance by evaluating user-tracking data and questionnaires. Results and Conclusion: Analysis of the evaluation data proofs the broad acceptance of the e-learning modules during eight semesters. The courses are in stable or even increasing use from winter term 2005/06 until spring term 2009. Conclusion: Our e-learning-model is broadly accepted among students with different levels of knowledge at the Faculty of Medicine in Marburg. If the e-learning courses are maintained thoroughly, minor adaptations can increase acceptance and usage even furthermore. Their use should be extended to the medical education of technical assistances and nurses, who work in the field of human genetics. Keywords: Human genetics, e-Learning, evaluation, multimedia
Einleitung: Die vorliegende Studie beschreibt unser Online-Lehrmaterial Humangenetik im Zusammenhang mit dem k-MED-Projekt (Knowledge in Medical Education) an der Philipps-Universität Marburg. Es besteht aus fünf E-Learning-Modulen: Zytogenetik, Chromosomenstörungen, Formalgenetik, Grundlagen der molekularen Diagnostik sowie Kongenitale Abnormitäten und Fehlbildungssyndrome. Diese E-Module sollen ein einheitliches Wissensniveau der Studierenden gewährleisten und die Dozenten in der Präsenzlehre entlasten. Methoden: Die fünf E-Learning-Module Humangenetik wurden auf freiwilliger Basis einer großen Personengruppe von ca. 3300 Studierenden am Fachbereich Humanmedizin der Universität Marburg über eine Dauer von vier Jahren angeboten. Die Teilnehmer bestanden aus Naturwissenschaftlern (Humanbiologie) im 5. Fachsemester und Studierenden der Humanmedizin, die sich entweder in der Vorklinik (1. Semester) oder im klinischen Studienabschnitt (7./8. Semester) befanden. Von diesen wurden Daten zur Akzeptanz in Form von Usertrackingdaten und klausur-begleitenden Fragebögen erhoben. Ergebnisse und Schlussfolgerung: Die Evaluation zeigte eine breite Akzeptanz unserer Lehrmodule über einen Zeitraum von acht Semestern. Obwohl das Angebot freiwillig ist, werden die Online-Kurse Humangenetik konstant oder sogar in zunehmendem Maße zwischen Wintersemester 2005/06 und Sommersemester 2009 genutzt. Fazit: Unser E-Learning-Modell Humangenetik wird von Studierenden aus unterschiedlichen Semestern und Studiengängen am Fachbereich Humanmedizin gut angenommen und genutzt. Bei sorgfältiger Pflege der Online-Kurse steigern moderate Anpassungen sowohl Akzeptanz als auch Benutzungshäufigkeit in signifikanter Weise. Die Anwendung der E-Learning Module erscheint uns auch in der Ausbildung von MTAs oder Pflegekräften sinnvoll, um ein ausreichendes Grundwissen in Humangenetik zu gewährleisten. Schlüsselwörter: Humangenetik, Evaluation, Multimedia, E-Learning