42 results on '"Schipper D"'
Search Results
2. Assess and validate predictive performance of models for in-hospital mortality in COVID-19 patients: A retrospective cohort study in the Netherlands comparing the value of registry data with high-granular electronic health records
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Vagliano, Iacopo, Schut, Martijn C., Abu-Hanna, Ameen, Dongelmans, Dave A., de Lange, Dylan W., Gommers, Diederik, Cremer, Olaf L., Bosman, Rob J., Rigter, Sander, Wils, Evert-Jan, Frenzel, Tim, de Jong, Remko, Peters, Marco A.A., Kamps, Marlijn J.A., Ramnarain, Dharmanand, Nowitzky, Ralph, Nooteboom, Fleur G.C.A., de Ruijter, Wouter, Urlings-Strop, Louise C., Smit, Ellen G.M., Mehagnoul-Schipper, D. Jannet, Dormans, Tom, de Jager, Cornelis P.C., Hendriks, Stefaan H.A., Achterberg, Sefanja, Oostdijk, Evelien, Reidinga, Auke C., Festen-Spanjer, Barbara, Brunnekreef, Gert B., Cornet, Alexander D., van den Tempel, Walter, Boelens, Age D., Koetsier, Peter, Lens, Judith, Faber, Harald J., Karakus, A., Entjes, Robert, de Jong, Paul, Rettig, Thijs C.D., Reuland, M.C., Arbous, Sesmu, Fleuren, Lucas M., Dam, Tariq A., Thoral, Patrick J., Lalisang, Robbert C.A., Tonutti, Michele, de Bruin, Daan P., Elbers, Paul W.G., and de Keizer, Nicolette F.
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- 2022
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3. Predicting responders to prone positioning in mechanically ventilated patients with COVID-19 using machine learning
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Dam, Tariq A., Roggeveen, Luca F., van Diggelen, Fuda, Fleuren, Lucas M., Jagesar, Ameet R., Otten, Martijn, de Vries, Heder J., Gommers, Diederik, Cremer, Olaf L., Bosman, Rob J., Rigter, Sander, Wils, Evert-Jan, Frenzel, Tim, Dongelmans, Dave A., de Jong, Remko, Peters, Marco A. A., Kamps, Marlijn J. A., Ramnarain, Dharmanand, Nowitzky, Ralph, Nooteboom, Fleur G. C. A., de Ruijter, Wouter, Urlings-Strop, Louise C., Smit, Ellen G. M., Mehagnoul-Schipper, D. Jannet, Dormans, Tom, de Jager, Cornelis P. C., Hendriks, Stefaan H. A., Achterberg, Sefanja, Oostdijk, Evelien, Reidinga, Auke C., Festen-Spanjer, Barbara, Brunnekreef, Gert B., Cornet, Alexander D., van den Tempel, Walter, Boelens, Age D., Koetsier, Peter, Lens, Judith, Faber, Harald J., Karakus, A., Entjes, Robert, de Jong, Paul, Rettig, Thijs C. D., Arbous, Sesmu, Vonk, Sebastiaan J. J., Machado, Tomas, Herter, Willem E., de Grooth, Harm-Jan, Thoral, Patrick J., Girbes, Armand R. J., Hoogendoorn, Mark, and Elbers, Paul W. G.
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- 2022
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4. Age Moderates the Effect of Obesity on Mortality Risk in Critically Ill Patients With COVID-19: A Nationwide Observational Cohort Study*
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den Uil, Corstiaan A., Termorshuizen, Fabian, Rietdijk, Wim J. R., Sablerolles, Roos S. G., van der Kuy, Hugo P. M., Haas, Lenneke E. M., van der Voort, Peter H. J., de Lange, Dylan W., Pickkers, Peter, de Keizer, Nicolette F., Arbous, M. S., Barnas, M. G. W., Boer, D. P., Bosman, R. J., Brunnekreef, G. B., de Bruin, M. Th., de Graaff, M. J., de Jong, R. M., de Meijer, A. R., de Ruijter, W., de Waal, R., Dijkhuizen, A., Dongelmans, D. A., Dormans, T. P. J., Draisma, A., Drogt, I., Eikemans, B. J. W., Elbers, P. W. G., Epker, J. L., Erkamp, M. L., Festen-Spanjer, B., Frenzel, T., Georgieva, L., Gritters, N. C., Hené, I. Z., Hoeksema, M., Holtkamp, J. W. M., Hoogendoorn, M. E., Jacobs, C. J. G. M., Janssen, I. T. A., Kieft, H., Koetsier, M. P., Koning, T. J. J., Kreeftenberg, H., Kusadasi, N., Lens, J. A., Lutisan, J. G., Mehagnoul-Schipper, D. J., Moolenaar, D., Nooteboom, F., Postma, N., Pruijsten, R. V., Ramnarain, D., Reidinga, A. C., Rengers, E., Rijkeboer, A. A., Rijpstra, T., Rozendaal, F. W., Schnabel, R. M., Silderhuis, V. M., Spijkstra, J. J., Spronk, P. E., te Velde, L. F., Urlings-Strop, L. C., van den Berg, A. E., van den Berg, R., van der Horst, I. C. C., van Driel, E. M., van Gulik, L., van Iersel, F. M., van Lieshout, M., van Oers, J. A. H., van Slobbe-Bijlsma, E. R., van Tellingen, M., Vandeputte, J., Verbiest, D. P., Versluis, D. J., Verweij, E., Vrolijk-de Mos, M., and Wesselink, R. M. J.
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- 2023
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5. Predictors for extubation failure in COVID-19 patients using a machine learning approach
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Fleuren, Lucas M., Dam, Tariq A., Tonutti, Michele, de Bruin, Daan P., Lalisang, Robbert C. A., Gommers, Diederik, Cremer, Olaf L., Bosman, Rob J., Rigter, Sander, Wils, Evert-Jan, Frenzel, Tim, Dongelmans, Dave A., de Jong, Remko, Peters, Marco, Kamps, Marlijn J. A., Ramnarain, Dharmanand, Nowitzky, Ralph, Nooteboom, Fleur G. C. A., de Ruijter, Wouter, Urlings-Strop, Louise C., Smit, Ellen G. M., Mehagnoul-Schipper, D. Jannet, Dormans, Tom, de Jager, Cornelis P. C., Hendriks, Stefaan H. A., Achterberg, Sefanja, Oostdijk, Evelien, Reidinga, Auke C., Festen-Spanjer, Barbara, Brunnekreef, Gert B., Cornet, Alexander D., van den Tempel, Walter, Boelens, Age D., Koetsier, Peter, Lens, Judith, Faber, Harald J., Karakus, A., Entjes, Robert, de Jong, Paul, Rettig, Thijs C. D., Arbous, Sesmu, Vonk, Sebastiaan J. J., Fornasa, Mattia, Machado, Tomas, Houwert, Taco, Hovenkamp, Hidde, Noorduijn Londono, Roberto, Quintarelli, Davide, Scholtemeijer, Martijn G., de Beer, Aletta A., Cinà, Giovanni, Kantorik, Adam, de Ruijter, Tom, Herter, Willem E., Beudel, Martijn, Girbes, Armand R. J., Hoogendoorn, Mark, Thoral, Patrick J., and Elbers, Paul W. G.
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- 2021
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6. The Dutch Data Warehouse, a multicenter and full-admission electronic health records database for critically ill COVID-19 patients
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Fleuren, Lucas M., Dam, Tariq A., Tonutti, Michele, de Bruin, Daan P., Lalisang, Robbert C. A., Gommers, Diederik, Cremer, Olaf L., Bosman, Rob J., Rigter, Sander, Wils, Evert-Jan, Frenzel, Tim, Dongelmans, Dave A., de Jong, Remko, Peters, Marco, Kamps, Marlijn J. A., Ramnarain, Dharmanand, Nowitzky, Ralph, Nooteboom, Fleur G. C. A., de Ruijter, Wouter, Urlings-Strop, Louise C., Smit, Ellen G. M., Mehagnoul-Schipper, D. Jannet, Dormans, Tom, de Jager, Cornelis P. C., Hendriks, Stefaan H. A., Achterberg, Sefanja, Oostdijk, Evelien, Reidinga, Auke C., Festen-Spanjer, Barbara, Brunnekreef, Gert B., Cornet, Alexander D., van den Tempel, Walter, Boelens, Age D., Koetsier, Peter, Lens, Judith, Faber, Harald J., Karakus, A., Entjes, Robert, de Jong, Paul, Rettig, Thijs C. D., Arbous, Sesmu, Vonk, Sebastiaan J. J., Fornasa, Mattia, Machado, Tomas, Houwert, Taco, Hovenkamp, Hidde, Noorduijn-Londono, Roberto, Quintarelli, Davide, Scholtemeijer, Martijn G., de Beer, Aletta A., Cina, Giovanni, Beudel, Martijn, Herter, Willem E., Girbes, Armand R. J., Hoogendoorn, Mark, Thoral, Patrick J., and Elbers, Paul W. G.
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- 2021
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7. Incidence, Risk Factors and Outcome of Suspected Central Venous Catheter-related Infections in Critically Ill COVID-19 Patients
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Smit, Jasper M., Exterkate, Lotte, Van Tienhoven, Arne J., Haaksma, Mark E., Heldeweg, Micah L.A., Fleuren, Lucas, Thoral, Patrick, Dam, Tariq A., Heunks, Leo M.A., Gommers, Diederik, Cremer, Olaf L., Bosman, Rob J., Rigter, Sander, Wils, Evert Jan, Frenzel, Tim, Vlaar, Alexander P., Dongelmans, Dave A., De Jong, Remko, Peters, Marco, Kamps, Marlijn J.A., Ramnarain, Dharmanand, Nowitzky, Ralph, Nooteboom, Fleur G.C.A., De Ruijter, Wouter, Urlings-Strop, Louise C., Smit, Ellen G.M., Mehagnoul-Schipper, D. Jannet, Dormans, Tom, De Jager, Cornelis P.C., Hendriks, Stefaan H.A., Achterberg, Sefanja, Oostdijk, Evelien, Reidinga, Auke C., Festen-Spanjer, Barbara, Brunnekreef, Gert B., Cornet, Alexander D., Van Den Tempel, Walter, Boelens, Age D., Koetsier, Peter, Lens, Judith, Faber, Harald J., Karakus, A., Entjes, Robert, De Jong, Paul, Rettig, Thijs C.D., Arbous, Sesmu, Vonk, Bas, Machado, Tomas, Girbes, Armand R.J., Sieswerda, Elske, Elbers, Paul W.G., Tuinman, Pieter R., Intensive care medicine, Radiology and nuclear medicine, Anesthesiology, Internal medicine, ACS - Diabetes & metabolism, ACS - Microcirculation, Amsterdam Cardiovascular Sciences, Cardio-thoracic surgery, General practice, AII - Infectious diseases, Medical Microbiology and Infection Prevention, ACS - Pulmonary hypertension & thrombosis, Intensive Care Medicine, APH - Quality of Care, Graduate School, AII - Cancer immunology, CCA - Cancer biology and immunology, and Intensive Care
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catheter-related infections ,Catheterization, Central Venous ,Critical Illness ,Incidence ,Other Research Radboud Institute for Health Sciences [Radboudumc 0] ,COVID-19 ,Critical Care and Intensive Care Medicine ,Central venous catheters ,All institutes and research themes of the Radboud University Medical Center ,Risk Factors ,Emergency Medicine ,Humans ,Retrospective Studies ,intensive care - Abstract
Background: Aims of this study were to investigate the prevalence and incidence of catheter-related infection, identify risk factors, and determine the relation of catheter-related infection with mortality in critically ill COVID-19 patients. Methods: This was a retrospective cohort study of central venous catheters (CVCs) in critically ill COVID-19 patients. Eligible CVC insertions required an indwelling time of at least 48 hours and were identified using a full-admission electronic health record database. Risk factors were identified using logistic regression. Differences in survival rates at day 28 of follow-up were assessed using a log-rank test and proportional hazard model. Results: In 538 patients, a total of 914 CVCs were included. Prevalence and incidence of suspected catheter-related infection were 7.9% and 9.4 infections per 1,000 catheter indwelling days, respectively. Prone ventilation for more than 5 days was associated with increased risk of suspected catheter-related infection; odds ratio, 5.05 (95% confidence interval 2.12-11.0). Risk of death was significantly higher in patients with suspected catheter-related infection (hazard ratio, 1.78; 95% confidence interval, 1.25-2.53). Conclusions: This study shows that in critically ill patients with COVID-19, prevalence and incidence of suspected catheter-related infection are high, prone ventilation is a risk factor, and mortality is higher in case of catheter-related infection.
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- 2022
8. SARS-CoV-2 ORF8 accessory protein is a virulence factor
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Bello-Perez, M., Hurtado-Tamayo, J., Mykytyn, A. Z., Lamers, M. M., Requena-Platek, R., Schipper, D., Muñoz-Santos, D., Ripoll-Gómez, J., Esteban, A., Sánchez-Cordón, P. J., Enjuanes, L., Haagmans, B. L., Sola, I., Bello-Perez, M., Hurtado-Tamayo, J., Mykytyn, A. Z., Lamers, M. M., Requena-Platek, R., Schipper, D., Muñoz-Santos, D., Ripoll-Gómez, J., Esteban, A., Sánchez-Cordón, P. J., Enjuanes, L., Haagmans, B. L., and Sola, I.
- Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) encodes six accessory proteins (3a, 6, 7a, 7b, 8, and 9b) for which limited information is available on their role in pathogenesis. We showed that the deletion of open reading frames (ORFs) 6, 7a, or 7b individually did not significantly impact viral pathogenicity in humanized K18-hACE2 transgenic mice. In contrast, the deletion of ORF8 partially attenuated SARS-CoV-2, resulting in reduced lung pathology and 40% less mortality, indicating that ORF8 is a critical determinant of SARS-CoV-2 pathogenesis. Attenuation of SARS-CoV-2-∆8 was not associated with a significant decrease in replication either in the lungs of mice or in organoid-derived human airway cells. An increase in the interferon signaling at early times post-infection (1 dpi) in the lungs of mice and a decrease in the pro-inflammatory and interferon response at late times post-infection, both in the lungs of mice (6 dpi) and in organoid-derived human airway cells [72 hours post-infection (hpi)], were observed. The early, but not prolonged, interferon response along with the lower inflammatory response could explain the partial attenuation of SARS-CoV-∆8. The presence of ORF8 in SARS-CoV-2 was associated with an increase in the number of macrophages in the lungs of mice. In addition, the supernatant of SARS-CoV-2-WT (wild-type)-infected organoid-derived cells enhanced the activation of macrophages as compared to SARS-CoV-2-∆8-infected cells. These results show that ORF8 is a virulence factor involved in inflammation that could be targeted in COVID-19 therapies. IMPORTANCE The relevance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ORF8 in the pathogenesis of COVID-19 is unclear. Virus natural isolates with deletions in ORF8 were associated with wild milder disease, suggesting that ORF8 might contribute to SARS-CoV-2 virulence. This manuscript shows that ORF8 is involved in inflammation and in the activation of macrophages in
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- 2023
9. SARS-CoV-2 ORF8 accessory protein is a virulence factor
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Ministerio de Economía y Competitividad (España), Agencia Estatal de Investigación (España), Ministerio de Ciencia, Innovación y Universidades (España), European Commission, National Institutes of Health (US), Netherlands Organisation for Health Research and Development, Bello-Pérez, Melissa [0000-0002-9212-083X], https://ror.org/02gfc7t72, Bello-Pérez, Melissa, Hurtado-Tamayo, Jesús, Mykytyn, Anna Z., Lamers, Mart M., Requena-Platek, Ricardo, Schipper, D., Muñoz-Santos, Diego, Ripoll-Gómez, Jorge, Esteban, Ana, Sánchez-Cordón, P. J., Enjuanes Sánchez, Luis, Haagmans, Bart L., Solá Gurpegui, Isabel, Ministerio de Economía y Competitividad (España), Agencia Estatal de Investigación (España), Ministerio de Ciencia, Innovación y Universidades (España), European Commission, National Institutes of Health (US), Netherlands Organisation for Health Research and Development, Bello-Pérez, Melissa [0000-0002-9212-083X], https://ror.org/02gfc7t72, Bello-Pérez, Melissa, Hurtado-Tamayo, Jesús, Mykytyn, Anna Z., Lamers, Mart M., Requena-Platek, Ricardo, Schipper, D., Muñoz-Santos, Diego, Ripoll-Gómez, Jorge, Esteban, Ana, Sánchez-Cordón, P. J., Enjuanes Sánchez, Luis, Haagmans, Bart L., and Solá Gurpegui, Isabel
- Abstract
The relevance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ORF8 in the pathogenesis of COVID-19 is unclear. Virus natural isolates with deletions in ORF8 were associated with wild milder disease, suggesting that ORF8 might contribute to SARS-CoV-2 virulence. This manuscript shows that ORF8 is involved in inflammation and in the activation of macrophages in two experimental systems: humanized K18-hACE2 transgenic mice and organoid-derived human airway cells. These results identify ORF8 protein as a potential target for COVID-19 therapies.
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- 2023
10. Validity of Amontons’ law for run-in short-cut aramid fiber reinforced elastomers: The effect of epoxy coated fibers
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Khafidh, M., Schipper, D. J., Masen, M. A., Vleugels, N., Dierkes, W. K., and Noordermeer, J. W. M.
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Friction between two contacting surfaces is studied extensively. One of the known friction theories is Amontons’ law which states that the friction force is proportional to the normal force. However, Amontons’ law has been found to be invalid for elastomers. In the present study, the validity of Amontons’ law for short-cut aramid fiber reinforced elastomers is studied. Two types of fillers are used to reinforce the elastomers, namely highly dispersible silica and short-cut aramid fibers. Short-cut aramid fibers with two different surface treatments are used, namely non-reactive fibers with standard oily finish (SF-fibers) and fibers treated with an epoxy coating (EF-fibers). A pin-on-disc tribometer is used to investigate the frictional behavior of the composites in sliding contact with a granite counter surface. The results show that, after the run-in phase, Amontons’ law is valid for those composites that are reinforced by short-cut aramid fibers (without reinforcing filler, i.e., silica) if the contact pressure is below a threshold value. However, once the contact pressure exceeds this threshold value, Amontons’ law will be invalid. The threshold contact pressure of the composites containing EF-fibers is higher than of the composites containing SF-fibers. The composites that are reinforced by silica and short-cut aramid fibers do not follow Amontons’ law.
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- 2024
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11. Comparison of patient characteristics and long-term mortality between transferred and non-transferred COVID-19 patients in Dutch intensive care units: A national cohort study
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Wortel, Safira A., Bakhshi-Raiez, Ferishta, Termorshuizen, Fabian, de Lange, Dylan W., Dongelmans, Dave A., de Keizer, Nicolette F., Arbous, M. S., Barnas, M. G. W., Bindels, A. J. G. H., Boer, D. P., Bosman, R. J., Brunnekreef, G. B., de Bruin, M. Th., de Graaff, M., de Jong, R. M., de Meijer, A. R., de Ruijter, W., de Waal, R., Dijkhuizen, A., Dormans, T. P. J., Draisma, A., Drogt, I., Eikemans, B. J. W., Elbers, P. W. G., Epker, J. L., Erkamp, M. L., Festen-Spanjer, B., Frenzel, T., Gommers, D., Gritters, N. C., Hené, I. Z., Hoeksema, M., Holtkamp, J. W. M., Hoogendoorn, M. E., Houwink, A. P. I., Jacobs, C. J. M. G., Janssen, I. T. A., Kieft, H., Koetsier, M. P., Koning, T. J. J., Kusadasi, N., Lens, J. A., Lutisan, J. G., Mehagnoul-Schipper, D. J., Moolenaar, D., Nooteboom, F., Pruijsten, R. V., Ramnarain, D., Reidinga, A. C., Rengers, E., Rijkeboer, A. A., Rozendaal, F. W., Schnabel, R. M., Silderhuis, V. M., Spijkstra, J. J., Spronk, P., te Velde, L. F., Urlings-Strop, L. C., van Bussel, B. C. T., van den Berg, A. E., van den Berg, R., van der Voort, P. H. J., van Driel, E. M., van Gulik, L., van Iersel, F. M., van Lieshout, M., van Slobbe-Bijlsma, E. R., van Tellingen, M., Vandeputte, J., Verbiest, D. P., Versluis, D. J., Verweij, E., Mos, M. Vrolijk-de, Wesselink, R. M. J., Graduate School, Medical Informatics, APH - Methodology, APH - Quality of Care, Intensive Care Medicine, APH - Digital Health, Radiology and Nuclear Medicine, ACS - Microcirculation, and ANS - Neurovascular Disorders
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COVID-19 ,intrahospital transfer ,severity of illness ,intensive care unit ,mortality - Abstract
Background: COVID-19 patients were often transferred to other intensive care units (ICUs) to prevent that ICUs would reach their maximum capacity. However, transferring ICU patients is not free of risk. We aim to compare the characteristics and outcomes of transferred versus non-transferred COVID-19 ICU patients in the Netherlands. Methods: We included adult COVID-19 patients admitted to Dutch ICUs between March 1, 2020 and July 1, 2021. We compared the patient characteristics and outcomes of non-transferred and transferred patients and used a Directed Acyclic Graph to identify potential confounders in the relationship between transfer and mortality. We used these confounders in a Cox regression model with left truncation at the day of transfer to analyze the effect of transfers on mortality during the 180 days after ICU admission. Results: We included 10,209 patients: 7395 non-transferred and 2814 (27.6%) transferred patients. In both groups, the median age was 64 years. Transferred patients were mostly ventilated at ICU admission (83.7% vs. 56.2%) and included a larger proportion of low-risk patients (70.3% vs. 66.5% with mortality risk
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- 2022
12. Dynamic prediction of mortality in COVID-19 patients in the intensive care unit:A retrospective multi-center cohort study
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Smit, J. M., Krijthe, J. H., Endeman, H., Tintu, A. N., de Rijke, Y. B., Gommers, D. A.M.P.J., Cremer, O. L., Bosman, R. J., Rigter, S., Wils, E. J., Frenzel, T., Dongelmans, D. A., De Jong, R., Peters, M. A.A., Kamps, M. J.A., Ramnarain, D., Nowitzky, R., Nooteboom, F. G.C.A., De Ruijter, W., Urlings-Strop, L. C., Smit, E. G.M., Mehagnoul-Schipper, D. J., Dormans, T., De Jager, C. P.C., Hendriks, S. H.A., Achterberg, S., Oostdijk, E., Reidinga, A. C., Festen-Spanjer, B., Brunnekreef, G. B., Cornet, A. D., Van den Tempel, W., Boelens, A. D., Koetsier, P., Lens, J. A., Faber, H. J., karakus, A., Entjes, R., De Jong, P., Rettig, T. C.D., Arbous, M. S., Lalisang, R. C.A., Tonutti, M., De Bruin, D. P., Elbers, P. W.G., Van Bommel, J., Reinders, M. J.T., Smit, J. M., Krijthe, J. H., Endeman, H., Tintu, A. N., de Rijke, Y. B., Gommers, D. A.M.P.J., Cremer, O. L., Bosman, R. J., Rigter, S., Wils, E. J., Frenzel, T., Dongelmans, D. A., De Jong, R., Peters, M. A.A., Kamps, M. J.A., Ramnarain, D., Nowitzky, R., Nooteboom, F. G.C.A., De Ruijter, W., Urlings-Strop, L. C., Smit, E. G.M., Mehagnoul-Schipper, D. J., Dormans, T., De Jager, C. P.C., Hendriks, S. H.A., Achterberg, S., Oostdijk, E., Reidinga, A. C., Festen-Spanjer, B., Brunnekreef, G. B., Cornet, A. D., Van den Tempel, W., Boelens, A. D., Koetsier, P., Lens, J. A., Faber, H. J., karakus, A., Entjes, R., De Jong, P., Rettig, T. C.D., Arbous, M. S., Lalisang, R. C.A., Tonutti, M., De Bruin, D. P., Elbers, P. W.G., Van Bommel, J., and Reinders, M. J.T.
- Abstract
Background: The COVID-19 pandemic continues to overwhelm intensive care units (ICUs) worldwide, and improved prediction of mortality among COVID-19 patients could assist decision making in the ICU setting. In this work, we report on the development and validation of a dynamic mortality model specifically for critically ill COVID-19 patients and discuss its potential utility in the ICU. Methods: We collected electronic medical record (EMR) data from 3222 ICU admissions with a COVID-19 infection from 25 different ICUs in the Netherlands. We extracted daily observations of each patient and fitted both a linear (logistic regression) and non-linear (random forest) model to predict mortality within 24 h from the moment of prediction. Isotonic regression was used to re-calibrate the predictions of the fitted models. We evaluated the models in a leave-one-ICU-out (LOIO) cross-validation procedure. Results: The logistic regression and random forest model yielded an area under the receiver operating characteristic curve of 0.87 [0.85; 0.88] and 0.86 [0.84; 0.88], respectively. The recalibrated model predictions showed a calibration intercept of −0.04 [−0.12; 0.04] and slope of 0.90 [0.85; 0.95] for logistic regression model and a calibration intercept of −0.19 [−0.27; −0.10] and slope of 0.89 [0.84; 0.94] for the random forest model. Discussion: We presented a model for dynamic mortality prediction, specifically for critically ill COVID-19 patients, which predicts near-term mortality rather than in-ICU mortality. The potential clinical utility of dynamic mortality models such as benchmarking, improving resource allocation and informing family members, as well as the development of models with more causal structure, should be topics for future research.
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- 2022
13. Assess and validate predictive performance of models for in-hospital mortality in COVID-19 patients:A retrospective cohort study in the Netherlands comparing the value of registry data with high-granular electronic health records
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Vagliano, Iacopo, Schut, Martijn C., Abu-Hanna, Ameen, Dongelmans, Dave A., de Lange, Dylan W., Gommers, Diederik, Cremer, Olaf L., Bosman, Rob J., Rigter, Sander, Wils, Evert Jan, Frenzel, Tim, de Jong, Remko, Peters, Marco A.A., Kamps, Marlijn J.A., Ramnarain, Dharmanand, Nowitzky, Ralph, Nooteboom, Fleur G.C.A., de Ruijter, Wouter, Urlings-Strop, Louise C., Smit, Ellen G.M., Mehagnoul-Schipper, D. Jannet, Dormans, Tom, de Jager, Cornelis P.C., Hendriks, Stefaan H.A., Achterberg, Sefanja, Oostdijk, Evelien, Reidinga, Auke C., Festen-Spanjer, Barbara, Brunnekreef, Gert B., Cornet, Alexander D., van den Tempel, Walter, Boelens, Age D., Koetsier, Peter, Lens, Judith, Faber, Harald J., Karakus, A., Entjes, Robert, de Jong, Paul, Rettig, Thijs C.D., Reuland, M. C., Arbous, Sesmu, Fleuren, Lucas M., Dam, Tariq A., Thoral, Patrick J., Lalisang, Robbert C.A., Tonutti, Michele, de Bruin, Daan P., Elbers, Paul W.G., de Keizer, Nicolette F., Vagliano, Iacopo, Schut, Martijn C., Abu-Hanna, Ameen, Dongelmans, Dave A., de Lange, Dylan W., Gommers, Diederik, Cremer, Olaf L., Bosman, Rob J., Rigter, Sander, Wils, Evert Jan, Frenzel, Tim, de Jong, Remko, Peters, Marco A.A., Kamps, Marlijn J.A., Ramnarain, Dharmanand, Nowitzky, Ralph, Nooteboom, Fleur G.C.A., de Ruijter, Wouter, Urlings-Strop, Louise C., Smit, Ellen G.M., Mehagnoul-Schipper, D. Jannet, Dormans, Tom, de Jager, Cornelis P.C., Hendriks, Stefaan H.A., Achterberg, Sefanja, Oostdijk, Evelien, Reidinga, Auke C., Festen-Spanjer, Barbara, Brunnekreef, Gert B., Cornet, Alexander D., van den Tempel, Walter, Boelens, Age D., Koetsier, Peter, Lens, Judith, Faber, Harald J., Karakus, A., Entjes, Robert, de Jong, Paul, Rettig, Thijs C.D., Reuland, M. C., Arbous, Sesmu, Fleuren, Lucas M., Dam, Tariq A., Thoral, Patrick J., Lalisang, Robbert C.A., Tonutti, Michele, de Bruin, Daan P., Elbers, Paul W.G., and de Keizer, Nicolette F.
- Abstract
Purpose: To assess, validate and compare the predictive performance of models for in-hospital mortality of COVID-19 patients admitted to the intensive care unit (ICU) over two different waves of infections. Our models were built with high-granular Electronic Health Records (EHR) data versus less-granular registry data. Methods: Observational study of all COVID-19 patients admitted to 19 Dutch ICUs participating in both the national quality registry National Intensive Care Evaluation (NICE) and the EHR-based Dutch Data Warehouse (hereafter EHR). Multiple models were developed on data from the first 24 h of ICU admissions from February to June 2020 (first COVID-19 wave) and validated on prospective patients admitted to the same ICUs between July and December 2020 (second COVID-19 wave). We assessed model discrimination, calibration, and the degree of relatedness between development and validation population. Coefficients were used to identify relevant risk factors. Results: A total of 1533 patients from the EHR and 1563 from the registry were included. With high granular EHR data, the average AUROC was 0.69 (standard deviation of 0.05) for the internal validation, and the AUROC was 0.75 for the temporal validation. The registry model achieved an average AUROC of 0.76 (standard deviation of 0.05) in the internal validation and 0.77 in the temporal validation. In the EHR data, age, and respiratory-system related variables were the most important risk factors identified. In the NICE registry data, age and chronic respiratory insufficiency were the most important risk factors. Conclusion: In our study, prognostic models built on less-granular but readily-available registry data had similar performance to models built on high-granular EHR data and showed similar transportability to a prospective COVID-19 population. Future research is needed to verify whether this finding can be confirmed for upcoming waves.
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- 2022
14. Rapid Evaluation of Coronavirus Illness Severity (RECOILS) in intensive care:Development and validation of a prognostic tool for in-hospital mortality
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Plečko, Drago, Bennett, Nicolas, Mårtensson, Johan, Dam, Tariq A., Entjes, Robert, Rettig, Thijs C.D., Dongelmans, Dave A., Boelens, Age D., Rigter, Sander, Hendriks, Stefaan H.A., de Jong, Remko, Kamps, Marlijn J.A., Peters, Marco, Karakus, Attila, Gommers, Diederik, Ramnarain, Dharmanand, Wils, Evert Jan, Achterberg, Sefanja, Nowitzky, Ralph, van den Tempel, Walter, de Jager, Cornelis P.C., Nooteboom, Fleur G.C.A., Oostdijk, Evelien, Koetsier, Peter, Cornet, Alexander D., Reidinga, Auke C., de Ruijter, Wouter, Bosman, Rob J., Frenzel, Tim, Urlings-Strop, Louise C., de Jong, Paul, Smit, Ellen G.M., Cremer, Olaf L., Mehagnoul-Schipper, D. Jannet, Faber, Harald J., Lens, Judith, Brunnekreef, Gert B., Festen-Spanjer, Barbara, Dormans, Tom, de Bruin, Daan P., Lalisang, Robbert C.A., Vonk, Sebastiaan J.J., Haan, Martin E., Fleuren, Lucas M., Thoral, Patrick J., Elbers, Paul W.G., Bellomo, Rinaldo, Plečko, Drago, Bennett, Nicolas, Mårtensson, Johan, Dam, Tariq A., Entjes, Robert, Rettig, Thijs C.D., Dongelmans, Dave A., Boelens, Age D., Rigter, Sander, Hendriks, Stefaan H.A., de Jong, Remko, Kamps, Marlijn J.A., Peters, Marco, Karakus, Attila, Gommers, Diederik, Ramnarain, Dharmanand, Wils, Evert Jan, Achterberg, Sefanja, Nowitzky, Ralph, van den Tempel, Walter, de Jager, Cornelis P.C., Nooteboom, Fleur G.C.A., Oostdijk, Evelien, Koetsier, Peter, Cornet, Alexander D., Reidinga, Auke C., de Ruijter, Wouter, Bosman, Rob J., Frenzel, Tim, Urlings-Strop, Louise C., de Jong, Paul, Smit, Ellen G.M., Cremer, Olaf L., Mehagnoul-Schipper, D. Jannet, Faber, Harald J., Lens, Judith, Brunnekreef, Gert B., Festen-Spanjer, Barbara, Dormans, Tom, de Bruin, Daan P., Lalisang, Robbert C.A., Vonk, Sebastiaan J.J., Haan, Martin E., Fleuren, Lucas M., Thoral, Patrick J., Elbers, Paul W.G., and Bellomo, Rinaldo
- Abstract
Background: The prediction of in-hospital mortality for ICU patients with COVID-19 is fundamental to treatment and resource allocation. The main purpose was to develop an easily implemented score for such prediction. Methods: This was an observational, multicenter, development, and validation study on a national critical care dataset of COVID-19 patients. A systematic literature review was performed to determine variables possibly important for COVID-19 mortality prediction. Using a logistic multivariable model with a LASSO penalty, we developed the Rapid Evaluation of Coronavirus Illness Severity (RECOILS) score and compared its performance against published scores. Results: Our development (validation) cohort consisted of 1480 (937) adult patients from 14 (11) Dutch ICUs admitted between March 2020 and April 2021. Median age was 65 (65) years, 31% (26%) died in hospital, 74% (72%) were males, average length of ICU stay was 7.83 (10.25) days and average length of hospital stay was 15.90 (19.92) days. Age, platelets, PaO2/FiO2 ratio, pH, blood urea nitrogen, temperature, PaCO2, Glasgow Coma Scale (GCS) score measured within +/−24 h of ICU admission were used to develop the score. The AUROC of RECOILS score was 0.75 (CI 0.71–0.78) which was higher than that of any previously reported predictive scores (0.68 [CI 0.64–0.71], 0.61 [CI 0.58–0.66], 0.67 [CI 0.63–0.70], 0.70 [CI 0.67–0.74] for ISARIC 4C Mortality Score, SOFA, SAPS-III, and age, respectively). Conclusions: Using a large dataset from multiple Dutch ICUs, we developed a predictive score for mortality of COVID-19 patients admitted to ICU, which outperformed other predictive scores reported so far.
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- 2022
15. Incidence, Risk Factors and Outcome of Suspected Central Venous Catheter-related Infections in Critically Ill COVID-19 Patients: A Multicenter Retrospective Cohort Study
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Medische Staf Intensive Care, Infection & Immunity, MMB Medische Staf, Exterkate, Lotte, van Tienhoven, Arne J, Haaksma, Mark E, Heldeweg, Micah L A, Fleuren, Lucas, Thoral, Patrick, Dam, Tariq A, Heunks, Leo M A, Gommers, Diederik, Cremer, Olaf L, Bosman, Rob J, Rigter, Sander, Wils, Evert-Jan, Frenzel, Tim, Vlaar, Alexander P, Dongelmans, Dave A, de Jong, Remko, Peters, Marco, Kamps, Marlijn J A, Ramnarain, Dharmanand, Nowitzky, Ralph, Nooteboom, Fleur G C A, de Ruijter, Wouter, Urlings-Strop, Louise C, Smit, Ellen G M, Mehagnoul-Schipper, D Jannet, Dormans, Tom, de Jager, Cornelis P C, Hendriks, Stefaan H A, Achterberg, Sefanja, Oostdijk, Evelien, Reidinga, Auke C, Festen-Spanjer, Barbara, Brunnekreef, Gert B, Cornet, Alexander D, van den Tempel, Walter, Boelens, Age D, Koetsier, Peter, Lens, Judith, Faber, Harald J, Karakus, A, Entjes, Robert, de Jong, Paul, Rettig, Thijs C D, Arbous, Sesmu, Vonk, Bas, Machado, Tomas, Girbes, Armand R J, Sieswerda, Elske, Elbers, Paul W G, Tuinman, Pieter R, Medische Staf Intensive Care, Infection & Immunity, MMB Medische Staf, Exterkate, Lotte, van Tienhoven, Arne J, Haaksma, Mark E, Heldeweg, Micah L A, Fleuren, Lucas, Thoral, Patrick, Dam, Tariq A, Heunks, Leo M A, Gommers, Diederik, Cremer, Olaf L, Bosman, Rob J, Rigter, Sander, Wils, Evert-Jan, Frenzel, Tim, Vlaar, Alexander P, Dongelmans, Dave A, de Jong, Remko, Peters, Marco, Kamps, Marlijn J A, Ramnarain, Dharmanand, Nowitzky, Ralph, Nooteboom, Fleur G C A, de Ruijter, Wouter, Urlings-Strop, Louise C, Smit, Ellen G M, Mehagnoul-Schipper, D Jannet, Dormans, Tom, de Jager, Cornelis P C, Hendriks, Stefaan H A, Achterberg, Sefanja, Oostdijk, Evelien, Reidinga, Auke C, Festen-Spanjer, Barbara, Brunnekreef, Gert B, Cornet, Alexander D, van den Tempel, Walter, Boelens, Age D, Koetsier, Peter, Lens, Judith, Faber, Harald J, Karakus, A, Entjes, Robert, de Jong, Paul, Rettig, Thijs C D, Arbous, Sesmu, Vonk, Bas, Machado, Tomas, Girbes, Armand R J, Sieswerda, Elske, Elbers, Paul W G, and Tuinman, Pieter R
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- 2022
16. Dynamic prediction of mortality in COVID-19 patients in the intensive care unit: A retrospective multi-center cohort study
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Arts-assistenten DV&B, Medische Staf Intensive Care, Infection & Immunity, Smit, J M, Krijthe, J H, Endeman, H, Tintu, A N, de Rijke, Y B, Gommers, D A M P J, Cremer, O L, Bosman, R J, Rigter, S, Wils, E-J, Frenzel, T, Dongelmans, D A, De Jong, R, Peters, M A A, Kamps, M J A, Ramnarain, D, Nowitzky, R, Nooteboom, F G C A, De Ruijter, W, Urlings-Strop, L C, Smit, E G M, Mehagnoul-Schipper, D J, Dormans, T, De Jager, C P C, Hendriks, S H A, Achterberg, S, Oostdijk, E, Reidinga, A C, Festen-Spanjer, B, Brunnekreef, G B, Cornet, A D, Van den Tempel, W, Boelens, A D, Koetsier, P, Lens, J A, Faber, H J, Karakus, A, Entjes, R, De Jong, P, Rettig, T C D, Arbous, M S, Lalisang, R C A, Tonutti, M, De Bruin, D P, Elbers, P W G, Van Bommel, J, Reinders, M J T, Arts-assistenten DV&B, Medische Staf Intensive Care, Infection & Immunity, Smit, J M, Krijthe, J H, Endeman, H, Tintu, A N, de Rijke, Y B, Gommers, D A M P J, Cremer, O L, Bosman, R J, Rigter, S, Wils, E-J, Frenzel, T, Dongelmans, D A, De Jong, R, Peters, M A A, Kamps, M J A, Ramnarain, D, Nowitzky, R, Nooteboom, F G C A, De Ruijter, W, Urlings-Strop, L C, Smit, E G M, Mehagnoul-Schipper, D J, Dormans, T, De Jager, C P C, Hendriks, S H A, Achterberg, S, Oostdijk, E, Reidinga, A C, Festen-Spanjer, B, Brunnekreef, G B, Cornet, A D, Van den Tempel, W, Boelens, A D, Koetsier, P, Lens, J A, Faber, H J, Karakus, A, Entjes, R, De Jong, P, Rettig, T C D, Arbous, M S, Lalisang, R C A, Tonutti, M, De Bruin, D P, Elbers, P W G, Van Bommel, J, and Reinders, M J T
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- 2022
17. Rapid evaluation of Coronavirus Illness Severity (RECOILS) in intensive care: Development and validation of a prognostic tool for in-hospital mortality
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Medische Staf Intensive Care, Infection & Immunity, Plečko, Drago, Bennett, Nicolas, Mårtensson, Johan, Dam, Tariq A, Entjes, Robert, Rettig, Thijs C D, Dongelmans, Dave A, Boelens, Age D, Rigter, Sander, Hendriks, Stefaan H A, de Jong, Remko, Kamps, Marlijn J A, Peters, Marco, Karakus, Attila, Gommers, Diederik, Ramnarain, Dharmanand, Wils, Evert-Jan, Achterberg, Sefanja, Nowitzky, Ralph, van den Tempel, Walter, de Jager, Cornelis P C, Nooteboom, Fleur G C A, Oostdijk, Evelien, Koetsier, Peter, Cornet, Alexander D, Reidinga, Auke C, de Ruijter, Wouter, Bosman, Rob J, Frenzel, Tim, Urlings-Strop, Louise C, de Jong, Paul, Smit, Ellen G M, Cremer, Olaf L, Mehagnoul-Schipper, D Jannet, Faber, Harald J, Lens, Judith, Brunnekreef, Gert B, Festen-Spanjer, Barbara, Dormans, Tom, de Bruin, Daan P, Lalisang, Robbert C A, Vonk, Sebastiaan J J, Haan, Martin E, Fleuren, Lucas M, Thoral, Patrick J, Elbers, Paul W G, Bellomo, Rinaldo, Medische Staf Intensive Care, Infection & Immunity, Plečko, Drago, Bennett, Nicolas, Mårtensson, Johan, Dam, Tariq A, Entjes, Robert, Rettig, Thijs C D, Dongelmans, Dave A, Boelens, Age D, Rigter, Sander, Hendriks, Stefaan H A, de Jong, Remko, Kamps, Marlijn J A, Peters, Marco, Karakus, Attila, Gommers, Diederik, Ramnarain, Dharmanand, Wils, Evert-Jan, Achterberg, Sefanja, Nowitzky, Ralph, van den Tempel, Walter, de Jager, Cornelis P C, Nooteboom, Fleur G C A, Oostdijk, Evelien, Koetsier, Peter, Cornet, Alexander D, Reidinga, Auke C, de Ruijter, Wouter, Bosman, Rob J, Frenzel, Tim, Urlings-Strop, Louise C, de Jong, Paul, Smit, Ellen G M, Cremer, Olaf L, Mehagnoul-Schipper, D Jannet, Faber, Harald J, Lens, Judith, Brunnekreef, Gert B, Festen-Spanjer, Barbara, Dormans, Tom, de Bruin, Daan P, Lalisang, Robbert C A, Vonk, Sebastiaan J J, Haan, Martin E, Fleuren, Lucas M, Thoral, Patrick J, Elbers, Paul W G, and Bellomo, Rinaldo
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- 2022
18. Additional file 1 of Predicting responders to prone positioning in mechanically ventilated patients with COVID-19 using machine learning
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Dam, Tariq A., Roggeveen, Luca F., van Diggelen, Fuda, Fleuren, Lucas M., Jagesar, Ameet R., Otten, Martijn, de Vries, Heder J., Gommers, Diederik, Cremer, Olaf L., Bosman, Rob J., Rigter, Sander, Wils, Evert-Jan, Frenzel, Tim, Dongelmans, Dave A., de Jong, Remko, Peters, Marco A. A., Kamps, Marlijn J. A., Ramnarain, Dharmanand, Nowitzky, Ralph, Nooteboom, Fleur G. C. A., de Ruijter, Wouter, Urlings-Strop, Louise C., Smit, Ellen G. M., Mehagnoul-Schipper, D. Jannet, Dormans, Tom, de Jager, Cornelis P. C., Hendriks, Stefaan H. A., Achterberg, Sefanja, Oostdijk, Evelien, Reidinga, Auke C., Festen-Spanjer, Barbara, Brunnekreef, Gert B., Cornet, Alexander D., van den Tempel, Walter, Boelens, Age D., Koetsier, Peter, Lens, Judith, Faber, Harald J., Karakus, A., Entjes, Robert, de Jong, Paul, Rettig, Thijs C. D., Arbous, Sesmu, Vonk, Sebastiaan J. J., Machado, Tomas, Herter, Willem E., de Grooth, Harm-Jan, Thoral, Patrick J., Girbes, Armand R. J., Hoogendoorn, Mark, and Elbers, Paul W. G.
- Abstract
Additional file 1: Table S1. Parameter bounds. Table S2. Parameter categories. Table S3. Feature derivations. Table S4. Included features. Table S5. Hyper parameter optimization. Table S6. Feature Importance Metrics. Table S7. Patient characteristics. Table S8. ROC AUC score. Table S9. F1-score. Table S10. Sensitivity Analysis Minimal Prone Duration. Table S11. Sensitivity Analysis Cut-off Values. Table S12. Ranked Feature Importance. Table S13. Feature Importance Values. Figure S1. PaO2/FiO2 ratio difference in mmHg over time after starting prone position. Prone positions which continued for longer than 24 hours were filtered out.
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- 2022
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19. Risk factors for adverse outcomes during mechanical ventilation of 1152 COVID-19 patients: a multicenter machine learning study with highly granular data from the Dutch Data Warehouse
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Fleuren, Lucas M., Tonutti, Michele, de Bruin, Daan P., Lalisang, Robbert C.A., Dam, Tariq A., Gommers, Diederik, Cremer, Olaf L., Bosman, Rob J., Vonk, Sebastiaan J.J., Fornasa, Mattia, Machado, Tomas, van der Meer, Nardo J.M., Rigter, Sander, Wils, Evert Jan, Frenzel, Tim, Dongelmans, Dave A., de Jong, Remko, Peters, Marco, Kamps, Marlijn J.A., Ramnarain, Dharmanand, Nowitzky, Ralph, Nooteboom, Fleur G.C.A., de Ruijter, Wouter, Urlings-Strop, Louise C., Smit, Ellen G.M., Mehagnoul-Schipper, D. Jannet, Dormans, Tom, de Jager, Cornelis P.C., Hendriks, Stefaan H.A., Oostdijk, Evelien, Reidinga, Auke C., Festen-Spanjer, Barbara, Brunnekreef, Gert, Cornet, Alexander D., van den Tempel, Walter, Boelens, Age D., Koetsier, Peter, Lens, Judith, Achterberg, Sefanja, Faber, Harald J., Karakus, A., Beukema, Menno, Entjes, Robert, de Jong, Paul, Houwert, Taco, Hovenkamp, Hidde, Noorduijn Londono, Roberto, Quintarelli, Davide, Scholtemeijer, Martijn G., de Beer, Aletta A., Cinà, Giovanni, Beudel, Martijn, de Keizer, Nicolet F., Hoogendoorn, Mark, Girbes, Armand R.J., Herter, Willem E., Elbers, Paul W.G., Thoral, Patrick J., Rettig, Thijs C.D., Reuland, M. C., van Manen, Laura, Montenij, Leon, van Bommel, Jasper, van den Berg, Roy, van Geest, Ellen, Hana, Anisa, Boersma, W. G., van den Bogaard, B., Pickkers, Peter, van der Heiden, Pim, van Gemeren, Claudia C.W., Meinders, Arend Jan, de Bruin, Martha, Rademaker, Emma, van Osch, Frits H.M., de Kruif, Martijn, Schroten, Nicolas, Arnold, Klaas Sierk, Fijen, J. W., van Koesveld, Jacomar J.M., Simons, Koen S., Labout, Joost, van de Gaauw, Bart, Kuiper, Michael, Beishuizen, Albertus, Geutjes, Dennis, Lutisan, Johan, Grady, Bart P.X., van den Akker, Remko, Simons, Bram, Rijkeboer, A. A., Arbous, Sesmu, Aries, Marcel, van den Oever, Niels C.Gritters, van Tellingen, Martijn, Dijkstra, Annemieke, van Raalte, Rutger, Roggeveen, Luca, van Diggelen, Fuda, Hassouni, Ali el, Guzman, David Romero, Bhulai, Sandjai, Ouweneel, Dagmar, Driessen, Ronald, Peppink, Jan, de Grooth, H. J., Zijlstra, G. J., van Tienhoven, A. J., van der Heiden, Evelien, Spijkstra, Jan Jaap, van der Spoel, Hans, de Man, Angelique, Klausch, Thomas, de Vries, Heder, de Neree tot Babberich, Michael, Thijssens, Olivier, Wagemakers, Lot, van der Pol, Hilde G.A., Hendriks, Tom, Berend, Julie, Silva, Virginia Ceni, Kullberg, Bob, Heunks, Leo, Juffermans, Nicole, Slooter, Arjan, Intensive care medicine, ACS - Diabetes & metabolism, ACS - Microcirculation, Amsterdam Cardiovascular Sciences, Neurology, AII - Infectious diseases, AII - Cancer immunology, CCA - Cancer biology and immunology, AII - Inflammatory diseases, Epidemiology and Data Science, APH - Methodology, ACS - Pulmonary hypertension & thrombosis, Intensive Care Medicine, APH - Quality of Care, Medical Informatics, Graduate School, Nephrology, Cardiology, Gastroenterology and Hepatology, Amsterdam Gastroenterology Endocrinology Metabolism, APH - Digital Health, Artificial intelligence, Network Institute, Computational Intelligence, Artificial Intelligence (section level), Mathematics, Intensive Care, Epidemiologie, RS: NUTRIM - R3 - Respiratory & Age-related Health, RS: MHeNs - R1 - Cognitive Neuropsychiatry and Clinical Neuroscience, MUMC+: MA Medische Staf IC (9), and Internal medicine
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Icu patients ,Coronavirus disease 2019 (COVID-19) ,Adverse outcomes ,medicine.medical_treatment ,Critical Care and Intensive Care Medicine ,Machine learning ,computer.software_genre ,law.invention ,03 medical and health sciences ,0302 clinical medicine ,SDG 3 - Good Health and Well-being ,law ,SCORE ,medicine ,030212 general & internal medicine ,Risk factor ,Research Articles ,Mechanical ventilation ,business.industry ,RC86-88.9 ,Other Research Radboud Institute for Health Sciences [Radboudumc 0] ,COVID-19 ,030208 emergency & critical care medicine ,Medical emergencies. Critical care. Intensive care. First aid ,Intensive care unit ,Data warehouse ,Data extraction ,Mortality prediction ,Risk factors ,Artificial intelligence ,business ,computer - Abstract
Background The identification of risk factors for adverse outcomes and prolonged intensive care unit (ICU) stay in COVID-19 patients is essential for prognostication, determining treatment intensity, and resource allocation. Previous studies have determined risk factors on admission only, and included a limited number of predictors. Therefore, using data from the highly granular and multicenter Dutch Data Warehouse, we developed machine learning models to identify risk factors for ICU mortality, ventilator-free days and ICU-free days during the course of invasive mechanical ventilation (IMV) in COVID-19 patients. Methods The DDW is a growing electronic health record database of critically ill COVID-19 patients in the Netherlands. All adult ICU patients on IMV were eligible for inclusion. Transfers, patients admitted for less than 24 h, and patients still admitted at time of data extraction were excluded. Predictors were selected based on the literature, and included medication dosage and fluid balance. Multiple algorithms were trained and validated on up to three sets of observations per patient on day 1, 7, and 14 using fivefold nested cross-validation, keeping observations from an individual patient in the same split. Results A total of 1152 patients were included in the model. XGBoost models performed best for all outcomes and were used to calculate predictor importance. Using Shapley additive explanations (SHAP), age was the most important demographic risk factor for the outcomes upon start of IMV and throughout its course. The relative probability of death across age values is visualized in Partial Dependence Plots (PDPs), with an increase starting at 54 years. Besides age, acidaemia, low P/F-ratios and high driving pressures demonstrated a higher probability of death. The PDP for driving pressure showed a relative probability increase starting at 12 cmH2O. Conclusion Age is the most important demographic risk factor of ICU mortality, ICU-free days and ventilator-free days throughout the course of invasive mechanical ventilation in critically ill COVID-19 patients. pH, P/F ratio, and driving pressure should be monitored closely over the course of mechanical ventilation as risk factors predictive of these outcomes.
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- 2021
20. Evolution of Clinical Phenotypes of COVID-19 Patients During Intensive Care Treatment: An Unsupervised Machine Learning Analysis
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Siepel, Sander, primary, Dam, Tariq A., additional, Fleuren, Lucas M., additional, Gommers, Diederik, additional, Cremer, Olaf L., additional, Bosman, Rob J., additional, Rigter, Sander, additional, Wils, Evert-Jan, additional, Frenzel, Tim, additional, Dongelmans, Dave A., additional, de Jong, Remko, additional, Peters, Marco, additional, Kamps, Marlijn J.A, additional, Ramnarain, Dharmanand, additional, Nowitzky, Ralph, additional, Nooteboom, Fleur G.C.A., additional, de Ruijter, Wouter, additional, Urlings-Strop, Louise C., additional, Smit, Ellen G.M., additional, Mehagnoul-Schipper, D. Jannet, additional, Dormans, Tom, additional, de Jager, Cornelis P.C., additional, Hendriks, Stefaan H.A., additional, Achterberg, Sefanja, additional, Oostdijk, Evelien, additional, Reidinga, Auke C., additional, Festen-Spanjer, Barbara, additional, Brunnekreef, Gert B., additional, Cornet, Alexander D., additional, Tempel, Walter van den, additional, Boelens, Age D., additional, Koetsier, Peter, additional, Lens, Judith, additional, Faber, Harald J., additional, Karakus, A., additional, Entjes, Robert, additional, de Jong, Paul, additional, Rettig, Thijs C.D., additional, Arbous, Sesmu, additional, Vonk, Sebastiaan J.J., additional, Machado, Tomas, additional, Herter, Willem E., additional, Girbes, Armand R.J., additional, Hoogendoorn, Mark, additional, Thoral, Patrick J., additional, Elbers, Paul W.G., additional, and Bennis, Frank C., additional
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- 2022
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21. Rapid and reliable ratiometric fluorescence detection of nitro explosive 2,4,6-trinitrophenol based on a near infrared (NIR) luminescent Zn(II)-Nd(III) nanoring.
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Meng Y, Cheng Y, Yang X, Lv X, Huang X, and Schipper D
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Rapid and quantitative detection of 2,4,6-trinitrophenol (TNP) is very crucial for homeland security, military application, and environment protection. Herein, a nine-metal Zn(II)-Nd(III) nanoring 1 with a diameter of 2.3 nm was constructed by the use of a long-chain Schiff base ligand, which shows ratiometric fluorescence response to TNP with high selectivity and sensitivity. The fluorescence sensing behavior of 1 to TNP is expressed by a first-order equation I
1060nm /I560nm = -0.0128*[TNP] + 0.9723, which can be used to quantitatively analyze TNP concentrations in solution. The limits of detection (LODs) to TNP based on the ligand-centered (LC) and Nd(III) emissions of 1 are 5.93 μM and 3.18 μM, respectively. The fluorescence response mechanism to TNP is attributed to the competitive absorption effect and photoinduced electron transfer (PET). The luminescence quenching of 1 is dominated by static process., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)- Published
- 2024
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22. Rapid and reliable ratiometric fluorescence detection of isoquercitrin based on a high-nuclearity Zn(II)-Nd(III) nanomolecular sensor.
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Zhao J, Yang X, Leng X, Wang C, and Schipper D
- Abstract
Rapid and quantitative detection of isoquercitrin (Isq) has been attracting much attention due to its outstanding pharmacological and physiological activities. Herein, an interesting 48-metal Zn(II)-Nd(III) nanocluster (1, molecular sizes 1.3 × 2.8 × 3.1 nm) with salen-type Schiff base ligand was constructed as molecular sensor for the luminescence detection of Isq. 1 exhibits visible ligand-centered emission and NIR luminescence of Nd(III), and shows ratiometric fluorescence response to Isq with high sensitivity even in the presence of other interferences. The fluorescence sensing behavior can be expressed by a second-order equation I
1060nm /I480nm = A*[Isq]2 + B*[Isq] + C, which is used to quantitatively analyze the Isq concentrations in DMF and FCS. The LODs to Isq for the ligand-centered and lanthanide emissions of 1 in DMF are 0.21 μM and 0.11 nM, respectively. The quenching of the ligand-centered emission of 1 caused by Isq is attributed to the competitive absorption of light energy and "inner effect", while, the luminescence enhancement is due to the "antenna effect"., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)- Published
- 2024
- Full Text
- View/download PDF
23. Ratiometric Fluorescence Detection of Levofloxacin Based on a Cube-like Zn(II)-Eu(III) Nanocluster: Functionalized Sodium Alginate Film for the Detection in Serum and Medicine.
- Author
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Yu H, Lin J, Yang X, Wang C, Schipper D, and Yang K
- Subjects
- Spectrometry, Fluorescence, Animals, Humans, Cattle, Tablets, Fluorescent Dyes chemistry, Alginates chemistry, Zinc chemistry, Zinc blood, Levofloxacin blood, Levofloxacin analysis, Europium chemistry
- Abstract
A cube-like Zn(II)-Eu(III) nanocluster 1 (molecular sizes: 1.8 × 2.0 × 2.0 nm) was constructed by the use of a new long-chain Schiff base ligand. It shows a ratiometric fluorescence response to levofloxacin (LFX) with high sensitivity and selectivity, which can be expressed as I
615 nm / I550 nm = A *[LFX]2 + B *[LFX] + C . It is used to quantitatively detect the LFX concentrations in fetal calf serum (FCS) and tablets sold in pharmacy. Filter paper strips bearing 1 can be used to qualitatively detect LFX by a color change to red under a UV lamp. 1 and its hybrid with sodium alginate (SA), 1 @SA, display potential applications in the qualitative detection of LFX in FCS and the medicine. The limit of detection of 1 to LFX is as low as 2.1 × 10-2 nM.- Published
- 2024
- Full Text
- View/download PDF
24. A 20-Metal Zn(II)-Cd(II)-Eu(III) Nanocluster with Qualitative and Quantitative Luminescence Detection of Meloxicam (an Anti-Inflammatory Drug).
- Author
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Lin J, Yang X, Chen Y, Yang K, and Schipper D
- Subjects
- Luminescent Measurements, Luminescence, Nanostructures chemistry, Limit of Detection, Meloxicam analysis, Zinc chemistry, Zinc analysis, Europium chemistry, Anti-Inflammatory Agents, Non-Steroidal analysis, Anti-Inflammatory Agents, Non-Steroidal chemistry
- Abstract
Meloxicam (MLX) is a novel nonsteroidal anti-inflammatory drug, but on the other hand, it has become one of the common microcontaminants in surface waters and sewage. Herein, we report the preparation of a ternary-metal Zn(II)-Cd(II)-Eu(III) nanocluster 1 for the response of MLX through the enhancement of lanthanide luminescence. The luminescence sensing behavior of 1 is expressed by the equation I
615nm = 3060 × [MLX] + 46,604, which can be used in the quantitative analysis of MLX concentrations in meloxicam dispersible tablets. Filter paper strips bearing 1 can be used to qualitatively detect MLX by a color change to red under a UV lamp. The luminescence response time is no more than five s, and the detection limit is as low as 2.31 × 10-2 nM.- Published
- 2024
- Full Text
- View/download PDF
25. Construction of a Zn(II)-Eu(III) Nanoring with Temperature-Dependent Luminescence for the Qualitative and Quantitative Detection of Neopterin as an Inflammatory Marker.
- Author
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Meng Y, Cheng Y, Yang X, Wang C, Yang K, and Schipper D
- Subjects
- Humans, Luminescence, Luminescent Measurements, Biomarkers analysis, Biomarkers blood, Limit of Detection, Animals, Zinc chemistry, Zinc analysis, Neopterin analysis, Neopterin urine, Neopterin blood, Europium chemistry, Temperature, Nanostructures chemistry
- Abstract
A nine-metal Zn(II)-Eu(III) nanoring 1 with a diameter of about 2.3 nm was constructed by the use of a long-chain Schiff base ligand. It shows a luminescence response to neopterin (Neo) through the enhancement of lanthanide emission with high selectivity and sensitivity, which can be used to quantitatively analyze the concentrations of Neo in fetal calf serum and urine. The luminescence sensing of 1 to Neo is temperature-dependent, and it displays more obvious response behavior at lower temperatures. Filter paper strips bearing 1 can be used to qualitatively detect Neo by the color change from chartreuse to red under a UV lamp. The limit of detection is as low as 3.77 × 10
-2 nM.- Published
- 2024
- Full Text
- View/download PDF
26. Solvent effects on the intramolecular charge transfer excited state of 3CzClIPN: a broadband transient absorption study.
- Author
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Zheng R, Cheng M, Ma R, Schipper D, Pichugin K, and Sciaini G
- Abstract
The prediction of solvent properties using molecular probes often relies on correlating steady-state absorption and fluorescence measurements, as well as determining absorption maxima and/or Stokes shifts. In this study, we employ femtosecond broadband transient absorption (fs-bb-TA) spectroscopy to investigate the spectroscopic behaviour of the intramolecular charge transfer (ICT) excited state of 3CzClIPN (2,4,6-tri(9 H -carbazol-9-yl)-5-chloroisophthalonitrile), a representative ICT organic molecule, in both aromatic and non-aromatic solvents. Unlike observations in non-aromatic media, fs-bb-TA spectra of 3CzClIPN in aromatic solvents exhibit enhanced spectral broadening that strongly correlates with the solvent's polarity. We hypothesise that this spectral broadening originates from a wider configurational energy landscape experienced by the positively charged carbazole Cz
+ group, owing to the larger size and, consequently, reduced solvation effectiveness of aromatic solvent molecules.- Published
- 2024
- Full Text
- View/download PDF
27. Mutation of PUB17 in tomato leads to reduced susceptibility to necrotrophic fungi.
- Author
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Ramirez Gaona M, van Tuinen A, Schipper D, Kano A, Wolters PJ, Visser RGF, van Kan JAL, Wolters AA, and Bai Y
- Subjects
- Plant Growth Regulators, Fungi, Mutation genetics, Plant Diseases genetics, Plant Diseases microbiology, Gene Expression Regulation, Plant, Solanum lycopersicum genetics
- Published
- 2023
- Full Text
- View/download PDF
28. SARS-CoV-2 ORF8 accessory protein is a virulence factor.
- Author
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Bello-Perez M, Hurtado-Tamayo J, Mykytyn AZ, Lamers MM, Requena-Platek R, Schipper D, Muñoz-Santos D, Ripoll-Gómez J, Esteban A, Sánchez-Cordón PJ, Enjuanes L, Haagmans BL, and Sola I
- Subjects
- Mice, Animals, Humans, Virulence Factors genetics, Respiratory System, Mice, Transgenic, SARS-CoV-2 genetics, COVID-19
- Abstract
Importance: The relevance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ORF8 in the pathogenesis of COVID-19 is unclear. Virus natural isolates with deletions in ORF8 were associated with wild milder disease, suggesting that ORF8 might contribute to SARS-CoV-2 virulence. This manuscript shows that ORF8 is involved in inflammation and in the activation of macrophages in two experimental systems: humanized K18-hACE2 transgenic mice and organoid-derived human airway cells. These results identify ORF8 protein as a potential target for COVID-19 therapies., Competing Interests: The authors declare no conflict of interest.
- Published
- 2023
- Full Text
- View/download PDF
29. SARS-CoV-2 Omicron entry is type II transmembrane serine protease-mediated in human airway and intestinal organoid models.
- Author
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Mykytyn AZ, Breugem TI, Geurts MH, Beumer J, Schipper D, van Acker R, van den Doel PB, van Royen ME, Zhang J, Clevers H, Haagmans BL, and Lamers MM
- Subjects
- Humans, Antiviral Agents, COVID-19 virology, SARS-CoV-2 physiology, Spike Glycoprotein, Coronavirus genetics, Spike Glycoprotein, Coronavirus metabolism, Virus Internalization, Serine Proteases metabolism
- Abstract
SARS-CoV-2 can enter cells after its spike protein is cleaved by either type II transmembrane serine proteases (TTSPs), like TMPRSS2, or cathepsins. It is now widely accepted that the Omicron variant uses TMPRSS2 less efficiently and instead enters cells via cathepsins, but these findings have yet to be verified in more relevant cell models. Although we could confirm efficient cathepsin-mediated entry for Omicron in a monkey kidney cell line, experiments with protease inhibitors showed that Omicron (BA.1 and XBB1.5) did not use cathepsins for entry into human airway organoids and instead utilized TTSPs. Likewise, CRISPR-edited intestinal organoids showed that entry of Omicron BA.1 relied on the expression of the serine protease TMPRSS2 but not cathepsin L or B. Together, these data force us to rethink the concept that Omicron has adapted to cathepsin-mediated entry and indicate that TTSP inhibitors should not be dismissed as prophylactic or therapeutic antiviral strategy against SARS-CoV-2. IMPORTANCE Coronavirus entry relies on host proteases that activate the viral fusion protein, spike. These proteases determine the viral entry route, tropism, host range, and can be attractive drug targets. Whereas earlier studies using cell lines suggested that the Omicron variant of SARS-CoV-2 has changed its protease usage, from cell surface type II transmembrane serine proteases (TTSPs) to endosomal cathepsins, we report that this is not the case in human airway and intestinal organoid models, suggesting that host TTSP inhibition is still a viable prophylactic or therapeutic antiviral strategy against current SARS-CoV-2 variants and highlighting the importance of relevant human in vitro cell models., Competing Interests: H.C. is inventor on patents held by the Royal Netherlands Academy of Arts and Sciences that cover organoid technology: PCT/NL2008/050543, WO2009/022907; PCT/NL2010/000017, WO2010/090513; PCT/IB2011/002167, WO2012/014076; PCT/IB2012/052950, WO2012/168930; PCT/EP2015/060815, WO2015/173425; PCT/EP2015/077990, WO2016/083613; PCT/EP2015/077988, WO2016/083612; PCT/EP2017/054797, WO2017/149025; PCT/EP2017/065101, WO2017/220586; PCT/EP2018/086716, and GB1819224.5. H.C.'s full disclosure is given at https://www.uu.nl/staff/JCClevers/.
- Published
- 2023
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30. A Near-Infrared Luminescent 11-Metal Cd(II)-Nd(III) Nanocluster for the Rapid Excitation Wavelength-Dependent Detection of Rutin.
- Author
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Liu Y, Zhao J, Yang X, Leng X, Wang C, Yang Z, and Schipper D
- Abstract
A near-infrared luminescent Cd(II)-Nd(III) nanocluster 1 (molecular dimensions of 1.0 nm × 2.2 nm × 2.2 nm) was obtained using a Schiff base ligand. It can be used as a sensor for the wavelength-dependent detection of rutin even in the presence of other interference, which can be expressed by the third-order equation I
Ex470 nm / IEx395 nm = A [Rut]3 + B [Rut]2 + C [Rut] + D . 1 is used to analyze the Rut concentrations quantitatively in CH3 CN and fetal calf serum (FCS). For the determination of Rut concentrations in FCS, the ranges of recovery and relative standard deviations are 99.06-104.70% and 2.30-4.15%, respectively.- Published
- 2023
- Full Text
- View/download PDF
31. Rapid and quantitative detection of the inflammatory marker neopterin based on a visible luminescent Zn(II)-Eu(III) nanocluster.
- Author
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Zhao J, Leng X, Lin J, Yang X, Lv X, Huang X, Yang Z, and Schipper D
- Subjects
- Neopterin, Luminescent Measurements, Zinc, Luminescence, Europium
- Abstract
A high-nuclearity Zn(II)-Eu(III) nanocluster was synthesized for the rapid and quantitative luminescence detection of neopterin as an inflammatory marker.
- Published
- 2023
- Full Text
- View/download PDF
32. Construction of a Near-IR-Luminescent Rectangular Yb(III) Complex from a Dodecadentate Schiff Base Ligand for the Excitation-Wavelength-Dependent Detection of Aloe Emodin (a Natural Medicinal Ingredient).
- Author
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Hu F, Yang X, Leng X, Wang C, Yang K, Zhang L, and Schipper D
- Subjects
- Ligands, Schiff Bases, Anthraquinones, Emodin
- Abstract
A near-IR-luminescent octanuclear Yb(III) complex 1 was constructed from a new dodecadentate Schiff base ligand, which is used in the rapid and reliable wavelength-dependent detection of aloe emodin (AE) with high sensitivity even in the presence of other interferences.
- Published
- 2023
- Full Text
- View/download PDF
33. Construction of a Near-Infrared Luminescent 48-Metal Rectangular Zn(II)-Yb(III) Nanocluster with Carbonate Templates for the Dual-Emissive Detection of Rutin as a Medicinal Ingredient.
- Author
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Zhao J, Leng X, Yang X, Ma Y, Wang C, Li H, Zhang Z, Lin J, and Schipper D
- Subjects
- Ligands, Ytterbium, Zinc, Luminescence, Lanthanoid Series Elements
- Abstract
An interesting 48-metal Zn(II)-Yb(III) nanocluster ( 1 ) with a size of about 1.3 × 2.8 × 3.1 nm was constructed by carbonate templates from a Schiff base ligand. It exhibits ligand-centered emission and near-infrared (NIR) luminescence of Yb(III), which are used in the dual-emissive detection of rutin (Rut) with high sensitivity even in the presence of other interferences. The response behavior can be expressed by the second-order equation I
980 nm / I510 nm = A *[Rut]2 + B *[Rut] + C , and the limits of detection to Rut for the emissions of 1 are 2.23 μM and 0.20 nM.- Published
- 2022
- Full Text
- View/download PDF
34. Rapid and reliable triple-emissive detection of 2,6-dichloro-4-nitroaniline as a pesticide based on a high-nuclear Cd(II)-Sm(III) nanocluster.
- Author
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Leng X, Yang X, Ma Y, Wang C, Li H, Zhang Z, Yang K, and Schipper D
- Subjects
- Cadmium chemistry, Ligands, Crystallography, X-Ray, Pesticides analysis
- Abstract
A luminescent 56-metal Cd(II)-Sm(III) nanocluster (1, molecular sizes: 4.5 × 2.7 × 2.7 nm) was constructed from a flexible Schiff base ligand, and its crystal structure was determined using the X-ray diffraction method. It shows a rapid triple-emissive response to 2,6-dichloro-4-nitroaniline (DCN, a common pesticide) with high sensitivity and selectivity, which can be used to quantitatively analyze the DCN concentrations in fruit extracts. The limits of detection (LODs) of 1 to DCN for the visible ligand-centered and Sm(III) emissions and NIR Sm(III) luminescence are from 0.95 μM to 2.81 μM.
- Published
- 2022
- Full Text
- View/download PDF
35. Antigenic cartography of SARS-CoV-2 reveals that Omicron BA.1 and BA.2 are antigenically distinct.
- Author
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Mykytyn AZ, Rissmann M, Kok A, Rosu ME, Schipper D, Breugem TI, van den Doel PB, Chandler F, Bestebroer T, de Wit M, van Royen ME, Molenkamp R, Oude Munnink BB, de Vries RD, GeurtsvanKessel C, Smith DJ, Koopmans MPG, Rockx B, Lamers MM, Fouchier RAM, and Haagmans BL
- Subjects
- Animals, Cell Line, Cricetinae, Humans, Immune Sera, COVID-19, SARS-CoV-2 genetics
- Abstract
The emergence and rapid spread of SARS-CoV-2 variants may affect vaccine efficacy substantially. The Omicron variant termed BA.2, which differs substantially from BA.1 based on genetic sequence, is currently replacing BA.1 in several countries, but its antigenic characteristics have not yet been assessed. Here, we used antigenic cartography to quantify and visualize antigenic differences between early SARS-CoV-2 variants (614G, Alpha, Beta, Gamma, Zeta, Delta, and Mu) using hamster antisera obtained after primary infection. We first verified that the choice of the cell line for the neutralization assay did not affect the topology of the map substantially. Antigenic maps generated using pseudo-typed SARS-CoV-2 on the widely used VeroE6 cell line and the human airway cell line Calu-3 generated similar maps. Maps made using authentic SARS-CoV-2 on Calu-3 cells also closely resembled those generated with pseudo-typed viruses. The antigenic maps revealed a central cluster of SARS-CoV-2 variants, which grouped on the basis of mutual spike mutations. Whereas these early variants are antigenically similar, clustering relatively close to each other in antigenic space, Omicron BA.1 and BA.2 have evolved as two distinct antigenic outliers. Our data show that BA.1 and BA.2 both escape vaccine-induced antibody responses as a result of different antigenic characteristics. Thus, antigenic cartography could be used to assess antigenic properties of future SARS-CoV-2 variants of concern that emerge and to decide on the composition of novel spike-based (booster) vaccines.
- Published
- 2022
- Full Text
- View/download PDF
36. Rapid and Reliable Excitation Wavelength-Dependent Detection of 2,6-Dipicolinic Acid Based on a Luminescent Cd(II)-Tb(III) Nanocluster.
- Author
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Leng X, Hao W, Yang X, Zhang Z, Li H, Ma Y, Cheng Y, and Schipper D
- Subjects
- Cadmium, Picolinic Acids chemistry, Lanthanoid Series Elements chemistry, Luminescence
- Abstract
A Cd(II)-Tb(III) nanocluster {[Cd
10 Tb9 L8 (OH)16 (OAc)23 (H2 O)3 ][Cd10 Tb9 L8 (OH)16 (OAc)23 (H2 O)4 ]}·3H2 O ( 1 ), which contains two crystallographically independent components, was constructed from a tridentate ligand (HL, 3-ethoxysalicylaldehyde). It exhibits rapid and reliable excitation wavelength-dependent luminescence response to 2,6-dipicolinic acid (DPA) [limit of detection = 0.23 nM], which is not influenced by aromatic carboxylates, amino acids, and ions. The test papers of 1 can be used to check DPA in solution. The equation IEx272nm / IEx329nm = 0.0109 × [DPA]2 + 0.106 × [DPA] + 2.39 of 1 for the luminescence response could be used to quantitatively measure the concentration of DPA in tap water. 1 displays rapid and stable luminescence response to DPA, with the sensing times shorter than 5 s and no changes for the lanthanide luminescence over 24 h.- Published
- 2022
- Full Text
- View/download PDF
37. Silencing susceptibility genes in potato hinders primary infection of Phytophthora infestans at different stages.
- Author
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Sun K, Schipper D, Jacobsen E, Visser RGF, Govers F, Bouwmeester K, and Bai Y
- Abstract
Most potato cultivars are susceptible to late blight disease caused by the oomycete pathogen Phytophthora infestans. A new source of resistance to prevent or diminish pathogen infection is found in the genetic loss of host susceptibility. Previously, we showed that RNAi-mediated silencing of the potato susceptibility (S) genes StDND1, StDMR1 and StDMR6 leads to increased late blight resistance. The mechanisms underlying this S-gene mediated resistance have thus far not been identified. In this study, we examined the infection process of P. infestans on StDND1-, StDMR1- and StDMR6-silenced potato lines. Microscopic analysis showed that penetration of P. infestans spores was hampered on StDND1-silenced plants. On StDMR1- and StDMR6-silenced plants, P. infestans infection was arrested at a primary infection stage by enhanced cell death responses. Histochemical staining revealed that StDMR1- and StDMR6-silenced plants display elevated ROS levels in cells at the infection sites. Resistance in StDND1-silenced plants, however, seems not to rely on a cell death response as ROS accumulation was found to be absent at most inoculated sites. Quantitative analysis of marker gene expression suggests that the increased resistance observed in StDND1- and StDMR6-silenced plants relies on an early onset of SA- and ET-mediated signalling pathways. Resistance mediated by silencing StDMR1 was found to be correlated with the early induction of SA-mediated signalling. These data provide evidence that different defense mechanisms are involved in late blight resistance mediated by functional impairment of different potato S-genes., (© The Author(s) 2022. Published by Oxford University Press. All rights reserved.)
- Published
- 2022
- Full Text
- View/download PDF
38. Regulatable Detection of Antibiotics Based on a Near-IR-Luminescent Tubelike Zn(II)-Yb(III) Nanocluster.
- Author
-
Chen Y, Yang X, Cheng Y, Zhang L, Yang Z, and Schipper D
- Subjects
- Nitrofurans analysis, Nitrofurans chemistry, Luminescence, Molecular Structure, Fluoroquinolones analysis, Fluoroquinolones chemistry, Coordination Complexes chemistry, Coordination Complexes chemical synthesis, Anti-Bacterial Agents chemistry, Anti-Bacterial Agents analysis, Anti-Bacterial Agents chemical synthesis, Zinc chemistry, Zinc analysis, Ytterbium chemistry
- Abstract
A tubelike Zn(II)-Yb(III) cluster, [Zn
6 Yb5 L5 (HL)(NO3 )4 (DMF)6 (EtOH)4 (H2 O)4 ] ( 1 ; DMF = N , N -dimethylformamide and EtOH = ethanol; molecular size 1.5 × 1.8 × 2.9 nm), was synthesized from a new long-chain Schiff base ligand. 1 exhibits a regulatable near-IR-luminescent response to nitrofuran antibiotics (NFAs) and fluoroquinolones with high sensitivity, which is not influenced by other antibiotics. The quenching constants of NFAs and fluoroquinolones range from 0.55 × 104 to 8.8 × 104 M-1 , and the detection limits of 1 to them are from 4.2 × 10-4 to 2.6 × 10-5 M. It also shows a luminescent response to real antibiotic drugs containing NFAs and fluoroquinolones.- Published
- 2022
- Full Text
- View/download PDF
39. Dynamic prediction of mortality in COVID-19 patients in the intensive care unit: A retrospective multi-center cohort study.
- Author
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Smit JM, Krijthe JH, Endeman H, Tintu AN, de Rijke YB, Gommers DAMPJ, Cremer OL, Bosman RJ, Rigter S, Wils EJ, Frenzel T, Dongelmans DA, De Jong R, Peters MAA, Kamps MJA, Ramnarain D, Nowitzky R, Nooteboom FGCA, De Ruijter W, Urlings-Strop LC, Smit EGM, Mehagnoul-Schipper DJ, Dormans T, De Jager CPC, Hendriks SHA, Achterberg S, Oostdijk E, Reidinga AC, Festen-Spanjer B, Brunnekreef GB, Cornet AD, Van den Tempel W, Boelens AD, Koetsier P, Lens JA, Faber HJ, Karakus A, Entjes R, De Jong P, Rettig TCD, Arbous MS, Lalisang RCA, Tonutti M, De Bruin DP, Elbers PWG, Van Bommel J, and Reinders MJT
- Abstract
Background: The COVID-19 pandemic continues to overwhelm intensive care units (ICUs) worldwide, and improved prediction of mortality among COVID-19 patients could assist decision making in the ICU setting. In this work, we report on the development and validation of a dynamic mortality model specifically for critically ill COVID-19 patients and discuss its potential utility in the ICU., Methods: We collected electronic medical record (EMR) data from 3222 ICU admissions with a COVID-19 infection from 25 different ICUs in the Netherlands. We extracted daily observations of each patient and fitted both a linear (logistic regression) and non-linear (random forest) model to predict mortality within 24 h from the moment of prediction. Isotonic regression was used to re-calibrate the predictions of the fitted models. We evaluated the models in a leave-one-ICU-out (LOIO) cross-validation procedure., Results: The logistic regression and random forest model yielded an area under the receiver operating characteristic curve of 0.87 [0.85; 0.88] and 0.86 [0.84; 0.88], respectively. The recalibrated model predictions showed a calibration intercept of -0.04 [-0.12; 0.04] and slope of 0.90 [0.85; 0.95] for logistic regression model and a calibration intercept of -0.19 [-0.27; -0.10] and slope of 0.89 [0.84; 0.94] for the random forest model., Discussion: We presented a model for dynamic mortality prediction, specifically for critically ill COVID-19 patients, which predicts near-term mortality rather than in-ICU mortality. The potential clinical utility of dynamic mortality models such as benchmarking, improving resource allocation and informing family members, as well as the development of models with more causal structure, should be topics for future research., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Authors.)
- Published
- 2022
- Full Text
- View/download PDF
40. Rapid Evaluation of Coronavirus Illness Severity (RECOILS) in intensive care: Development and validation of a prognostic tool for in-hospital mortality.
- Author
-
Plečko D, Bennett N, Mårtensson J, Dam TA, Entjes R, Rettig TCD, Dongelmans DA, Boelens AD, Rigter S, Hendriks SHA, de Jong R, Kamps MJA, Peters M, Karakus A, Gommers D, Ramnarain D, Wils EJ, Achterberg S, Nowitzky R, van den Tempel W, de Jager CPC, Nooteboom FGCA, Oostdijk E, Koetsier P, Cornet AD, Reidinga AC, de Ruijter W, Bosman RJ, Frenzel T, Urlings-Strop LC, de Jong P, Smit EGM, Cremer OL, Mehagnoul-Schipper DJ, Faber HJ, Lens J, Brunnekreef GB, Festen-Spanjer B, Dormans T, de Bruin DP, Lalisang RCA, Vonk SJJ, Haan ME, Fleuren LM, Thoral PJ, Elbers PWG, and Bellomo R
- Subjects
- Adult, Aged, Critical Care, Hospital Mortality, Humans, Intensive Care Units, Male, Multicenter Studies as Topic, Observational Studies as Topic, Patient Acuity, Prognosis, Retrospective Studies, SARS-CoV-2, COVID-19
- Abstract
Background: The prediction of in-hospital mortality for ICU patients with COVID-19 is fundamental to treatment and resource allocation. The main purpose was to develop an easily implemented score for such prediction., Methods: This was an observational, multicenter, development, and validation study on a national critical care dataset of COVID-19 patients. A systematic literature review was performed to determine variables possibly important for COVID-19 mortality prediction. Using a logistic multivariable model with a LASSO penalty, we developed the Rapid Evaluation of Coronavirus Illness Severity (RECOILS) score and compared its performance against published scores., Results: Our development (validation) cohort consisted of 1480 (937) adult patients from 14 (11) Dutch ICUs admitted between March 2020 and April 2021. Median age was 65 (65) years, 31% (26%) died in hospital, 74% (72%) were males, average length of ICU stay was 7.83 (10.25) days and average length of hospital stay was 15.90 (19.92) days. Age, platelets, PaO2/FiO2 ratio, pH, blood urea nitrogen, temperature, PaCO2, Glasgow Coma Scale (GCS) score measured within +/-24 h of ICU admission were used to develop the score. The AUROC of RECOILS score was 0.75 (CI 0.71-0.78) which was higher than that of any previously reported predictive scores (0.68 [CI 0.64-0.71], 0.61 [CI 0.58-0.66], 0.67 [CI 0.63-0.70], 0.70 [CI 0.67-0.74] for ISARIC 4C Mortality Score, SOFA, SAPS-III, and age, respectively)., Conclusions: Using a large dataset from multiple Dutch ICUs, we developed a predictive score for mortality of COVID-19 patients admitted to ICU, which outperformed other predictive scores reported so far., (© 2021 The Authors. Acta Anaesthesiologica Scandinavica published by John Wiley & Sons Ltd on behalf of Acta Anaesthesiologica Scandinavica Foundation.)
- Published
- 2022
- Full Text
- View/download PDF
41. Susceptibility of rabbits to SARS-CoV-2.
- Author
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Mykytyn AZ, Lamers MM, Okba NMA, Breugem TI, Schipper D, van den Doel PB, van Run P, van Amerongen G, de Waal L, Koopmans MPG, Stittelaar KJ, van den Brand JMA, and Haagmans BL
- Subjects
- Angiotensin-Converting Enzyme 2 physiology, Animals, COVID-19 etiology, COVID-19 veterinary, Disease Susceptibility veterinary, Female, HEK293 Cells, Humans, Virus Shedding, COVID-19 transmission, Rabbits virology, SARS-CoV-2 isolation & purification
- Abstract
Transmission of severe acute respiratory coronavirus-2 (SARS-CoV-2) between livestock and humans is a potential public health concern. We demonstrate the susceptibility of rabbits to SARS-CoV-2, which excrete infectious virus from the nose and throat upon experimental inoculation. Therefore, investigations on the presence of SARS-CoV-2 in farmed rabbits should be considered.
- Published
- 2021
- Full Text
- View/download PDF
42. NIR luminescent detection of quercetin based on an octanuclear Zn(ii)-Nd(iii) salen nanocluster.
- Author
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Niu M, Yang X, Ma Y, Wang C, and Schipper D
- Abstract
A NIR luminescent octanuclear Zn(ii)-Nd(iii) nanocluster 1 was constructed by the use of a salen-type Schiff base ligand. 1 exhibits a lanthanide luminescent response to Que with high sensitivity. The quenching constant of Que to the lanthanide emission is 2.6 × 10
4 M-1 , and the detection limit of 1 to Que is 2.5 μM. The response behavior of 1 to Que is not affected by the existence of some potential interferents such as biomolecules., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)- Published
- 2021
- Full Text
- View/download PDF
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