411 Works

Data from: Juveniles and migrants as drivers for seasonal epizootics of avian influenza virus

Jacintha G. B. Van Dijk, Bethany J. Hoye, Josanne H. Verhagen, Bart A. Nolet, Ron A. M. Fouchier & Marcel Klaassen
1. Similar to other infectious diseases, the prevalence of low pathogenic avian influenza viruses (LPAIV) has been seen to exhibit marked seasonal variation. However, mechanisms driving this variation in wild birds have yet to be tested. We investigated the validity of three previously suggested drivers for the seasonal dynamics in LPAIV infections in wild birds: (1) host density, (2) immunologically-naïve young, and (3) increased susceptibility in migrants. 2. To address these questions, we sampled a...

Predicting responders to prone positioning in mechanically ventilated patients with COVID-19 using machine learning

Tariq A. Dam, Luca F. Roggeveen, Fuda van Diggelen, Lucas M. Fleuren, Ameet R. Jagesar, Martijn Otten, Heder J. de Vries, Diederik Gommers, Olaf L. Cremer, Rob J. Bosman, Sander Rigter, Evert-Jan Wils, Tim Frenzel, Dave A. Dongelmans, Remko de Jong, Marco A. A. Peters, Marlijn J. A. Kamps, Dharmanand Ramnarain, Ralph Nowitzky, Fleur G. C. A. Nooteboom, Wouter de Ruijter, Louise C. Urlings-Strop, Ellen G. M. Smit, D. Jannet Mehagnoul-Schipper, Tom Dormans … & Paul W. G. Elbers
Abstract Background For mechanically ventilated critically ill COVID-19 patients, prone positioning has quickly become an important treatment strategy, however, prone positioning is labor intensive and comes with potential adverse effects. Therefore, identifying which critically ill intubated COVID-19 patients will benefit may help allocate labor resources. Methods From the multi-center Dutch Data Warehouse of COVID-19 ICU patients from 25 hospitals, we selected all 3619 episodes of prone positioning in 1142 invasively mechanically ventilated patients. We excluded...

Additional file 5 of IL-10 producing regulatory B cells are decreased in blood from smokers and COPD patients

Merel Jacobs, Sven Verschraegen, Bihiyga Salhi, Jasper Anckaert, Pieter Mestdagh, Guy G. Brusselle & Ken R. Bracke
Additional file 5. Supplementary Figure 4. Ratios of IL6+ B-effs to IL10+ B-regs in peripheral blood from healthy controls, smokers, and COPD patients determined by flow cytometry. Ratios of IL6+ B-effs to IL10+ B-regs within total B lymphocytes (A) and B cell subsets (B-E) are shown.

Additional file 9 of Significant variation in the performance of DNA methylation predictors across data preprocessing and normalization strategies

Anil P. S. Ori, Ake T. Lu, Steve Horvath & Roel A. Ophoff
Additional file 9. Supplementary methods.

Additional file 1 of Right ventricular strain measurements in critically ill patients: an observational SICS sub-study

Madelon E. Vos, Eline G. M. Cox, Maaike R. Schagen, Bart Hiemstra, Adrian Wong, Jacqueline Koeze, Iwan C. C. van der Horst & Renske Wiersema
Additional file 1: Table S1. Overview of eligible patients from the SICS cohorts and RV function measurements obtained. Table S2. Overview of missing strain segments. Table S3. Baseline characteristics based on conventional RV function measurements preserved and strain reduced (CPSR). Table S4. RV echocardiography variables categorized by receiving mechanical ventilation (with peep > 8). Table S5. Baseline characteristics sensitivity analysis: RV fractional area change (FAC) preserved versus RV strain reduced (FPSR). Figure S1. Sensitivity analysis;...

Additional file 3 of T-cells in human trigeminal ganglia express canonical tissue-resident memory T-cell markers

Peter-Paul A. Unger, Anna E. Oja, Tamana Khemai-Mehraban, Werner J. D. Ouwendijk, Pleun Hombrink & Georges M. G. M. Verjans
Additional file 3: Figure S3. Effect of concentration and duration collagenase IV digestion of human brain tissue on the expression of T-cell differentiation markers. Normal-appearing white matter obtained from 3 deceased brain donors was digested with collagenase IV at different tissue weight to digestion medium volume ratios (i.e., 1:2.5; 1:5 or 1:10 w:v) and different digestion incubation times (i.e., 30, 60 or 120 min). Frequency of CD4+, CD8+, CD69+, CD103+, CD45RA+, CD27+, CCR7+, CD28+, KLRG1+,...

Additional file 2 of Plasma proteome profiling identifies changes associated to AD but not to FTD

R. Babapour Mofrad, M. del Campo, C. F. W. Peeters, L. H. H. Meeter, H. Seelaar, M. Koel-Simmelink, I. H. G. B. Ramakers, H. A. M. Middelkoop, P. P. De Deyn, J. A. H. R. Claassen, J. C. van Swieten, C. Bridel, J. J. M. Hoozemans, P. Scheltens, W. M. van der Flier, Y. A. L. Pijnenburg & Charlotte E. Teunissen
Additional file 2.

Additional file 2 of Plasma proteome profiling identifies changes associated to AD but not to FTD

R. Babapour Mofrad, M. del Campo, C. F. W. Peeters, L. H. H. Meeter, H. Seelaar, M. Koel-Simmelink, I. H. G. B. Ramakers, H. A. M. Middelkoop, P. P. De Deyn, J. A. H. R. Claassen, J. C. van Swieten, C. Bridel, J. J. M. Hoozemans, P. Scheltens, W. M. van der Flier, Y. A. L. Pijnenburg & Charlotte E. Teunissen
Additional file 2.

Additional file 1 of Fetal exposure to phthalates and bisphenols and DNA methylation at birth: the Generation R Study

Chalana M. Sol, Abigail Gaylord, Susana Santos, Vincent W. V. Jaddoe, Janine F. Felix & Leonardo Trasande
Additional file 1. Additional file containing supplemental figures and tables. Fig. S1. Flowchart of participants included in the study. Fig. S2. Manhattan plot of associations between a mixture of phthalates and bisphenols during first, second and third trimester with DNA methylation at birth. Table S1. Urine concentrations of phthalates and bisphenols during pregnancy in non-participants. Table S2. CpGs with p-values <1.0 * 10–5 from epigenome-wide association study of a mixture of phthalates and bisphenols in...

Additional file 1 of Fetal exposure to phthalates and bisphenols and DNA methylation at birth: the Generation R Study

Chalana M. Sol, Abigail Gaylord, Susana Santos, Vincent W. V. Jaddoe, Janine F. Felix & Leonardo Trasande
Additional file 1. Additional file containing supplemental figures and tables. Fig. S1. Flowchart of participants included in the study. Fig. S2. Manhattan plot of associations between a mixture of phthalates and bisphenols during first, second and third trimester with DNA methylation at birth. Table S1. Urine concentrations of phthalates and bisphenols during pregnancy in non-participants. Table S2. CpGs with p-values <1.0 * 10–5 from epigenome-wide association study of a mixture of phthalates and bisphenols in...

Additional file 1 of Acute mesenteric ischemia: updated guidelines of the World Society of Emergency Surgery

Miklosh Bala, Fausto Catena, Jeffry Kashuk, Belinda De Simone, Carlos Augusto Gomes, Dieter Weber, Massimo Sartelli, Federico Coccolini, Yoram Kluger, Fikri M. Abu-Zidan, Edoardo Picetti, Luca Ansaloni, Goran Augustin, Walter L. Biffl, Marco Ceresoli, Osvaldo Chiara, Massimo Chiarugi, Raul Coimbra, Yunfeng Cui, Dimitris Damaskos, Salomone Di Saverio, Joseph M. Galante, Vladimir Khokha, Andrew W. Kirkpatrick, Kenji Inaba … & Ernest E. Moore
Additional file 1: Table S3. Summary of the updated 2022 guidelines for AMI: statements and recommendations.

Additional file 1 of Identifying common core outcome domains from core outcome sets of musculoskeletal conditions: protocol for a systematic review

Tamer S. Sabet, David B. Anderson, Peter W. Stubbs, Rachelle Buchbinder, Caroline B. Terwee, Alessandro Chiarotto, Joel Gagnier & Arianne P. Verhagen
Additional file 1. Appendix 1.

Additional file 2 of An integrated in silico-in vitro approach for identifying therapeutic targets against osteoarthritis

Raphaëlle Lesage, Mauricio N. Ferrao Blanco, Roberto Narcisi, Tim Welting, Gerjo J. V. M. van Osch & Liesbet Geris
Additional file 2. Supplementary computational method. Equations, mathematical framework and justification of deviation from the general rule in the equations.

Additional file 2 of An integrated in silico-in vitro approach for identifying therapeutic targets against osteoarthritis

Raphaëlle Lesage, Mauricio N. Ferrao Blanco, Roberto Narcisi, Tim Welting, Gerjo J. V. M. van Osch & Liesbet Geris
Additional file 2. Supplementary computational method. Equations, mathematical framework and justification of deviation from the general rule in the equations.

Additional file 3 of An integrated in silico-in vitro approach for identifying therapeutic targets against osteoarthritis

Raphaëlle Lesage, Mauricio N. Ferrao Blanco, Roberto Narcisi, Tim Welting, Gerjo J. V. M. van Osch & Liesbet Geris
Additional file 3: Table S2. List of variables and mouse genes correspondence. All mathematical variables and the corresponding node in the network have names written in upper cases and do not reflect the official human or mouse nomenclatures. To relate those variables to actual genes more easily, we provide this table of correspondence. A related mouse gene name and NCBI ID is indicated for each variable. Nevertheless, this is not exhaustive since some variables represent...

Additional file 9 of An integrated in silico-in vitro approach for identifying therapeutic targets against osteoarthritis

Raphaëlle Lesage, Mauricio N. Ferrao Blanco, Roberto Narcisi, Tim Welting, Gerjo J. V. M. van Osch & Liesbet Geris
Additional file 9: Data S4. In silico screening predictions of combinatorial treatments. (Microsoft Excel Worksheet). This file reports the results of a screening of combinatorial perturbations. More particularly, it reports all conditions that lead to a transition towards a healthy chondrocyte (SOX9+) when starting from a hypertrophic-like chondrocyte (Runx2+). The first sheet reports conditions leading to such transition 100% of the time, between 99 and 90% of the time for the next sheet and so...

Feasibility of a protocol for deprescribing antihypertensive medication in older patients in Dutch general practices

Dimokrat Hassan, Jorie Versmissen, Karin Hek, Liset van Dijk & Patricia M. L. A. van den Bemt
Abstract Background Older patients using antihypertensive medication may experience Adverse Drug Events (ADEs), and thus benefit from deprescribing. The lack of a practical protocol may hamper deprescribing. Therefore, we aimed to develop a deprescribing protocol, based on a review of literature, combined with a feasibility test in a small number of patients. Methods A deprescribing protocol for general practitioners was drafted and tested in older patients using multiple antihypertensive medication in a single arm intervention....

Feasibility of a protocol for deprescribing antihypertensive medication in older patients in Dutch general practices

Dimokrat Hassan, Jorie Versmissen, Karin Hek, Liset van Dijk & Patricia M. L. A. van den Bemt
Abstract Background Older patients using antihypertensive medication may experience Adverse Drug Events (ADEs), and thus benefit from deprescribing. The lack of a practical protocol may hamper deprescribing. Therefore, we aimed to develop a deprescribing protocol, based on a review of literature, combined with a feasibility test in a small number of patients. Methods A deprescribing protocol for general practitioners was drafted and tested in older patients using multiple antihypertensive medication in a single arm intervention....

Additional file 1 of Needs and preferences of women with prior severe preeclampsia regarding app-based cardiovascular health promotion

Lili L. Kókai, Marte F. van der Bijl, Martin S. Hagger, Diarmaid T. Ó Ceallaigh, Kirsten I.M. Rohde, Hans van Kippersluis, Alex Burdorf, Johannes J. Duvekot, Jeanine E. Roeters van Lennep & Anne I. Wijtzes
Supplementary Material 1: Correlations between components of needs per health behavior

Additional file 10 of Significant variation in the performance of DNA methylation predictors across data preprocessing and normalization strategies

Anil P. S. Ori, Ake T. Lu, Steve Horvath & Roel A. Ophoff
Additional file 10. Table that describes sample quality control statistics.

Additional file 11 of Significant variation in the performance of DNA methylation predictors across data preprocessing and normalization strategies

Anil P. S. Ori, Ake T. Lu, Steve Horvath & Roel A. Ophoff
Additional file 11. Table that describes general information on DNAm predictors implemented.

Additional file 12 of Significant variation in the performance of DNA methylation predictors across data preprocessing and normalization strategies

Anil P. S. Ori, Ake T. Lu, Steve Horvath & Roel A. Ophoff
Additional file 12. Peer review history.

Additional file 1 of Significant variation in the performance of DNA methylation predictors across data preprocessing and normalization strategies

Anil P. S. Ori, Ake T. Lu, Steve Horvath & Roel A. Ophoff
Additional file 1: Supplementary figures 1-6.

Additional file 2 of Significant variation in the performance of DNA methylation predictors across data preprocessing and normalization strategies

Anil P. S. Ori, Ake T. Lu, Steve Horvath & Roel A. Ophoff
Additional file 2. Table that describes full ICC statistics.

Additional file 4 of Significant variation in the performance of DNA methylation predictors across data preprocessing and normalization strategies

Anil P. S. Ori, Ake T. Lu, Steve Horvath & Roel A. Ophoff
Additional file 4. Overview of all pipelines implemented with short descriptions.

Registration Year

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Affiliations

  • Erasmus MC
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  • Vrije Universiteit Amsterdam
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