Publications by authors named "K G Dahm"

Impaired kinase signalling leads to various diseases, including cancer. At the same time, kinases make up the majority of the druggable genome and targeting kinase activity has proven to be a successful first-line therapy for many cancers. Among the best-studied kinases are the mitogen-activated protein kinases (MAPKs), which regulate cell proliferation, differentiation, motility, and survival.

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Dexamethasone is a life-saving treatment for severe COVID-19, yet its mechanism of action is unknown, and many patients deteriorate or die despite timely treatment initiation. Here, we identify dexamethasone treatment-induced cellular and molecular changes associated with improved survival in COVID-19 patients. We observed a reversal of transcriptional hallmark signatures in monocytes associated with severe COVID-19 and the induction of a monocyte substate characterized by the expression of glucocorticoid-response genes.

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As the number and complexity of transcriptomic datasets increase, there is a rising demand for accessible and user-friendly analysis tools. Here, we present hCoCena (horizontal construction of co-expression networks and analysis), a toolbox facilitating the analysis of a single dataset, as well as the joint analysis of multiple datasets. We describe steps for workspace setup, formatting tables, data processing, and network integration.

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The voltage-gated potassium channel 1.6 (Kv1.6) plays a vital role in ocular neurovascular beds and exerts its modulatory functions via interaction with other proteins.

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Background: Coronavirus disease 2019 (COVID-19) patients can develop pulmonary fibrosis (PF), which is associated with impaired outcome. We assessed specific leukocytic transcriptome profiles associated with PF and the influence of early dexamethasone (DEXA) treatment on the clinical course of PF in critically ill COVID-19 patients.

Methods: We performed a pre-post design study in 191 COVID-19 patients admitted to the Intensive Care Unit (ICU) spanning two treatment cohorts: the pre-DEXA- (n = 67) and the DEXA-cohort (n = 124).

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