Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 11 de 11
Circulation ; 144(SUPPL 1), 2021.
Article in English | EMBASE | ID: covidwho-1632416


Cardiac microthrombi are postulated to underlie cardiac injury in critical COVID-19. To determine pathogenic mechanism(s) of cardiac injury in fatal COVID-19, we conducted a single-center prospective cohort study of 69 consecutive COVID-19 decedents. Microthrombi was the most commonly detected acute cardiac histopathologic feature (n=48, 70%). We tested associations of cardiac microthrombi with biomarkers of inflammation, cardiac injury, and fibrinolysis and with inhospital antiplatelet therapy, therapeutic anticoagulation, and corticosteroid treatment, while adjusting for multiple clinical factors, including COVID-19 therapies. Higher peak ESR and CRP during hospitalization were independently associated with higher odds of microthrombi (ESR, Pnonlinearity 0.015, Passociation=0.008;CRP per 20mg/L increase, OR 1.17, 95%CI 1.00-1.36). Using single nuclei RNA-sequence analysis, we discovered an enrichment of prothrombotic, anti-fibrinolytic, and extracellular matrix signaling amongst cardiac fibroblasts in microthrombi-positive COVID-19 hearts, compared with microthrombi-negative COVID-19 hearts and non-COVID-19 donor hearts. Our cumulative findings identify these specific transcriptomic changes in cardiac fibroblasts as salient features of COVID-19-associated cardiac microthrombi.

Joint Conference of 59th Annual Meeting of the Association-for-Computational-Linguistics (ACL) / 11th International Joint Conference on Natural Language Processing (IJCNLP) / 6th Workshop on Representation Learning for NLP (RepL4NLP) ; : 1-13, 2021.
Article in English | Web of Science | ID: covidwho-1481502


This work investigates the use of interactively updated label suggestions to improve upon the efficiency of gathering annotations on the task of opinion mining in German Covid-19 social media data. We develop guidelines to conduct a controlled annotation study with social science students and find that suggestions from a model trained on a small, expert-annotated dataset already lead to a substantial improvement - in terms of inter-annotator agreement (+.14 Fleiss' kappa) and annotation quality - compared to students that do not receive any label suggestions. We further find that label suggestions from interactively trained models do not lead to an improvement over suggestions from a static model. Nonetheless, our analysis of suggestion bias shows that annotators remain capable of reflecting upon the suggested label in general. Finally, we confirm the quality of the annotated data in transfer learning experiments between different annotator groups. To facilitate further research in opinion mining on social media data, we release our collected data consisting of 200 expert and 2,785 student annotations.(1)

Journal of Heart and Lung Transplantation ; 40(4):S210-S211, 2021.
Article in English | Web of Science | ID: covidwho-1187632
Laryngorhinootologie ; 99(10): 676-679, 2020 10.
Article in German | MEDLINE | ID: covidwho-726949
Allergologie ; 7(43): 255-271, 20200701.
Article in German | WHO COVID, ELSEVIER | ID: covidwho-679467