@article{MeintrupBorgmannSeidletal.2021,
  author    = {Meintrup, David and Borgmann, Stefan and Seidl, Karlheinz and Stecher, Melanie and Jakob, Carolin E. M. and Pilgram, Lisa and Spinner, Christoph D. and Rieg, Siegbert and Isberner, Nora and Hower, Martin and Vehreschild, Maria and G{\"o}pel, Siri and Hanses, Frank and Nowak-Machen, Martina},
  title     = {Specific risk factors for fatal outcome in critically ill COVID-19 patients: results from a European multicenter study},
  series = {Journal of Clinical Medicine},
  volume    = {10},
  journal   = {Journal of Clinical Medicine},
  number    = {17},
  issn      = {2077-0383},
  doi       = {10.3390/jcm10173855},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-245191},
  year      = {2021},
  abstract  = {(1) Background: The aim of our study was to identify specific risk factors for fatal outcome in critically ill COVID-19 patients. (2) Methods: Our data set consisted of 840 patients enclosed in the LEOSS registry. Using lasso regression for variable selection, a multifactorial logistic regression model was fitted to the response variable survival. Specific risk factors and their odds ratios were derived. A nomogram was developed as a graphical representation of the model. (3) Results: 14 variables were identified as independent factors contributing to the risk of death for critically ill COVID-19 patients: age (OR 1.08, CI 1.06-1.10), cardiovascular disease (OR 1.64, CI 1.06-2.55), pulmonary disease (OR 1.87, CI 1.16-3.03), baseline Statin treatment (0.54, CI 0.33-0.87), oxygen saturation (unit = 1\%, OR 0.94, CI 0.92-0.96), leukocytes (unit 1000/μL, OR 1.04, CI 1.01-1.07), lymphocytes (unit 100/μL, OR 0.96, CI 0.94-0.99), platelets (unit 100,000/μL, OR 0.70, CI 0.62-0.80), procalcitonin (unit ng/mL, OR 1.11, CI 1.05-1.18), kidney failure (OR 1.68, CI 1.05-2.70), congestive heart failure (OR 2.62, CI 1.11-6.21), severe liver failure (OR 4.93, CI 1.94-12.52), and a quick SOFA score of 3 (OR 1.78, CI 1.14-2.78). The nomogram graphically displays the importance of these 14 factors for mortality. (4) Conclusions: There are risk factors that are specific to the subpopulation of critically ill COVID-19 patients.},
  language  = {en}
}
@article{DerakhshaniKurzJaptoketal.2019,
  author    = {Derakhshani, Shaghayegh and Kurz, Andreas and Japtok, Lukasz and Schumacher, Fabian and Pilgram, Lisa and Steinke, Maria and Kleuser, Burkhard and Sauer, Markus and Schneider-Schaulies, Sibylle and Avota, Elita},
  title     = {Measles virus infection fosters dendritic cell motility in a 3D environment to enhance transmission to target cells in the respiratory epithelium},
  series = {Frontiers in Immunology},
  volume    = {10},
  journal   = {Frontiers in Immunology},
  number    = {1294},
  doi       = {10.3389/fimmu.2019.01294},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-201818},
  year      = {2019},
  abstract  = {Transmission of measles virus (MV) from dendritic to airway epithelial cells is considered as crucial to viral spread late in infection. Therefore, pathways and effectors governing this process are promising targets for intervention. To identify these, we established a 3D respiratory tract model where MV transmission by infected dendritic cells (DCs) relied on the presence of nectin-4 on H358 lung epithelial cells. Access to recipient cells is an important prerequisite for transmission, and we therefore analyzed migration of MV-exposed DC cultures within the model. Surprisingly, enhanced motility toward the epithelial layer was observed for MV-infected DCs as compared to their uninfected siblings. This occurred independently of factors released from H358 cells indicating that MV infection triggered cytoskeletal remodeling associated with DC polarization enforced velocity. Accordingly, the latter was also observed for MV-infected DCs in collagen matrices and was particularly sensitive to ROCK inhibition indicating infected DCs preferentially employed the amoeboid migration mode. This was also implicated by loss of podosomes and reduced filopodial activity both of which were retained in MV-exposed uninfected DCs. Evidently, sphingosine kinase (SphK) and sphingosine-1-phosphate (S1P) as produced in response to virus-infection in DCs contributed to enhanced velocity because this was abrogated upon inhibition of sphingosine kinase activity. These findings indicate that MV infection promotes a push-and-squeeze fast amoeboid migration mode via the SphK/S1P system characterized by loss of filopodia and podosome dissolution. Consequently, this enables rapid trafficking of virus toward epithelial cells during viral exit.},
  language  = {en}
}
@article{PilgramEberweinWilleetal.2021,
  author    = {Pilgram, Lisa and Eberwein, Lukas and Wille, Kai and Koehler, Felix C. and Stecher, Melanie and Rieg, Siegbert and Kielstein, Jan T. and Jakob, Carolin E. M. and R{\"u}thrich, Maria and Burst, Volker and Prasser, Fabian and Borgmann, Stefan and M{\"u}ller, Roman-Ulrich and Lanznaster, Julia and Isberner, Nora and Tometten, Lukas and Dolff, Sebastian},
  title     = {Clinical course and predictive risk factors for fatal outcome of SARS-CoV-2 infection in patients with chronic kidney disease},
  series = {Infection},
  volume    = {49},
  journal   = {Infection},
  number    = {4},
  organization    = {LEOSS Study group},
  issn      = {0300-8126},
  doi       = {10.1007/s15010-021-01597-7},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-308957},
  pages     = {725-737},
  year      = {2021},
  abstract  = {Purpose The ongoing pandemic caused by the novel severe acute respiratory coronavirus 2 (SARS-CoV-2) has stressed health systems worldwide. Patients with chronic kidney disease (CKD) seem to be more prone to a severe course of coronavirus disease (COVID-19) due to comorbidities and an altered immune system. The study's aim was to identify factors predicting mortality among SARS-CoV-2-infected patients with CKD. Methods We analyzed 2817 SARS-CoV-2-infected patients enrolled in the Lean European Open Survey on SARS-CoV-2-infected patients and identified 426 patients with pre-existing CKD. Group comparisons were performed via Chi-squared test. Using univariate and multivariable logistic regression, predictive factors for mortality were identified. Results Comparative analyses to patients without CKD revealed a higher mortality (140/426, 32.9\% versus 354/2391, 14.8\%). Higher age could be confirmed as a demographic predictor for mortality in CKD patients (> 85 years compared to 15-65 years, adjusted odds ratio (aOR) 6.49, 95\% CI 1.27-33.20, p = 0.025). We further identified markedly elevated lactate dehydrogenase (> 2 × upper limit of normal, aOR 23.21, 95\% CI 3.66-147.11, p < 0.001), thrombocytopenia (< 120,000/µl, aOR 11.66, 95\% CI 2.49-54.70, p = 0.002), anemia (Hb < 10 g/dl, aOR 3.21, 95\% CI 1.17-8.82, p = 0.024), and C-reactive protein (≥ 30 mg/l, aOR 3.44, 95\% CI 1.13-10.45, p = 0.029) as predictors, while renal replacement therapy was not related to mortality (aOR 1.15, 95\% CI 0.68-1.93, p = 0.611). Conclusion The identified predictors include routinely measured and universally available parameters. Their assessment might facilitate risk stratification in this highly vulnerable cohort as early as at initial medical evaluation for SARS-CoV-2.},
  language  = {en}
}