Meissner, Konrad

[["dc.bibliographiccitation.journal","Frontiers in Immunology"],["dc.bibliographiccitation.volume","12"],["dc.contributor.affiliation","Abusukhun, Murad; \r\n1\r\nDepartment of Anesthesiology and Intensive Care Medicine, Jena University Hospital, Jena, Germany"],["dc.contributor.affiliation","Winkler, Martin S.; \r\n3\r\nDepartment of Anesthesiology, Emergency and Intensive Care Medicine, University of Göttingen, Göttingen, Germany"],["dc.contributor.affiliation","Pöhlmann, Stefan; \r\n4\r\nInfection Biology Unit, German Primate Center-Leibniz Institute for Primate Research, Göttingen, Germany"],["dc.contributor.affiliation","Moerer, Onnen; \r\n3\r\nDepartment of Anesthesiology, Emergency and Intensive Care Medicine, University of Göttingen, Göttingen, Germany"],["dc.contributor.affiliation","Meissner, Konrad; \r\n3\r\nDepartment of Anesthesiology, Emergency and Intensive Care Medicine, University of Göttingen, Göttingen, Germany"],["dc.contributor.affiliation","Tampe, Björn; \r\n6\r\nDepartment of Nephrology, University of Göttingen, Göttingen, Germany"],["dc.contributor.affiliation","Hofmann-Winkler, Heike; \r\n4\r\nInfection Biology Unit, German Primate Center-Leibniz Institute for Primate Research, Göttingen, Germany"],["dc.contributor.affiliation","Bauer, Michael; \r\n1\r\nDepartment of Anesthesiology and Intensive Care Medicine, Jena University Hospital, Jena, Germany"],["dc.contributor.affiliation","Gräler, Markus H.; \r\n1\r\nDepartment of Anesthesiology and Intensive Care Medicine, Jena University Hospital, Jena, Germany"],["dc.contributor.affiliation","Claus, Ralf A.; \r\n1\r\nDepartment of Anesthesiology and Intensive Care Medicine, Jena University Hospital, Jena, Germany"],["dc.contributor.author","Abusukhun, Murad"],["dc.contributor.author","Winkler, Martin S."],["dc.contributor.author","Pöhlmann, Stefan"],["dc.contributor.author","Moerer, Onnen"],["dc.contributor.author","Meissner, Konrad"],["dc.contributor.author","Tampe, Björn"],["dc.contributor.author","Hofmann-Winkler, Heike"],["dc.contributor.author","Bauer, Michael"],["dc.contributor.author","Gräler, Markus H."],["dc.contributor.author","Claus, Ralf A."],["dc.date.accessioned","2022-02-01T10:31:39Z"],["dc.date.available","2022-02-01T10:31:39Z"],["dc.date.issued","2021"],["dc.date.updated","2022-02-09T13:20:12Z"],["dc.description.abstract","Effective treatment strategies for severe coronavirus disease (COVID-19) remain scarce. Hydrolysis of membrane-embedded, inert sphingomyelin by stress responsive sphingomyelinases is a hallmark of adaptive responses and cellular repair. As demonstrated in experimental and observational clinical studies, the transient and stress-triggered release of a sphingomyelinase, SMPD1, into circulation and subsequent ceramide generation provides a promising target for FDA-approved drugs. Here, we report the activation of sphingomyelinase-ceramide pathway in 23 intensive care patients with severe COVID-19. We observed an increase of circulating activity of sphingomyelinase with subsequent derangement of sphingolipids in serum lipoproteins and from red blood cells (RBC). Consistent with increased ceramide levels derived from the inert membrane constituent sphingomyelin, increased activity of acid sphingomyelinase (ASM) accurately distinguished the patient cohort undergoing intensive care from healthy controls. Positive correlational analyses with biomarkers of severe clinical phenotype support the concept of an essential pathophysiological role of ASM in the course of SARS-CoV-2 infection as well as of a promising role for functional inhibition with anti-inflammatory agents in SARS-CoV-2 infection as also proposed in independent observational studies. We conclude that large-sized multicenter, interventional trials are now needed to evaluate the potential benefit of functional inhibition of this sphingomyelinase in critically ill patients with COVID-19."],["dc.description.abstract","Effective treatment strategies for severe coronavirus disease (COVID-19) remain scarce. Hydrolysis of membrane-embedded, inert sphingomyelin by stress responsive sphingomyelinases is a hallmark of adaptive responses and cellular repair. As demonstrated in experimental and observational clinical studies, the transient and stress-triggered release of a sphingomyelinase, SMPD1, into circulation and subsequent ceramide generation provides a promising target for FDA-approved drugs. Here, we report the activation of sphingomyelinase-ceramide pathway in 23 intensive care patients with severe COVID-19. We observed an increase of circulating activity of sphingomyelinase with subsequent derangement of sphingolipids in serum lipoproteins and from red blood cells (RBC). Consistent with increased ceramide levels derived from the inert membrane constituent sphingomyelin, increased activity of acid sphingomyelinase (ASM) accurately distinguished the patient cohort undergoing intensive care from healthy controls. Positive correlational analyses with biomarkers of severe clinical phenotype support the concept of an essential pathophysiological role of ASM in the course of SARS-CoV-2 infection as well as of a promising role for functional inhibition with anti-inflammatory agents in SARS-CoV-2 infection as also proposed in independent observational studies. We conclude that large-sized multicenter, interventional trials are now needed to evaluate the potential benefit of functional inhibition of this sphingomyelinase in critically ill patients with COVID-19."],["dc.identifier.doi","10.3389/fimmu.2021.784989"],["dc.identifier.eissn","1664-3224"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/98916"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-517"],["dc.publisher","Frontiers Media S.A."],["dc.relation.eissn","1664-3224"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0/"],["dc.title","Activation of Sphingomyelinase-Ceramide-Pathway in COVID-19 Purposes Its Inhibition for Therapeutic Strategies"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.journal","Pulmonology"],["dc.contributor.author","Stephani, C."],["dc.contributor.author","Herrmann, P."],["dc.contributor.author","Ritter, C.O."],["dc.contributor.author","Lotz, J."],["dc.contributor.author","Saager, L."],["dc.contributor.author","Meissner, K."],["dc.contributor.author","Moerer, O."],["dc.date.accessioned","2021-06-01T10:49:53Z"],["dc.date.available","2021-06-01T10:49:53Z"],["dc.date.issued","2021"],["dc.identifier.doi","10.1016/j.pulmoe.2020.12.011"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/86451"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-425"],["dc.relation.issn","2531-0437"],["dc.title","Anatomic lung recruitment in the early phase of severe COVID-19-pneumonia"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.journal","Frontiers in Physiology"],["dc.bibliographiccitation.volume","12"],["dc.contributor.author","Herrmann, Peter"],["dc.contributor.author","Busana, Mattia"],["dc.contributor.author","Cressoni, Massimo"],["dc.contributor.author","Lotz, Joachim"],["dc.contributor.author","Moerer, Onnen"],["dc.contributor.author","Saager, Leif"],["dc.contributor.author","Meissner, Konrad"],["dc.contributor.author","Quintel, Michael"],["dc.contributor.author","Gattinoni, Luciano"],["dc.date.accessioned","2021-12-01T09:24:03Z"],["dc.date.available","2021-12-01T09:24:03Z"],["dc.date.issued","2021"],["dc.description.abstract","Knowledge of gas volume, tissue mass and recruitability measured by the quantitative CT scan analysis (CT-qa) is important when setting the mechanical ventilation in acute respiratory distress syndrome (ARDS). Yet, the manual segmentation of the lung requires a considerable workload. Our goal was to provide an automatic, clinically applicable and reliable lung segmentation procedure. Therefore, a convolutional neural network (CNN) was used to train an artificial intelligence (AI) algorithm on 15 healthy subjects (1,302 slices), 100 ARDS patients (12,279 slices), and 20 COVID-19 (1,817 slices). Eighty percent of this populations was used for training, 20% for testing. The AI and manual segmentation at slice level were compared by intersection over union (IoU). The CT-qa variables were compared by regression and Bland Altman analysis. The AI-segmentation of a single patient required 5–10 s vs. 1–2 h of the manual. At slice level, the algorithm showed on the test set an IOU across all CT slices of 91.3 ± 10.0, 85.2 ± 13.9, and 84.7 ± 14.0%, and across all lung volumes of 96.3 ± 0.6, 88.9 ± 3.1, and 86.3 ± 6.5% for normal lungs, ARDS and COVID-19, respectively, with a U-shape in the performance: better in the lung middle region, worse at the apex and base. At patient level, on the test set, the total lung volume measured by AI and manual segmentation had a R 2 of 0.99 and a bias −9.8 ml [CI: +56.0/−75.7 ml]. The recruitability measured with manual and AI-segmentation, as change in non-aerated tissue fraction had a bias of +0.3% [CI: +6.2/−5.5%] and −0.5% [CI: +2.3/−3.3%] expressed as change in well-aerated tissue fraction. The AI-powered lung segmentation provided fast and clinically reliable results. It is able to segment the lungs of seriously ill ARDS patients fully automatically."],["dc.description.abstract","Knowledge of gas volume, tissue mass and recruitability measured by the quantitative CT scan analysis (CT-qa) is important when setting the mechanical ventilation in acute respiratory distress syndrome (ARDS). Yet, the manual segmentation of the lung requires a considerable workload. Our goal was to provide an automatic, clinically applicable and reliable lung segmentation procedure. Therefore, a convolutional neural network (CNN) was used to train an artificial intelligence (AI) algorithm on 15 healthy subjects (1,302 slices), 100 ARDS patients (12,279 slices), and 20 COVID-19 (1,817 slices). Eighty percent of this populations was used for training, 20% for testing. The AI and manual segmentation at slice level were compared by intersection over union (IoU). The CT-qa variables were compared by regression and Bland Altman analysis. The AI-segmentation of a single patient required 5–10 s vs. 1–2 h of the manual. At slice level, the algorithm showed on the test set an IOU across all CT slices of 91.3 ± 10.0, 85.2 ± 13.9, and 84.7 ± 14.0%, and across all lung volumes of 96.3 ± 0.6, 88.9 ± 3.1, and 86.3 ± 6.5% for normal lungs, ARDS and COVID-19, respectively, with a U-shape in the performance: better in the lung middle region, worse at the apex and base. At patient level, on the test set, the total lung volume measured by AI and manual segmentation had a R 2 of 0.99 and a bias −9.8 ml [CI: +56.0/−75.7 ml]. The recruitability measured with manual and AI-segmentation, as change in non-aerated tissue fraction had a bias of +0.3% [CI: +6.2/−5.5%] and −0.5% [CI: +2.3/−3.3%] expressed as change in well-aerated tissue fraction. The AI-powered lung segmentation provided fast and clinically reliable results. It is able to segment the lungs of seriously ill ARDS patients fully automatically."],["dc.identifier.doi","10.3389/fphys.2021.676118"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/94836"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-478"],["dc.publisher","Frontiers Media S.A."],["dc.relation.eissn","1664-042X"],["dc.rights","http://creativecommons.org/licenses/by/4.0/"],["dc.title","Using Artificial Intelligence for Automatic Segmentation of CT Lung Images in Acute Respiratory Distress Syndrome"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","155"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine"],["dc.bibliographiccitation.volume","29"],["dc.contributor.author","Lier, Martin"],["dc.contributor.author","Nessler, Stefan"],["dc.contributor.author","Stadelmann, Christine"],["dc.contributor.author","Pressler, Meike"],["dc.contributor.author","Saager, Leif"],["dc.contributor.author","Moerer, Onnen"],["dc.contributor.author","Roessler, Markus"],["dc.contributor.author","Meissner, Konrad"],["dc.contributor.author","Winkler, Martin S."],["dc.date.accessioned","2021-11-25T11:12:41Z"],["dc.date.accessioned","2022-08-18T12:33:00Z"],["dc.date.available","2021-11-25T11:12:41Z"],["dc.date.available","2022-08-18T12:33:00Z"],["dc.date.issued","2021-10-30"],["dc.date.updated","2022-07-29T12:17:57Z"],["dc.description.abstract","Abstract\r\n \r\n Background\r\n Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is a highly contagious airborne virus inducing pandemic coronavirus disease 2019 (COVID-19). This is most relevant for medical staff working under harmful conditions in emergencies often dealing with patients and an undefined SARS-CoV-2 status. We aimed to measure the effect of high-class filtering facepieces (FFP) in emergency medical service (EMS) staff by analyzing seroprevalence and history of positive polymerase chain reaction (PCR) for SARS-CoV-2.\r\n \r\n \r\n Method\r\n This observational cohort study included workers in EMS, who were compared with hospital staff (HS) and staff, which was not directly involved in patient care (NPC). All direct patient contacts of EMS workers were protected by FFP2/N95 (filtering face piece protection class 2/non-oil-based particulates filter efficiency 95%) masks, whereas HS was protected by FFP2/N95 exclusively when a patient had a proven or suspected SARS-CoV-2 infection. NPC was not protected by higher FFP. The seroprevalence of SARS-CoV-2 antibodies was analyzed by immunoassay by end of 12/2020 together with the history of a positive PCR. In addition, a self-assessment was performed regarding the quantity of SARS-CoV-2 positive contacts, about flu symptoms and personal belief of previous COVID-19 infections.\r\n \r\n \r\n Results\r\n The period in which contact to SARS-CoV-2 positive patients has been possible was 10 months (March to December 2020)—with 54,681 patient contacts documented for EMS—either emergencies (n = 33,241) or transportation services (n = 21,440). Seven hundred-thirty (n = 730) participants were included into the study (n = EMS: 325, HS: 322 and NPC: 83). The analysis of the survey showed that the exposure to patients with an unknown and consecutive positive SARS-CoV-2 result was significantly higher for EMS when compared to HS (EMS 55% vs. HS 30%, p = 0.01). The incidence of a SARS-CoV-2 infection in our cohort was 1.2% (EMS), 2.2% (HS) and 2.4% (NPC) within the three groups (ns) and lowest in EMS. Furthermore, the belief of previous COVID-19 was significant higher in EMS (19% vs. 10%),\r\n \r\n \r\n Conclusion\r\n The consistent use of FFP2/N95 in EMS is able to prevent work-related SARS-CoV-2 infections in emergency situations. The significance of physical airway protection in exposed medical staff is still relevant especially under the aspect of new viral variants and unclear effectiveness of new vaccines.\r\n \r\n \r\n Graphical Abstract"],["dc.description.sponsorship","Open-Access-Publikationsfonds 2022"],["dc.identifier.citation","Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine. 2021 Oct 30;29(1):155"],["dc.identifier.doi","10.1186/s13049-021-00969-0"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/93536"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/112914"],["dc.language.iso","en"],["dc.publisher","BioMed Central"],["dc.rights","CC BY 4.0"],["dc.rights.holder","The Author(s)"],["dc.subject","Personal protection equipment"],["dc.subject","Filtering facepiece"],["dc.subject","FFP2"],["dc.subject","N95"],["dc.subject","SARS-CoV-2"],["dc.subject","Seroprevalence"],["dc.subject","Emergency medical services"],["dc.title","High class filtering facepiece (FFP) are fundamental and effective in protection of emergency health care workers: an observational cohort study in a German community"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","173"],["dc.bibliographiccitation.journal","Clinical Neurophysiology"],["dc.bibliographiccitation.lastpage","185"],["dc.bibliographiccitation.volume","138"],["dc.contributor.author","Chakalov, Ivan"],["dc.contributor.author","Antal, Andrea"],["dc.contributor.author","Eckardt, S. Simon"],["dc.contributor.author","Paulus, Walter"],["dc.contributor.author","Saager, Leif"],["dc.contributor.author","Meissner, Konrad"],["dc.contributor.author","Bähr, Mathias"],["dc.contributor.author","Moerer, Onnen"],["dc.contributor.author","Stephani, Caspar"],["dc.date.accessioned","2022-05-02T08:09:50Z"],["dc.date.available","2022-05-02T08:09:50Z"],["dc.date.issued","2022"],["dc.identifier.doi","10.1016/j.clinph.2022.03.019"],["dc.identifier.pii","S1388245722002218"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/107481"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-561"],["dc.relation.issn","1388-2457"],["dc.rights.uri","https://www.elsevier.com/tdm/userlicense/1.0/"],["dc.title","The role of the TMS parameters for activation of the corticospinal pathway to the diaphragm"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","318"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","American Journal of Respiratory and Critical Care Medicine"],["dc.bibliographiccitation.lastpage","327"],["dc.bibliographiccitation.volume","203"],["dc.contributor.author","Giosa, Lorenzo"],["dc.contributor.author","Busana, Mattia"],["dc.contributor.author","Bonifazi, Matteo"],["dc.contributor.author","Romitti, Federica"],["dc.contributor.author","Vassalli, Francesco"],["dc.contributor.author","Pasticci, Iacopo"],["dc.contributor.author","Macrì, Matteo Maria"],["dc.contributor.author","D’Albo, Rosanna"],["dc.contributor.author","Collino, Francesca"],["dc.contributor.author","Gatta, Alessandro"],["dc.contributor.author","Palumbo, Maria Michela"],["dc.contributor.author","Herrmann, Peter"],["dc.contributor.author","Moerer, Onnen"],["dc.contributor.author","Iapichino, Gaetano"],["dc.contributor.author","Meissner, Konrad"],["dc.contributor.author","Quintel, Michael"],["dc.contributor.author","Gattinoni, Luciano"],["dc.date.accessioned","2021-04-14T08:29:57Z"],["dc.date.available","2021-04-14T08:29:57Z"],["dc.date.issued","2021"],["dc.identifier.doi","10.1164/rccm.202005-1687OC"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/83049"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-399"],["dc.relation.eissn","1535-4970"],["dc.relation.issn","1073-449X"],["dc.title","Mobilizing Carbon Dioxide Stores. An Experimental Study"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]