Search

Your search keyword '"Wright, David"' showing total 336 results
Start Over Author "Wright, David" Publication Year Range This year
336 results on '"Wright, David"'

2. Galaxy Tomography with the Gravitational Wave Background from Supermassive Black Hole Binaries

Author

Chen, Yifan, Daniel, Matthias, D'Orazio, Daniel J., Mitridate, Andrea, Sagunski, Laura, Xue, Xiao, Agazie, Gabriella, Baier, Jeremy G., Baker, Paul T., Bécsy, Bence, Blecha, Laura, Brazier, Adam, Brook, Paul R., Burke-Spolaor, Sarah, Burnette, Rand, Casey-Clyde, J. Andrew, Charisi, Maria, Chatterjee, Shami, Cohen, Tyler, Cordes, James M., Cornish, Neil J., Crawford, Fronefield, Cromartie, H. Thankful, DeCesar, Megan E., Demorest, Paul B., Deng, Heling, Dey, Lankeswar, Dolch, Timothy, Ferrara, Elizabeth C., Fiore, William, Fonseca, Emmanuel, Freedman, Gabriel E., Gardiner, Emiko C., Gersbach, Kyle A., Glaser, Joseph, Good, Deborah C., Gültekin, Kayhan, Hazboun, Jeffrey S., Jennings, Ross J., Johnson, Aaron D., Kaplan, David L., Kelley, Luke Zoltan, Key, Joey S., Laal, Nima, Lam, Michael T., Lamb, William G., Larsen, Bjorn, Lazio, T. Joseph W., Lewandowska, Natalia, Liu, Tingting, Luo, Jing, Lynch, Ryan S., Ma, Chung-Pei, Madison, Dustin R., McEwen, Alexander, McKee, James W., McLaughlin, Maura A., Meyers, Patrick M., Mingarelli, Chiara M. F., Nice, David J., Ocker, Stella Koch, Olum, Ken D., Pennucci, Timothy T., Petrov, Polina, Pol, Nihan S., Radovan, Henri A., Ransom, Scott M., Ray, Paul S., Romano, Joseph D., Runnoe, Jessie C., Saffer, Alexander, Sardesai, Shashwat C., Schmitz, Kai, Siemens, Xavier, Simon, Joseph, Siwek, Magdalena S., Fiscella, Sophia V. Sosa, Stairs, Ingrid H., Stinebring, Daniel R., Susobhanan, Abhimanyu, Swiggum, Joseph K., Taylor, Jacob, Taylor, Stephen R., Turner, Jacob E., Unal, Caner, Vallisneri, Michele, van Haasteren, Rutger, Verbiest, Joris, Vigeland, Sarah J., Witt, Caitlin A., Wright, David, and Young, Olivia

Subjects
Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Astrophysics of Galaxies, General Relativity and Quantum Cosmology, High Energy Physics - Phenomenology
Abstract

The detection of a stochastic gravitational wave background by pulsar timing arrays suggests the presence of a supermassive black hole binary population. Although the observed spectrum generally aligns with predictions from orbital evolution driven by gravitational wave emission in circular orbits, there is a discernible preference for a turnover at the lowest observed frequencies. This turnover could indicate a significant hardening phase, transitioning from early environmental influences to later stages predominantly influenced by gravitational wave emission. In the vicinity of these binaries, the ejection of stars or dark matter particles through gravitational three-body slingshots efficiently extracts orbital energy, leading to a low-frequency turnover in the spectrum. By analyzing the NANOGrav 15-year data, we assess how the gravitational wave spectrum depends on the initial inner galactic profile prior to disruption by binary ejections, accounting for a range of initial binary eccentricities. Our findings suggest a parsec-scale galactic center density around $10^6\,M_\odot/\textrm{pc}^3$ across most of the parameter space, offering insights into the environmental effects on black hole evolution and combined matter density near galaxy centers., Comment: 15 pages, 5 figures

Published
2024

3. CMB and energy conservation limits on nanohertz gravitational waves

Author

Wright, David, Giblin, John T., and Hazboun, Jeffrey

Subjects
General Relativity and Quantum Cosmology, Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - High Energy Astrophysical Phenomena
Abstract

The recent evidence for a stochastic gravitational wave background (GWB) in the nanohertz band, announced by pulsar timing array (PTA) collaborations around the world, has been posited to be sourced by either a population of supermassive black holes binaries or perturbations of spacetime near the inflationary era, generated by a zoo of various new physical phenomena. Gravitational waves (GWs) from these latter models would be explained by extensions to the standard model of cosmology and possibly to the standard model of particle physics. While PTA datasets can be used to characterize the parameter spaces of these models, energy conservation and limits from the cosmic microwave background (CMB) can be used $\textit{a priori}$ to bound those parameter spaces. Here we demonstrate that taking a simple rule for energy conservation and using CMB bounds on the radiation energy density can set stringent limits on the parameters for these models., Comment: 6 pages, 4 figures

Published
2024

4. The NANOGrav 15 yr Data Set: Running of the Spectral Index

Author

Agazie, Gabriella, Anumarlapudi, Akash, Archibald, Anne M., Arzoumanian, Zaven, Baier, Jeremy George, Baker, Paul T., Bécsy, Bence, Blecha, Laura, Brazier, Adam, Brook, Paul R., Burke-Spolaor, Sarah, Casey-Clyde, J. Andrew, Charisi, Maria, Chatterjee, Shami, Cohen, Tyler, Cordes, James M., Cornish, Neil J., Crawford, Fronefield, Cromartie, H. Thankful, Crowter, Kathryn, DeCesar, Megan E., Demorest, Paul B., Deng, Heling, Dey, Lankeswar, Dolch, Timothy, Esmyol, David, Ferrara, Elizabeth C., Fiore, William, Fonseca, Emmanuel, Freedman, Gabriel E., Gardiner, Emiko C., Garver-Daniels, Nate, Gentile, Peter A., Gersbach, Kyle A., Glaser, Joseph, Good, Deborah C., Gültekin, Kayhan, Hazboun, Jeffrey S., Jennings, Ross J., Johnson, Aaron D., Jones, Megan L., Kaplan, David L., Kelley, Luke Zoltan, Kerr, Matthew, Key, Joey S., Laal, Nima, Lam, Michael T., Lamb, William G., Larsen, Bjorn, Lazio, T. Joseph W., Lewandowska, Natalia, Santos, Rafael R. Lino dos, Liu, Tingting, Lorimer, Duncan R., Luo, Jing, Lynch, Ryan S., Ma, Chung-Pei, Madison, Dustin R., McEwen, Alexander, McKee, James W., McLaughlin, Maura A., McMann, Natasha, Meyers, Bradley W., Meyers, Patrick M., Mingarelli, Chiara M. F., Mitridate, Andrea, Ng, Cherry, Nice, David J., Ocker, Stella Koch, Olum, Ken D., Pennucci, Timothy T., Perera, Benetge B. P., Pol, Nihan S., Radovan, Henri A., Ransom, Scott M., Ray, Paul S., Romano, Joseph D., Runnoe, Jessie C., Saffer, Alexander, Sardesai, Shashwat C., Schmiedekamp, Ann, Schmiedekamp, Carl, Schmitz, Kai, Schröder, Tobias, Shapiro-Albert, Brent J., Siemens, Xavier, Simon, Joseph, Siwek, Magdalena S., Fiscella, Sophia V. Sosa, Stairs, Ingrid H., Stinebring, Daniel R., Stovall, Kevin, Susobhanan, Abhimanyu, Swiggum, Joseph K., Taylor, Stephen R., Turner, Jacob E., Unal, Caner, Vallisneri, Michele, van Haasteren, Rutger, Vigeland, Sarah J., von Eckardstein, Richard, Wahl, Haley M., Witt, Caitlin A., Wright, David, and Young, Olivia

Subjects
Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Cosmology and Nongalactic Astrophysics, General Relativity and Quantum Cosmology, High Energy Physics - Phenomenology
Abstract

The NANOGrav 15-year data provides compelling evidence for a stochastic gravitational-wave (GW) background at nanohertz frequencies. The simplest model-independent approach to characterizing the frequency spectrum of this signal consists in a simple power-law fit involving two parameters: an amplitude A and a spectral index \gamma. In this paper, we consider the next logical step beyond this minimal spectral model, allowing for a running (i.e., logarithmic frequency dependence) of the spectral index, \gamma_run(f) = \gamma + \beta \ln(f/f_ref). We fit this running-power-law (RPL) model to the NANOGrav 15-year data and perform a Bayesian model comparison with the minimal constant-power-law (CPL) model, which results in a 95% credible interval for the parameter \beta consistent with no running, \beta \in [-0.80,2.96], and an inconclusive Bayes factor, B(RPL vs. CPL) = 0.69 +- 0.01. We thus conclude that, at present, the minimal CPL model still suffices to adequately describe the NANOGrav signal; however, future data sets may well lead to a measurement of nonzero \beta. Finally, we interpret the RPL model as a description of primordial GWs generated during cosmic inflation, which allows us to combine our results with upper limits from big-bang nucleosynthesis, the cosmic microwave background, and LIGO-Virgo-KAGRA., Comment: 17 pages, 4 figures, 2 tables

Published
2024

5. The NANOGrav 15 yr data set: Posterior predictive checks for gravitational-wave detection with pulsar timing arrays

Author

Agazie, Gabriella, Anumarlapudi, Akash, Archibald, Anne M., Arzoumanian, Zaven, Baier, Jeremy George, Baker, Paul T., Bécsy, Bence, Blecha, Laura, Brazier, Adam, Brook, Paul R., Burke-Spolaor, Sarah, Casey-Clyde, J. Andrew, Charisi, Maria, Chatterjee, Shami, Chatziioannou, Katerina, Cohen, Tyler, Cordes, James M., Cornish, Neil J., Crawford, Fronefield, Cromartie, H. Thankful, Crowter, Kathryn, DeCesar, Megan E., Demorest, Paul B., Deng, Heling, Dey, Lankeswar, Dolch, Timothy, Ferrara, Elizabeth C., Fiore, William, Fonseca, Emmanuel, Freedman, Gabriel E., Gardiner, Emiko C., Garver-Daniels, Nate, Gentile, Peter A., Gersbach, Kyle A., Glaser, Joseph, Good, Deborah C., Gültekin, Kayhan, Hazboun, Jeffrey S., Jennings, Ross J., Johnson, Aaron D., Jones, Megan L., Kaiser, Andrew R., Kaplan, David L., Kelley, Luke Zoltan, Kerr, Matthew, Key, Joey S., Laal, Nima, Lam, Michael T., Lamb, William G., Larsen, Bjorn, Lazio, T. Joseph W., Lewandowska, Natalia, Liu, Tingting, Lorimer, Duncan R., Luo, Jing, Lynch, Ryan S., Ma, Chung-Pei, Madison, Dustin R., McEwen, Alexander, McKee, James W., McLaughlin, Maura A., McMann, Natasha, Meyers, Bradley W., Meyers, Patrick M., Mingarelli, Chiara M. F., Mitridate, Andrea, Ng, Cherry, Nice, David J., Ocker, Stella Koch, Olum, Ken D., Pennucci, Timothy T., Perera, Benetge B. P., Pol, Nihan S., Radovan, Henri A., Ransom, Scott M., Ray, Paul S., Romano, Joseph D., Runnoe, Jessie C., Saffer, Alexander, Sardesai, Shashwat C., Schmiedekamp, Ann, Schmiedekamp, Carl, Schmitz, Kai, Shapiro-Albert, Brent J., Siemens, Xavier, Simon, Joseph, Siwek, Magdalena S., Fiscella, Sophia V. Sosa, Stairs, Ingrid H., Stinebring, Daniel R., Stovall, Kevin, Susobhanan, Abhimanyu, Swiggum, Joseph K., Taylor, Stephen R., Turner, Jacob E., Unal, Caner, Vallisneri, Michele, Vigeland, Sarah J., Wahl, Haley M., Witt, Caitlin A., Wright, David, and Young, Olivia

Subjects
Astrophysics - High Energy Astrophysical Phenomena, General Relativity and Quantum Cosmology
Abstract

Pulsar-timing-array experiments have reported evidence for a stochastic background of nanohertz gravitational waves consistent with the signal expected from a population of supermassive--black-hole binaries. Those analyses assume power-law spectra for intrinsic pulsar noise and for the background, as well as a Hellings--Downs cross-correlation pattern among the gravitational-wave--induced residuals across pulsars. These assumptions are idealizations that may not be realized in actuality. We test them in the NANOGrav 15 yr data set using Bayesian posterior predictive checks: after fitting our fiducial model to real data, we generate a population of simulated data-set replications, and use them to assess whether the optimal-statistic significance, inter-pulsar correlations, and spectral coefficients assume extreme values for the real data when compared to the replications. We confirm that the NANOGrav 15 yr data set is consistent with power-law and Hellings--Downs assumptions. We also evaluate the evidence for the stochastic background using posterior-predictive versions of the frequentist optimal statistic and of Bayesian model comparison, and find comparable significance (3.2\ $\sigma$ and 3\ $\sigma$ respectively) to what was previously reported for the standard statistics. We conclude with novel visualizations of the reconstructed gravitational waveforms that enter the residuals for each pulsar. Our analysis strengthens confidence in the identification and characterization of the gravitational-wave background as reported by NANOGrav., Comment: 20 pages, 14 Figures

Published
2024

6. Suppressed HIV antibody responses following exposure to antiretrovirals – evidence from PrEP randomized trials and early antiretroviral treatment initiation studies

Author

Avelino-Silva, Vivian I, Stone, Mars, Bakkour, Sonia, Di Germanio, Clara, Schmidt, Michael, Conway, Ashtyn L, Wright, David, Grebe, Eduard, Custer, Brian, Kleinman, Steven H, Deng, Xutao, Lingappa, Jairam R, Defechereux, Patricia, Mehrotra, Megha, Grant, Robert M, Vasan, Sandhya, Facente, Shelley, Phanuphak, Nittaya, Sacdalan, Carlo, Akapirat, Siriwat, de Souza, Mark, Busch, Michael P, Norris, Philip J, and Study-IV-Pediatric, NHLBI Recipient Epidemiology and Donor Evaluation

Subjects
Medical Microbiology, Biomedical and Clinical Sciences, Clinical Sciences, Immunology, Clinical Trials and Supportive Activities, Prevention, Biotechnology, Sexually Transmitted Infections, HIV/AIDS, Infectious Diseases, Clinical Research, 4.1 Discovery and preclinical testing of markers and technologies, Infection, Good Health and Well Being, HIV testing, antiretroviral therapy, delayed diagnosis, diagnostics, pre-exposure prophylaxis, serologic tests, Microbiology, Public Health and Health Services, Clinical sciences, Epidemiology, Public health
Abstract

BACKGROUND: Exposure to antiretrovirals at or early after HIV acquisition can suppress viral replication and blunt antibody (Ab) responses; a reduced HIV detectability could impact diagnosis and blood donation screening. METHODS: We used three antigen (Ag)/Ab assays and one nucleic acid test (NAT) to analyze samples collected in pre-exposure prophylaxis (PrEP) trials (iPrEx; Partners PrEP) before infection detection by Ab-only rapid diagnostic tests (RDTs), and in early antiretroviral treatment (ART) initiation studies (RV254; SIPP). RESULTS: Reactivity using NAT and Ag/Ab assays in samples collected up to 8 weeks prior to the first reactive RDT from 251 PrEP trials participants varied between 49-61% for active PrEP users and between 27-37% for placebo users. Among RV254 participants, reactivity in Ag/Ab assays was

Published
2024

7. Detection of Nucleocapsid Antibodies Associated with Primary SARS-CoV-2 Infection in Unvaccinated and Vaccinated Blood Donors.

Author

Grebe, Eduard, Stone, Mars, Spencer, Bryan, Akinseye, Akintunde, Wright, David, Di Germanio, Clara, Bruhn, Roberta, Zurita, Karla, Contestable, Paul, Green, Valerie, Lanteri, Marion, Saa, Paula, Biggerstaff, Brad, Coughlin, Melissa, Kleinman, Steve, Custer, Brian, Jones, Jefferson, and Busch, Michael

Subjects
COVID-19, SARS-CoV-2, United States, blood safety, respiratory infections, vaccine-preventable diseases, viruses, zoonoses, Humans, COVID-19, Blood Donors, SARS-CoV-2, Antibodies, Viral, Adult, Middle Aged, Male, COVID-19 Vaccines, Female, Vaccination, Young Adult, Sensitivity and Specificity, Adolescent, Aged, Nucleocapsid, COVID-19 Serological Testing
Abstract

Nucleocapsid antibody assays can be used to estimate SARS-CoV-2 infection prevalence in regions implementing spike-based COVID-19 vaccines. However, poor sensitivity of nucleocapsid antibody assays in detecting infection after vaccination has been reported. We derived a lower cutoff for identifying previous infections in a large blood donor cohort (N = 142,599) by using the Ortho VITROS Anti-SARS-CoV-2 Total-N Antibody assay, improving sensitivity while maintaining specificity >98%. We validated sensitivity in samples donated after self-reported swab-confirmed infections diagnoses. Sensitivity for first infections in unvaccinated donors was 98.1% (95% CI 98.0-98.2) and for infection after vaccination was 95.6% (95% CI 95.6-95.7) based on the standard cutoff. Regression analysis showed sensitivity was reduced in the Delta compared with Omicron period, in older donors, in asymptomatic infections,

Published
2024

8. Perivascular space Identification Nnunet for Generalised Usage (PINGU)

Author

Sinclair, Benjamin, Vivash, Lucy, Moses, Jasmine, Lynch, Miranda, Pham, William, Dorfman, Karina, Marotta, Cassandra, Koh, Shaun, Bunyamin, Jacob, Rowsthorn, Ella, Jarema, Alex, Peiris, Himashi, Chen, Zhaolin, Shultz, Sandy R, Wright, David K, Kong, Dexiao, Naismith, Sharon L., OBrien, Terence J., and Law, Meng

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

Perivascular spaces(PVSs) form a central component of the brain\'s waste clearance system, the glymphatic system. These structures are visible on MRI images, and their morphology is associated with aging and neurological disease. Manual quantification of PVS is time consuming and subjective. Numerous deep learning methods for PVS segmentation have been developed, however the majority have been developed and evaluated on homogenous datasets and high resolution scans, perhaps limiting their applicability for the wide range of image qualities acquired in clinic and research. In this work we train a nnUNet, a top-performing biomedical image segmentation algorithm, on a heterogenous training sample of manually segmented MRI images of a range of different qualities and resolutions from 6 different datasets. These are compared to publicly available deep learning methods for 3D segmentation of PVS. The resulting model, PINGU (Perivascular space Identification Nnunet for Generalised Usage), achieved voxel and cluster level dice scores of 0.50(SD=0.15), 0.63(0.17) in the white matter(WM), and 0.54(0.11), 0.66(0.17) in the basal ganglia(BG). Performance on data from unseen sites was substantially lower for both PINGU(0.20-0.38(WM, voxel), 0.29-0.58(WM, cluster), 0.22-0.36(BG, voxel), 0.46-0.60(BG, cluster)) and the publicly available algorithms(0.18-0.30(WM, voxel), 0.29-0.38(WM cluster), 0.10-0.20(BG, voxel), 0.15-0.37(BG, cluster)), but PINGU strongly outperformed the publicly available algorithms, particularly in the BG. Finally, training PINGU on manual segmentations from a single site with homogenous scan properties gave marginally lower performances on internal cross-validation, but in some cases gave higher performance on external validation. PINGU stands out as broad-use PVS segmentation tool, with particular strength in the BG, an area of PVS related to vascular disease and pathology.

Published
2024

10. Estimation methods for estimands using the treatment policy strategy; a simulation study based on the PIONEER 1 Trial

Author

Bell, James, Drury, Thomas, Mütze, Tobias, Pipper, Christian Bressen, Guizzaro, Lorenzo, Mitroiu, Marian, Rantell, Khadija Rerhou, Wolbers, Marcel, and Wright, David

Subjects
Statistics - Applications
Abstract

Estimands using the treatment policy strategy for addressing intercurrent events are common in Phase III clinical trials. One estimation approach for this strategy is retrieved dropout whereby observed data following an intercurrent event are used to multiply impute missing data. However, such methods have had issues with variance inflation and model fitting due to data sparsity. This paper introduces likelihood-based versions of these approaches, investigating and comparing their statistical properties to the existing retrieved dropout approaches, simpler analysis models and reference-based multiple imputation. We use a simulation based upon the data from the PIONEER 1 Phase III clinical trial in Type II diabetics to present complex and relevant estimation challenges. The likelihood-based methods display similar statistical properties to their multiple imputation equivalents, but all retrieved dropout approaches suffer from high variance. Retrieved dropout approaches appear less biased than reference-based approaches, resulting in a bias-variance trade-off, but we conclude that the large degree of variance inflation is often more problematic than the bias. Therefore, only the simpler retrieved dropout models appear appropriate as a primary analysis in a clinical trial, and only where it is believed most data following intercurrent events will be observed. The jump-to-reference approach may represent a more promising estimation approach for symptomatic treatments due to its relatively high power and ability to fit in the presence of much missing data, despite its strong assumptions and tendency towards conservative bias. More research is needed to further develop how to estimate the treatment effect for a treatment policy strategy.

Published
2024

12. Epilepsy phenotype and its reproducibility after lateral fluid percussion‐induced traumatic brain injury in rats: Multicenter EpiBioS4Rx study project 1

Author

Ndode‐Ekane, Xavier Ekolle, Ali, Idrish, Santana‐Gomez, Cesar, Andrade, Pedro, Immonen, Riikka, Casillas‐Espinosa, Pablo, Paananen, Tomi, Manninen, Eppu, Puhakka, Noora, Smith, Gregory, Brady, Rhys D, Silva, Juliana, Braine, Emma, Hudson, Matt, Yamakawa, Glen R, Jones, Nigel C, Shultz, Sandy R, Harris, Neil, Wright, David K, Gröhn, Olli, Staba, Richard, O'Brien, Terence J, and Pitkänen, Asla

Subjects
Biomedical and Clinical Sciences, Neurosciences, Clinical Sciences, Neurodegenerative, Epilepsy, Traumatic Brain Injury (TBI), Traumatic Head and Spine Injury, Physical Injury - Accidents and Adverse Effects, Brain Disorders, Neurological, Animals, Male, Rats, Brain Injuries, Traumatic, Disease Models, Animal, Epilepsy, Post-Traumatic, Percussion, Phenotype, Rats, Sprague-Dawley, Reproducibility of Results, Seizures, harmonization, posttraumatic epilepsy, preclinical, video-EEG monitoring, Neurology & Neurosurgery, Clinical sciences
Abstract

ObjectiveThis study was undertaken to assess reproducibility of the epilepsy outcome and phenotype in a lateral fluid percussion model of posttraumatic epilepsy (PTE) across three study sites.MethodsA total of 525 adult male Sprague Dawley rats were randomized to lateral fluid percussion-induced brain injury (FPI) or sham operation. Of these, 264 were assigned to magnetic resonance imaging (MRI cohort, 43 sham, 221 traumatic brain injury [TBI]) and 261 to electrophysiological follow-up (EEG cohort, 41 sham, 220 TBI). A major effort was made to harmonize the rats, materials, equipment, procedures, and monitoring systems. On the 7th post-TBI month, rats were video-EEG monitored for epilepsy diagnosis.ResultsA total of 245 rats were video-EEG phenotyped for epilepsy on the 7th postinjury month (121 in MRI cohort, 124 in EEG cohort). In the whole cohort (n = 245), the prevalence of PTE in rats with TBI was 22%, being 27% in the MRI and 18% in the EEG cohort (p > .05). Prevalence of PTE did not differ between the three study sites (p > .05). The average seizure frequency was .317 ± .725 seizures/day at University of Eastern Finland (UEF; Finland), .085 ± .067 at Monash University (Monash; Australia), and .299 ± .266 at University of California, Los Angeles (UCLA; USA; p  .05). Of the 219 seizures, 53% occurred as part of a seizure cluster (≥3 seizures/24 h; p >.05 between the study sites). Of the 209 seizures, 56% occurred during lights-on period and 44% during lights-off period (p > .05 between the study sites).SignificanceThe PTE phenotype induced by lateral FPI is reproducible in a multicenter design. Our study supports the feasibility of performing preclinical multicenter trials in PTE to increase statistical power and experimental rigor to produce clinically translatable data to combat epileptogenesis after TBI.

Published
2024

13. Successful harmonization in EpiBioS4Rx biomarker study on post-traumatic epilepsy paves the way towards powered preclinical multicenter studies

Author

Ndode-Ekane, Xavier Ekolle, Ali, Idrish, Santana-Gomez, Cesar E, Casillas-Espinosa, Pablo M, Andrade, Pedro, Smith, Gregory, Paananen, Tomi, Manninen, Eppu, Immonen, Riikka, Puhakka, Noora, Ciszek, Robert, Hämäläinen, Elina, Brady, Rhys D, Silva, Juliana, Braine, Emma, Hudson, Matthew R, Yamakawa, Glenn, Jones, Nigel C, Shultz, Sandy R, Wright, David, Harris, Neil, Gröhn, Olli, Staba, Richard J, O'Brien, Terence J, and Pitkänen, Asla

Subjects
Biomedical and Clinical Sciences, Neurosciences, Neurodegenerative, Physical Injury - Accidents and Adverse Effects, Brain Disorders, Traumatic Head and Spine Injury, Traumatic Brain Injury (TBI), Epilepsy, Good Health and Well Being, Animals, Rats, Biomarkers, Brain Injuries, Traumatic, Disease Models, Animal, Epilepsy, Post-Traumatic, Seizures, Multicenter Studies as Topic, Common data element, Epileptogenesis, Lateral fluid -percussion injury, Magnetic, Resonance imaging, Plasma sampling, Traumatic brain injury, Videoelectroencephalogram, Lateral fluid-percussion injury, Neurology & Neurosurgery
Abstract

ObjectiveProject 1 of the Preclinical Multicenter Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx) consortium aims to identify preclinical biomarkers for antiepileptogenic therapies following traumatic brain injury (TBI). The international participating centers in Finland, Australia, and the United States have made a concerted effort to ensure protocol harmonization. Here, we evaluate the success of harmonization process by assessing the timing, coverage, and performance between the study sites.MethodWe collected data on animal housing conditions, lateral fluid-percussion injury model production, postoperative care, mortality, post-TBI physiological monitoring, timing of blood sampling and quality, MR imaging timing and protocols, and duration of video-electroencephalography (EEG) follow-up using common data elements. Learning effect in harmonization was assessed by comparing procedural accuracy between the early and late stages of the project.ResultsThe animal housing conditions were comparable between the study sites but the postoperative care procedures varied. Impact pressure, duration of apnea, righting reflex, and acute mortality differed between the study sites (p  0.05). Plasma quality was poor in 4% of the samples in UEF, 1% in Monash and 14% in UCLA. More than 97% of the final cohort were MR imaged at all timepoints in all study sites. The timing of imaging did not differ on D2 and D9 (p > 0.05), but varied at D30, 5 months, and ex vivo timepoints (p  0.05). A decrease in acute mortality and increase in plasma quality across time reflected a learning effect in the TBI production and blood sampling protocols.SignificanceOur study is the first demonstration of the feasibility of protocol harmonization for performing powered preclinical multi-center trials for biomarker and therapy discovery of post-traumatic epilepsy.

Published
2024

17. Suppressed HIV antibody responses following exposure to antiretrovirals—evidence from PrEP randomized trials and early antiretroviral treatment initiation studies

Author

Avelino-Silva, Vivian I., Stone, Mars, Bakkour, Sonia, Di Germanio, Clara, Schmidt, Michael, Conway, Ashtyn L., Wright, David, Grebe, Eduard, Custer, Brian, Kleinman, Steven H., Deng, Xutao, Lingappa, Jairam R., Defechereux, Patricia, Mehrotra, Megha, Grant, Robert M., Vasan, Sandhya, Facente, Shelley, Phanuphak, Nittaya, Sacdalan, Carlo, Akapirat, Siriwat, de Souza, Mark, Busch, Michael P., and Norris, Philip J.

Published
2024
Full Text
View/download PDF

18. Stratified analyses refine association between TLR7 rare variants and severe COVID-19

Author

Rimoldi, Valeria, Paraboschi, Elvezia M., Bandera, Alessandra, Peyvandi, Flora, Grasselli, Giacomo, Blasi, Francesco, Malvestiti, Francesco, Pelusi, Serena, Bianco, Cristiana, Miano, Lorenzo, Lombardi, Angela, Invernizzi, Pietro, Gerussi, Alessio, Citerio, Giuseppe, Biondi, Andrea, Valsecchi, Maria Grazia, Cazzaniga, Marina Elena, Foti, Giuseppe, Beretta, Ilaria, D'Angiò, Mariella, Bettini, Laura Rachele, Farré, Xavier, Iraola-Guzmán, Susana, Kogevinas, Manolis, Castaño-Vinyals, Gemma, Garcia-Etxebarria, Koldo, Nafria, Beatriz, D'Amato, Mauro, Palom, Adriana, Begg, Colin, Clohisey, Sara, Hinds, Charles, Horby, Peter, Knight, Julian, Ling, Lowell, Maslove, David, McAuley, Danny, Millar, Johnny, Montgomery, Hugh, Nichol, Alistair, Openshaw, Peter J.M., Pereira, Alexandre C., Ponting, Chris P., Rowan, Kathy, Semple, Malcolm G., Shankar-Hari, Manu, Summers, Charlotte, Walsh, Timothy, Baillie, J. Kenneth, Aravindan, Latha, Armstrong, Ruth, Biggs, Heather, Boz, Ceilia, Brown, Adam, Clark, Richard, Coutts, Audrey, Coyle, Judy, Cullum, Louise, Das, Sukamal, Day, Nicky, Donnelly, Lorna, Duncan, Esther, Fawkes, Angie, Fineran, Paul, Fourman, Max Head, Furlong, Anita, Furniss, James, Gallagher, Bernadette, Gilchrist, Tammy, Golightly, Ailsa, Griffiths, Fiona, Hafezi, Katarzyna, Hamilton, Debbie, Hendry, Ross, Law, Andy, Law, Dawn, Law, Rachel, Law, Sarah, Lidstone-Scott, Rebecca, Macgillivray, Louise, Maclean, Alan, Mal, Hanning, McCafferty, Sarah, Mcmaster, Ellie, Meikle, Jen, Moore, Shona C., Morrice, Kirstie, Murphy, Lee, Murphy, Sheena, Hellen, Mybaya, Oosthuyzen, Wilna, Zheng, Chenqing, Chen, Jiantao, Parkinson, Nick, Paterson, Trevor, Schon, Katherine, Stenhouse, Andrew, Das, Mihaela, Swets, Maaike, Szoor-McElhinney, Helen, Taneski, Filip, Turtle, Lance, Wackett, Tony, Ward, Mairi, Weaver, Jane, Wrobel, Nicola, Zechner, Marie, Arbane, Gill, Bociek, Aneta, Campos, Sara, Grau, Neus, Jones, Tim Owen, Lim, Rosario, Marotti, Martina, Ostermann, Marlies, Whitton, Christopher, Alldis, Zoe, Astin-Chamberlain, Raine, Bibi, Fatima, Biddle, Jack, Blow, Sarah, Bolton, Matthew, Borra, Catherine, Bowles, Ruth, Burton, Maudrian, Choudhury, Yasmin, Collier, David, Cox, Amber, Easthope, Amy, Ebano, Patrizia, Fotiadis, Stavros, Gurasashvili, Jana, Halls, Rosslyn, Hartridge, Pippa, Kallon, Delordson, Kassam, Jamila, Lancoma-Malcolm, Ivone, Matharu, Maninderpal, May, Peter, Mitchelmore, Oliver, Newman, Tabitha, Patel, Mital, Pheby, Jane, Pinzuti, Irene, Prime, Zoe, Prysyazhna, Oleksandra, Shiel, Julian, Taylor, Melanie, Tierney, Carey, Wood, Suzanne, Zak, Anne, Zongo, Olivier, Bonner, Stephen, Hugill, Keith, Jones, Jessica, Liggett, Steven, Headlam, Evie, Bandla, Nageswar, Gellamucho, Minnie, Davies, Michelle, Thompson, Christopher, Abdelrazik, Marwa, Bakthavatsalam, Dhanalakshmi, Elhassan, Munzir, Ganesan, Arunkumar, Haldeos, Anne, Moreno-Cuesta, Jeronimo, Purohit, Dharam, Vincent, Rachel, Xavier, Kugan, Rohit, Kumar, Alasdair, Frater, Saleem, Malik, David, Carter, Samuel, Jenkins, Lamond, Zoe, Alanna, Wall, Fernandez-Roman, Jaime, Hamilton, David O., Johnson, Emily, Johnston, Brian, Martinez, Maria Lopez, Mulla, Suleman, Shaw, David, Waite, Alicia A.C., Waugh, Victoria, Welters, Ingeborg D., Williams, Karen, Cavazza, Anna, Cockrell, Maeve, Corcoran, Eleanor, Depante, Maria, Finney, Clare, Jerome, Ellen, McPhail, Mark, Nayak, Monalisa, Noble, Harriet, O'Reilly, Kevin, Pappa, Evita, Saha, Rohit, Saha, Sian, Smith, John, Knighton, Abigail, Antcliffe, David, Banach, Dorota, Brett, Stephen, Coghlan, Phoebe, Fernandez, Ziortza, Gordon, Anthony, Rojo, Roceld, Arias, Sonia Sousa, Templeton, Maie, Meredith, Megan, Morris, Lucy, Ryan, Lucy, Clark, Amy, Sampson, Julia, Peters, Cecilia, Dent, Martin, Langley, Margaret, Ashraf, Saima, Wei, Shuying, Andrew, Angela, Bashyal, Archana, Davidson, Neil, Hutton, Paula, McKechnie, Stuart, Wilson, Jean, Baptista, David, Crowe, Rebecca, Fernandes, Rita, Herdman-Grant, Rosaleen, Joseph, Anna, O'Connor, Denise, Allen, Meryem, Loveridge, Adam, McKenley, India, Morino, Eriko, Naranjo, Andres, Simms, Richard, Sollesta, Kathryn, Swain, Andrew, Venkatesh, Harish, Khera, Jacyntha, Fox, Jonathan, Andrew, Gillian, Barclay, Lucy, Callaghan, Marie, Campbell, Rachael, Clark, Sarah, Hope, Dave, Marshall, Lucy, McCulloch, Corrienne, Briton, Kate, Singleton, Jo, Birch, Sohphie, Brimfield, Lutece, Daly, Zoe, Pogson, David, Rose, Steve, Battle, Ceri, Brinkworth, Elaine, Harford, Rachel, Murphy, Carl, Newey, Luke, Rees, Tabitha, Williams, Marie, Arnold, Sophie, Polgarova, Petra, Stroud, Katerina, Meaney, Eoghan, Jones, Megan, Ng, Anthony, Agrawal, Shruti, Pathan, Nazima, White, Deborah, Daubney, Esther, Elston, Kay, Grauslyte, Lina, Hussain, Musarat, Phull, Mandeep, Pogreban, Tatiana, Rosaroso, Lace, Salciute, Erika, Franke, George, Wong, Joanna, George, Aparna, Ortiz-Ruiz de Gordoa, Laura, Peasgood, Emily, Phillips, Claire, Bates, Michelle, Dasgin, Jo, Gill, Jaspret, Nilsson, Annette, Scriven, James, Delgado, Carlos Castro, Dawson, Deborah, Ding, Lijun, Durrant, Georgia, Ezeobu, Obiageri, Farnell-Ward, Sarah, Harrison, Abiola, Kanu, Rebecca, Leaver, Susannah, Maccacari, Elena, Manna, Soumendu, Saluzzio, Romina Pepermans, Queiroz, Joana, Samakomva, Tinashe, Sicat, Christine, Texeira, Joana, Da Gloria, Edna Fernandes, Lisboa, Ana, Rawlins, John, Mathew, Jisha, Kinch, Ashley, Hurt, William James, Shah, Nirav, Clark, Victoria, Thanasi, Maria, Yun, Nikki, Patel, Kamal, Bennett, Sara, Goodwin, Emma, Jackson, Matthew, Kent, Alissa, Tibke, Clare, Woodyatt, Wiesia, Zaki, Ahmed, Abraheem, Azmerelda, Bamford, Peter, Cawley, Kathryn, Dunmore, Charlie, Faulkner, Maria, Girach, Rumanah, Jeffrey, Helen, Jones, Rhianna, London, Emily, Nagra, Imrun, Nasir, Farah, Sainsbury, Hannah, Smedley, Clare, Patel, Tahera, Smith, Matthew, Chukkambotla, Srikanth, Kazi, Aayesha, Hartley, Janice, Dykes, Joseph, Hijazi, Muhammad, Keith, Sarah, Khan, Meherunnisa, Ryan-Smith, Janet, Springle, Philippa, Thomas, Jacqueline, Truman, Nick, Saad, Samuel, Coleman, Dabheoc, Fine, Christopher, Matt, Roseanna, Gay, Bethan, Dalziel, Jack, Ali, Syamlan, Goodchild, Drew, Harling, Rhiannan, Bhatterjee, Ravi, Goddard, Wendy, Davison, Chloe, Duberly, Stephen, Hargreaves, Jeanette, Bolton, Rachel, Davey, Miriam, Golden, David, Seaman, Rebecca, Cherian, Shiney, Cutler, Sean, Heron, Anne Emma, Roynon-Reed, Anna, Szakmany, Tamas, Williams, Gemma, Richards, Owen, Cheema, Yusuf, Brooke, Hollie, Buckley, Sarah, Suarez, Jose Cebrian, Charlesworth, Ruth, Hansson, Karen, Norris, John, Poole, Alice, Rose, Alastair, Sandhu, Rajdeep, Sloan, Brendan, Smithson, Elizabeth, Thirumaran, Muthu, Wagstaff, Veronica, Metcalfe, Alexandra, Brunton, Mark, Caterson, Jess, Coles, Holly, Frise, Matthew, Rai, Sabi Gurung, Jacques, Nicola, Keating, Liza, Tilney, Emma, Bartley, Shauna, Bhuie, Parminder, Gibson, Sian, Lyle, Amanda, McNeela, Fiona, Radhakrishnan, Jayachandran, Hughes, Alistair, Yates, Bryan, Reynolds, Jessica, Campbell, Helen, Thompsom, Maria, Dodds, Steve, Duffy, Stacey, Greer, Sandra, Shuker, Karen, Tridente, Ascanio, Khade, Reena, Sundar, Ashok, Tsinaslanidis, George, Birkinshaw, Isobel, Carter, Joseph, Howard, Kate, Ingham, Joanne, Joy, Rosie, Pearson, Harriet, Roche, Samantha, Scott, Zoe, Bancroft, Hollie, Bellamy, Mary, Carmody, Margaret, Daglish, Jacqueline, Moore, Faye, Rhodes, Joanne, Sangombe, Mirriam, Kadiri, Salma, Croft, Maria, White, Ian, Frost, Victoria, Aquino, Maia, Jha, Rajeev, Krishnamurthy, Vinodh, Lim, Lai, Lim, Li, Combes, Edward, Joefield, Teishel, Monnery, Sonja, Beech, Valerie, Trotman, Sallyanne, Almaden-Boyle, Christine, Austin, Pauline, Cabrelli, Louise, Cole, Stephen, Casey, Matt, Chapman, Susan, Whyte, Clare, Baird, Yolanda, Butler, Aaron, Chadbourn, Indra, Folkes, Linda, Fox, Heather, Gardner, Amy, Gomez, Raquel, Hobden, Gillian, Hodgson, Luke, King, Kirsten, Margarson, Michael, Martindale, Tim, Meadows, Emma, Raynard, Dana, Thirlwall, Yvette, Helm, David, Margalef, Jordi, Criste, Kristine, Cusack, Rebecca, Golder, Kim, Golding, Hannah, Jones, Oliver, Leggett, Samantha, Male, Michelle, Marani, Martyna, Prager, Kirsty, Williams, Toran, Roberts, Belinda, Salmon, Karen, Anderson, Peter, Archer, Katie, Austin, Karen, Davis, Caroline, Durie, Alison, Kelsall, Olivia, Thrush, Jessica, Vigurs, Charlie, Wild, Laura, Wood, Hannah-Louise, Tranter, Helen, Harrison, Alison, Cowley, Nicholas, McAlindon, Michael, Burtenshaw, Andrew, Digby, Stephen, Low, Emma, Morgan, Aled, Cother, Naiara, Rankin, Tobias, Clayton, Sarah, McCurdy, Alex, Ahmed, Cecilia, Baines, Balvinder, Clamp, Sarah, Colley, Julie, Haq, Risna, Hayes, Anne, Hulme, Jonathan, Hussain, Samia, Joseph, Sibet, Kumar, Rita, Maqsood, Zahira, Purewal, Manjit, Benham, Leonie, Bradshaw, Zena, Brown, Joanna, Caswell, Melanie, Cupitt, Jason, Melling, Sarah, Preston, Stephen, Slawson, Nicola, Stoddard, Emma, Warden, Scott, Deacon, Bethan, Lynch, Ceri, Pothecary, Carla, Roche, Lisa, Howe, Gwenllian Sera, Singh, Jayaprakash, Turner, Keri, Ellis, Hannah, Stroud, Natalie, Hunt, Jodie, Dearden, Joy, Dobson, Emma, Drummond, Andy, Mulcahy, Michelle, Munt, Sheila, O'Connor, Grainne, Philbin, Jennifer, Rishton, Chloe, Tully, Redmond, Winnard, Sarah, Cathcart, Susanne, Duffy, Katharine, Puxty, Alex, Puxty, Kathryn, Turner, Lynne, Ireland, Jane, Semple, Gary, Long, Kate, Whiteley, Simon, Wilby, Elizabeth, Ogg, Bethan, Cowton, Amanda, Kay, Andrea, Kent, Melanie, Potts, Kathryn, Wilkinson, Ami, Campbell, Suzanne, Brown, Ellen, Melville, Julie, Naisbitt, Jay, Joseph, Rosane, Lazo, Maria, Walton, Olivia, Neal, Alan, Alexander, Peter, Allen, Schvearn, Bradley-Potts, Joanne, Brantwood, Craig, Egan, Jasmine, Felton, Timothy, Padden, Grace, Ward, Luke, Moss, Stuart, Glasgow, Susannah, Abel, Lynn, Brett, Michael, Digby, Brian, Gemmell, Lisa, Hornsby, James, MacGoey, Patrick, O'Neil, Pauline, Price, Richard, Rodden, Natalie, Rooney, Kevin, Sundaram, Radha, Thomson, Nicola, Hopkins, Bridget, Thrasyvoulou, Laura, Willis, Heather, Clark, Martyn, Coulding, Martina, Jude, Edward, McCormick, Jacqueline, Mercer, Oliver, Potla, Darsh, Rehman, Hafiz, Savill, Heather, Turner, Victoria, Downes, Charlotte, Holding, Kathleen, Riches, Katie, Hilton, Mary, Hayman, Mel, Subramanian, Deepak, Daniel, Priya, Adanini, Oluronke, Bhatia, Nikhil, Msiska, Maines, Collins, Rebecca, Clement, Ian, Patel, Bijal, Gulati, A., Hays, Carole, Webster, K., Hudson, Anne, Webster, Andrea, Stephenson, Elaine, McCormack, Louise, Slater, Victoria, Nixon, Rachel, Hanson, Helen, Fearby, Maggie, Kelly, Sinead, Bridgett, Victoria, Robinson, Philip, Camsooksai, Julie, Humphrey, Charlotte, Jenkins, Sarah, Reschreiter, Henrik, Wadams, Beverley, Death, Yasmin, Bastion, Victoria, Clarke, Daphene, David, Beena, Kent, Harriet, Lorusso, Rachel, Lubimbi, Gamu, Murdoch, Sophie, Penacerrada, Melchizedek, Thomas, Alastair, Valentine, Jennifer, Vochin, Ana, Wulandari, Retno, Djeugam, Brice, Bell, Gillian, English, Katy, Katary, Amro, Wilcox, Louise, Bruce, Michelle, Connolly, Karen, Duncan, Tracy, T-Michael, Helen, Lindergard, Gabriella, Hey, Samuel, Fox, Claire, Alfonso, Jordan, Durrans, Laura Jayne, Guerin, Jacinta, Blackledge, Bethan, Harris, Jade, Hruska, Martin, Eltayeb, Ayaa, Lamb, Thomas, Hodgkiss, Tracey, Cooper, Lisa, Rothwell, Joanne, Allan, Angela, Anderson, Felicity, Kaye, Callum, Liew, Jade, Medhora, Jasmine, Scott, Teresa, Trumper, Erin, Botello, Adriana, Lankester, Liana, Nikitas, Nikitas, Wells, Colin, Stowe, Bethan, Spencer, Kayleigh, Brandwood, Craig, Smith, Lara, Birchall, Katie, Kolakaluri, Laurel, Baines, Deborah, Sukumaran, Anila, Apetri, Elena, Basikolo, Cathrine, Catlow, Laura, Charles, Bethan, Dark, Paul, Doonan, Reece, Harvey, Alice, Horner, Daniel, Knowles, Karen, Lee, Stephanie, Lomas, Diane, Lyons, Chloe, Marsden, Tracy, McLaughlan, Danielle, McMorrow, Liam, Pendlebury, Jessica, Perez, Jane, Poulaka, Maria, Proudfoot, Nicola, Slaughter, Melanie, Slevin, Kathryn, Thomas, Vicky, Walker, Danielle, Michael, Angiy, Collis, Matthew, Cosier, Tracey, Millen, Gemma, Richardson, Neil, Schumacher, Natasha, Weston, Heather, Rand, James, Baxter, Nicola, Henderson, Steven, Kennedy-Hay, Sophie, McParland, Christopher, Rooney, Laura, Sim, Malcolm, McCreath, Gordan, Akeroyd, Louise, Bano, Shereen, Bromley, Matt, Gurr, Lucy, Lawton, Tom, Morgan, James, Sellick, Kirsten, Warren, Deborah, Wilkinson, Brian, McGowan, Janet, Ledgard, Camilla, Stacey, Amelia, Pye, Kate, Bellwood, Ruth, Bentley, Michael, Bewley, Jeremy, Garland, Zoe, Grimmer, Lisa, Gumbrill, Bethany, Johnson, Rebekah, Sweet, Katie, Webster, Denise, Efford, Georgia, Convery, Karen, Fottrell-Gould, Deirdre, Hudig, Lisa, Keshet-Price, Jocelyn, Randell, Georgina, Stammers, Katie, Bokhari, Maria, Linnett, Vanessa, Lucas, Rachael, McCormick, Wendy, Ritzema, Jenny, Sanderson, Amanda, Wild, Helen, Rostron, Anthony, Roy, Alistair, Woods, Lindsey, Cornell, Sarah, Wakinshaw, Fiona, Rogerson, Kimberley, Jarmain, Jordan, Parker, Robert, Reddy, Amie, Turner-Bone, Ian, Wilding, Laura, Harding, Peter, Abernathy, Caroline, Foster, Louise, Gratrix, Andrew, Martinson, Vicky, Parkinson, Priyai, Stones, Elizabeth, Carbral-Ortega, Llucia, Bercades, Georgia, Brealey, David, Hass, Ingrid, MacCallum, Niall, Martir, Gladys, Raith, Eamon, Reyes, Anna, Smyth, Deborah, Zitter, Letizia, Benyon, Sarah, Marriott, Suzie, Park, Linda, Keenan, Samantha, Gordon, Elizabeth, Quinn, Helen, Baines, Kizzy, Cagova, Lenka, Fofano, Adama, Garner, Lucie, Holcombe, Helen, Mepham, Sue, Mitchell, Alice Michael, Mwaura, Lucy, Praman, Krithivasan, Vuylsteke, Alain, Zamikula, Julie, Purewal, Bally, Rivers, Vanessa, Bell, Stephanie, Blakemore, Hayley, Borislavova, Borislava, Faulkner, Beverley, Gendall, Emma, Goff, Elizabeth, Hayes, Kati, Thomas, Matt, Worner, Ruth, Smith, Kerry, Stephens, Deanna, Mew, Louise, Mwaura, Esther, Stewart, Richard, Williams, Felicity, Wren, Lynn, Sutherland, Sara-Beth, Bevan, Emily, Martin, Jane, Trodd, Dawn, Watson, Geoff, Brown, Caroline Wrey, Collins, Amy, Khaliq, Waqas, Gude, Estefania Treus, Akinkugbe, Olugbenga, Bamford, Alasdair, Beech, Emily, Belfield, Holly, Bell, Michael, Davies, Charlene, Jones, Gareth A.L., McHugh, Tara, Meghari, Hamza, O'Neill, Lauran, Peters, Mark J., Ray, Samiran, Tomas, Ana Luisa, Burn, Iona, Hambrook, Geraldine, Manso, Katarina, Penn, Ruth, Shanmugasundaram, Pradeep, Tebbutt, Julie, Thornton, Danielle, Cole, Jade, Davies, Rhys, Duffin, Donna, Hill, Helen, Player, Ben, Thomas, Emma, Williams, Angharad, Griffin, Denise, Muchenje, Nycola, Mupudzi, Mcdonald, Partridge, Richard, Conyngham, Jo-Anna, Thomas, Rachel, Wright, Mary, Corral, Maria Alvarez, Jacob, Reni, Jones, Cathy, Denmade, Craig, Beavis, Sarah, Dale, Katie, Gascoyne, Rachel, Hawes, Joanne, Pritchard, Kelly, Stevenson, Lesley, Whileman, Amanda, Doble, Patricia, Hutter, Joanne, Pawley, Corinne, Shovelton, Charmaine, Vaida, Marius, Butcher, Deborah, O'Sullivan, Susie, Butterworth-Cowin, Nicola, Ahmad, Norfaizan, Barker, Joann, Bauchmuller, Kris, Bird, Sarah, Cawthron, Kay, Harrington, Kate, Jackson, Yvonne, Kibutu, Faith, Lenagh, Becky, Masuko, Shamiso, Mills, Gary H., Raithatha, Ajay, Wiles, Matthew, Willson, Jayne, Newell, Helen, Lye, Alison, Nwafor, Lorenza, Jarman, Claire, Rowland-Jones, Sarah, Foote, David, Cole, Joby, Thompson, Roger, Watson, James, Hesseldon, Lisa, Macharia, Irene, Chetam, Luke, Smith, Jacqui, Ford, Amber, Anderson, Samantha, Birchall, Kathryn, Housley, Kay, Walker, Sara, Milner, Leanne, Hanratty, Helena, Trower, Helen, Phillips, Patrick, Oxspring, Simon, Donne, Ben, Jardine, Catherine, Williams, Dewi, Hay, Alasdair, Flanagan, Rebecca, Hughes, Gareth, Latham, Scott, McKenna, Emma, Anderson, Jennifer, Hull, Robert, Rhead, Kat, Cruz, Carina, Pattison, Natalie, Charnock, Rob, McFarland, Denise, Cosgrove, Denise, Ahmed, Ashar, Morris, Anna, Jakkula, Srinivas, Ali, Asifa, Brady, Megan, Dale, Sam, Dance, Annalisa, Gledhill, Lisa, Greig, Jill, Hanson, Kathryn, Holdroyd, Kelly, Home, Marie, Kelly, Diane, Kitson, Ross, Matapure, Lear, Melia, Deborah, Mellor, Samantha, Nortcliffe, Tonicha, Pinnell, Jez, Robinson, Matthew, Shaw, Lisa, Shaw, Ryan, Thomis, Lesley, Wilson, Alison, Wood, Tracy, Bayo, Lee-Ann, Merwaha, Ekta, Ishaq, Tahira, Hanley, Sarah, Hibbert, Meg, Tetla, Dariusz, Woodford, Chrsitopher, Durga, Latha, Kennard-Holden, Gareth, Branney, Debbie, Frankham, Jordan, Pitts, Sally, White, Nigel, Laha, Shondipon, Verlander, Mark, Williams, Alexandra, Altabaibeh, Abdelhakim, Alvaro, Ana, Gilbert, Kayleigh, Ma, Louise, Mostoles, Loreta, Parmar, Chetan, Simpson, Kathryn, Jetha, Champa, Booker, Lauren, Pratley, Anezka, Adams, Colene, Agasou, Anita, Arden, Tracie, Bowes, Amy, Boyle, Pauline, Beekes, Mandy, Button, Heather, Capps, Nigel, Carnahan, Mandy, Carter, Anne, Childs, Danielle, Donaldson, Denise, Hard, Kelly, Hurford, Fran, Hussain, Yasmin, Javaid, Ayesha, Jones, James, Jose, Sanal, Leigh, Michael, Martin, Terry, Millward, Helen, Motherwell, Nichola, Rikunenko, Rachel, Stickley, Jo, Summers, Julie, Ting, Louise, Tivenan, Helen, Tonks, Louise, Wilcox, Rebecca, Holland, Maureen, Keenan, Natalie, Lyons, Marc, Wassall, Helen, Marsh, Chris, Mahenthran, Mervin, Carter, Emma, Kong, Thomas, Blackman, Helen, Creagh-Brown, Ben, Donlon, Sinead, Michalak-Glinska, Natalia, Mtuwa, Sheila, Pristopan, Veronika, Salberg, Armorel, Smith, Eleanor, Stone, Sarah, Piercy, Charles, Verula, Jerik, Burda, Dorota, Montaser, Rugia, Harden, Lesley, Mayangao, Irving, Marriott, Cheryl, Bradley, Paul, Harris, Celia, Anderson, Susan, Andrews, Eleanor, Birch, Janine, Collins, Emma, Hammerton, Kate, O'Leary, Ryan, Clark, Michele, Purvis, Sarah, Barber, Russell, Hewitt, Claire, Hilldrith, Annette, Jackson-Lawrence, Karen, Shepardson, Sarah, Wills, Maryanne, Butler, Susan, Tavares, Silvia, Cunningham, Amy, Hindale, Julia, Arif, Sarwat, Bean, Sarah, Burt, Karen, Spivey, Michael, Demetriou, Carrie, Eckbad, Charlotte, Hierons, Sarah, Howie, Lucy, Mitchard, Sarah, Ramos, Lidia, Serrano-Ruiz, Alfredo, White, Katie, Kelly, Fiona, Cristiano, Daniele, Dormand, Natalie, Farzad, Zohreh, Gummadi, Mahitha, Liyanage, Kamal, Patel, Brijesh, Salmi, Sara, Sloane, Geraldine, Thwaites, Vicky, Varghese, Mathew, Zborowski, Anelise C., Allan, John, Geary, Tim, Houston, Gordon, Meikle, Alistair, O'Brien, Peter, Forsey, Miranda, Kaliappan, Agilan, Nicholson, Anne, Riches, Joanne, Vertue, Mark, Allan, Elizabeth, Darlington, Kate, Davies, Ffyon, Easton, Jack, Kumar, Sumit, Lean, Richard, Menzies, Daniel, Pugh, Richard, Qiu, Xinyi, Davies, Llinos, Williams, Hannah, Scanlon, Jeremy, Davies, Gwyneth, Mackay, Callum, Lewis, Joannne, Rees, Stephanie, Oblak, Metod, Popescu, Monica, Thankachen, Mini, Higham, Andrew, Simpson, Kerry, Craig, Jayne, Baruah, Rosie, Morris, Sheila, Ferguson, Susie, Shepherd, Amy, Prockter Moore, Luke Stephen, Vizcaychipi, Marcela Paola, Gomes de Almeida Martins, Laura, Carungcong, Jaime, Mohamed Ali, Inthakab Ali, Beaumont, Karen, Blunt, Mark, Coton, Zoe, Curgenven, Hollie, Elsaadany, Mohamed, Fernandes, Kay, Ally, Sameena Mohamed, Rangarajan, Harini, Sarathy, Varun, Selvanayagam, Sivarupan, Vedage, Dave, White, Matthew, Gill, Mandy, Paul, Paul, Ratnam, Valli, Shelton, Sarah, Wynter, Inez, Carmody, Siobhain, Page, Valerie Joan, Beith, Claire Marie, Black, Karen, Clements, Suzanne, Morrison, Alan, Strachan, Dominic, Taylor, Margaret, Clarkson, Michelle, D'Sylva, Stuart, Norman, Kathryn, Auld, Fiona, Donnachie, Joanne, Edmond, Ian, Prentice, Lynn, Runciman, Nikole, Salutous, Dario, Symon, Lesley, Todd, Anne, Turner, Patricia, Short, Abigail, Sweeney, Laura, Murdoch, Euan, Senaratne, Dhaneesha, Hill, Michaela, Kannan, Thogulava, Laura, Wild, Crawley, Rikki, Crew, Abigail, Cunningham, Mishell, Daniels, Allison, Harrison, Laura, Hope, Susan, Inweregbu, Ken, Jones, Sian, Lancaster, Nicola, Matthews, Jamie, Nicholson, Alice, Wray, Gemma, Langton, Helen, Prout, Rachel, Watters, Malcolm, Novis, Catherine, Barron, Anthony, Collins, Ciara, Kaul, Sundeep, Passmore, Heather, Prendergast, Claire, Reed, Anna, Rogers, Paula, Shokkar, Rajvinder, Woodruff, Meriel, Middleton, Hayley, Polgar, Oliver, Nolan, Claire, Mahay, Kanta, Collier, Dawn, Hormis, Anil, Maynard, Victoria, Graham, Cheryl, Walker, Rachel, Knights, Ellen, Price, Alicia, Thomas, Alice, Thorpe, Chris, Behan, Teresa, Burnett, Caroline, Hatton, Jonathan, Heeney, Elaine, Mitra, Atideb, Newton, Maria, Pollard, Rachel, Stead, Rachael, Amin, Vishal, Anastasescu, Elena, Anumakonda, Vikram, Karthik, Komala, Kausar, Rizwana, Reid, Karen, Smith, Jacqueline, Imeson-Wood, Janet, Skinner, Denise, Gaylard, Jane, Mullan, Dee, Newman, Julie, Brown, Alison, Crickmore, Vikki, Debreceni, Gabor, Wilkins, Joy, Nicol, Liz, Reece-Anthony, Rosie, Birt, Mark, Ghosh, Alison, Williams, Emma, Allen, Louise, Beranova, Eva, Crisp, Nikki, Deery, Joanne, Hazelton, Tracy, Knight, Alicia, Price, Carly, Tilbey, Sorrell, Turki, Salah, Turney, Sharon, Cooper, Joshua, Finch, Cheryl, Liderth, Sarah, Quinn, Alison, Waddington, Natalia, Coventry, Tina, Fowler, Susan, MacMahon, Michael, McGregor, Amanda, Cowley, Anne, Highgate, Judith, Gregory, Jane, O'Connell, Susan, Smith, Tim, Barberis, Luigi, Gopal, Shameer, Harris, Nichola, Lake, Victoria, Metherell, Stella, Radford, Elizabeth, Daniel, Amelia, Finn, Joanne, Saha, Rajnish, White, Nikki, Donnison, Phil, Trim, Fiona, Eapen, Beena, Birch, Jenny, Bough, Laura, Goodsell, Josie, Tutton, Rebecca, Williams, Patricia, Williams, Sarah, Winter-Goodwin, Barbara, Nichol, Ailstair, Brickell, Kathy, Smyth, Michelle, Murphy, Lorna, Coetzee, Samantha, Gales, Alistair, Otahal, Igor, Raj, Meena, Sell, Craig, Hilltout, Paula, Evitts, Jayne, Tyler, Amanda, Waldron, Joanne, Beesley, Kate, Board, Sarah, Kubisz-Pudelko, Agnieszka, Lewis, Alison, Perry, Jess, Pippard, Lucy, Wood, Di, Buckley, Clare, Barry, Peter, Flint, Neil, Rekha, Patel, Hales, Dawn, Bunni, Lara, Jennings, Claire, Latif, Monica, Marshall, Rebecca, Subramanian, Gayathri, McGuigan, Peter J., Wasson, Christopher, Finn, Stephanie, Green, Jackie, Collins, Erin, King, Bernadette, Campbell, Andy, Smuts, Sara, Duffield, Joseph, Smith, Oliver, Mallon, Lewis, Claire, Watkins, Botfield, Liam, Butler, Joanna, Dexter, Catherine, Fletcher, Jo, Garg, Atul, Kuravi, Aditya, Ranga, Poonam, Virgilio, Emma, Belagodu, Zakaula, Fuller, Bridget, Gherman, Anca, Olufuwa, Olumide, Paramsothy, Remi, Stuart, Carmel, Oakley, Naomi, Kamundi, Charlotte, Tyl, David, Collins, Katy, Silva, Pedro, Taylor, June, King, Laura, Coates, Charlotte, Crowley, Maria, Wakefield, Phillipa, Beadle, Jane, Johnson, Laura, Sargeant, Janet, Anderson, Madeleine, Brady, Ailbhe, Chan, Rebekah, Little, Jeff, McIvor, Shane, Prady, Helena, Whittle, Helen, Mathew, Bijoy, Attwood, Ben, Parsons, Penny, Ward, Geraldine, Bremmer, Pamela, Joe, West, Tracy, Baird, Jim, Ruddy, Davies, Ellie, Sathe, Sonia, Dennis, Catherine, McGregor, Alastair, Parris, Victoria, Srikaran, Sinduya, Sukha, Anisha, Clarke, Noreen, Whiteside, Jonathan, Mascarenhas, Mairi, Donaldson, Avril, Matheson, Joanna, Barrett, Fiona, O'Hara, Marianne, Okeefe, Laura, Bradley, Clare, Eastgate-Jackson, Christine, Filipe, Helder, Martin, Daniel, Maharajh, Amitaa, Garcia, Sara Mingo, Pakou, Glykeria, De Neef, Mark, Dent, Kathy, Horsley, Elizabeth, Akhtar, Muhmmad Nauman, Pearson, Sandra, Potoczna, Dorota, Spencer, Sue, Clapham, Melanie, Harper, Rosemary, Poultney, Una, Rice, Polly, Mutch, Rachel, Armstrong, Lisa, Bates, Hayley, Dooks, Emma, Farquhar, Fiona, Hairsine, Brigid, McParland, Chantal, Packham, Sophie, Bi, Rehana, Scholefield, Barney, Ashton, Lydia, George, Linsha, Twiss, Sophie, Wright, David, Chablani, Manish, Kirkby, Amy, Netherton, Kimberley, Davies, Kim, O'Brien, Linda, Omar, Zohra, Perkins, Emma, Lewis, Tracy, Sutherland, Isobel, Burns, Karen, Ben Chandler, Dr, Elliott, Kerry, Mallinson, Janine, Turnbull, Alison, Gondo, Prisca, Hadebe, Bernard, Kayani, Abdul, Masunda, Bridgett, Anderson, Taya, Hawcutt, Dan, O'Malley, Laura, Rad, Laura, Rogers, Naomi, Saunderson, Paula, Allison, Kathryn Sian, Afolabi, Deborah, Whitbread, Jennifer, Jones, Dawn, Dore, Rachael, Halkes, Matthew, Mercer, Pauline, Thornton, Lorraine, Dawson, Joy, Garrioch, Sweyn, Tolson, Melanie, Aldridge, Jonathan, Kapoor, Ritoo, Loader, David, Castle, Karen, Humphreys, Sally, Tampsett, Ruth, Mackintosh, Katherine, Ayers, Amanda, Harrison, Wendy, North, Julie, Allibone, Suzanne, Genetu, Roman, Kasipandian, Vidya, Patel, Amit, Mac, Ainhi, Murphy, Anthony, Mahjoob, Parisa, Nazari, Roonak, Worsley, Lucy, Fagan, Andrew, Bemand, Thomas, Black, Ethel, Dela Rosa, Arnold, Howle, Ryan, Jhanji, Shaman, Baikady, Ravishankar Rao, Tatham, Kate Colette, Thomas, Benjamin, Bell, Dina, Boyle, Rosalind, Douglas, Katie, Glass, Lynn, Lee, Emma, Lennon, Liz, Rattray, Austin, Taylor, Abigail, Hughes, Rachel Anne, Thomas, Helen, Rees, Alun, Duskova, Michaela, Phipps, Janet, Brooks, Suzanne, Edwards, Michelle, Quaid, Sheena, Watson, Ekaterina, Brayne, Adam, Fisher, Emma, Hunt, Jane, Jackson, Peter, Kaye, Duncan, Love, Nicholas, Parkin, Juliet, Tuckey, Victoria, Van Koutrik, Lynne, Carter, Sasha, Andrew, Benedict, Findlay, Louise, Adams, Katie, Service, Jen, Williams, Alison, Cheyne, Claire, Saunderson, Anne, Moultrie, Sam, Odam, Miranda, Hall, Kathryn, Mapfunde, Isheunesu, Willis, Charlotte, Lyon, Alex, Sri-Chandana, Chunda, Scherewode, Joslan, Stephenson, Lorraine, Marsh, Sarah, Hardy, John, Houlden, Henry, Moncur, Eleanor, Tariq, Ambreen, Tucci, Arianna, Hobrok, Maria, Loosley, Ronda, McGuinness, Heather, Tench, Helen, Wolf-Roberts, Rebecca, Irvine, Val, Shelley, Benjamin, Gorman, Claire, Gupta, Abhinav, Timlick, Elizabeth, Brady, Rebecca, Milligan, Barry, Bellini, Arianna, Bryant, Jade, Mayer, Anton, Pickard, Amy, Roe, Nicholas, Sowter, Jason, Howlett, Alex, Fidler, Katy, Tagliavini, Emma, Donnelly, Kevin, Boos, Jannik, van der Made, Caspar I., Ramakrishnan, Gayatri, Coughlan, Eamon, Asselta, Rosanna, Löscher, Britt-Sabina, Valenti, Luca V.C., de Cid, Rafael, Bujanda, Luis, Julià, Antonio, Pairo-Castineira, Erola, May, Sandra, Zametica, Berina, Heggemann, Julia, Albillos, Agustín, Banales, Jesus M., Barretina, Jordi, Blay, Natalia, Bonfanti, Paolo, Buti, Maria, Fernandez, Javier, Marsal, Sara, Prati, Daniele, Ronzoni, Luisa, Sacchi, Nicoletta, Schultze, Joachim L., Riess, Olaf, Franke, Andre, Rawlik, Konrad, Ellinghaus, David, Hoischen, Alexander, Schmidt, Axel, and Ludwig, Kerstin U.

Published
2024
Full Text
View/download PDF

20. A standardized protocol using clinical adjudication to define true infection status in patients presenting to the emergency department with suspected infections and/or sepsis

Author

Whitfield, Natalie N., Hogan, Catherine A., Chenoweth, James, Hansen, Jonathan, Hsu, Edbert B., Humphries, Roger, Mann, Edana, May, Larissa, Michelson, Edward A., Rothman, Richard, Self, Wesley H., Smithline, Howard A., Karita, Helen Cristina Stankiewicz, Steingrub, Jay S., Swedien, Daniel, Weissman, Alexandra, Wright, David W., Liesenfeld, Oliver, and Shapiro, Nathan I.

Published
2024
Full Text
View/download PDF

21. Effect of PCI on Health Status in Ischemic Left Ventricular Dysfunction: Insights From REVIVED-BCIS2

Author

Perera, Divaka, Chiribiri, Amedeo, Carr-White, Gerry, Pavlidis, Antonis, Redwood, Simon, Clapp, Brian, Rinaldi, Aldo, Rahman, Haseeb, Briceno, Natalia, Arnold, Sophie, Raynsford, Amy, Wilson, Karen, Clack, Lucy, Petrie, Mark, McEntegart, Margaret, Watkins, Stuart, Shaukat, Aadil, Rocchiccioli, Paul, McAdam, Marion, McPherson, Elizabeth, Cowan, Louise, Wood, Marie, Weerackody, Roshan, Davies, Ceri, Smith, Elliot, Modi, Bhavik, Mathew, Bindu, Mitchelmore, Oliver, Adrego, Rita, Andiapen, Mervyn, O’Kane, Peter, Din, Jehangir, Kennard, Sarah, Orr, Sarah, Purnell, Cathie, Greenwood, John, Blaxill, Jonathan, Mozid, Abdul, Anderson, Michelle, Somers, Kathryn, Dixon, Lana, Walsh, Simon, Spence, Mark, Glover, Patricia, Brown, Caroline, Edwards, Richard, McDiarmid, Adam, Egred, Mohaned, Narytnyk, Alla, Wealleans, Vera, Amin-Youssef, George, Shah, Ajay, McDonagh, Theresa, Byrne, Jonathan, Pareek, Nilesh, Breeze, Jonathan, Antao, Catherine, De Silva, Kalpa, Strange, Julian, Johnson, Tom, Nightingale, Angus, Gallego, Laura, Medina, Cristina, Gershlick, Anthony, McCann, Gerald, Ladwiniec, Andrew, Squire, Iain, Davison, Joanna, Kenmuir-Hogg, Kris, Spratt, James, Cosgrove, Claudia, Williams, Rupert, Firoozi, Sam, Lim, Pitt, Bonato, Giovanna, Sookhoo, Vennessa, Conway, Dwayne, Brooksby, Paul, Wright, Judith, Exley, Donna, Cotton, James, Horton, Richard, Metherell, Stella, Smallwood, Andrew, Hogrefe, Kai, Cheng, Adrian, Beirnes, Charmaine, Sidgwick, Sian, Lockie, Tim, Patel, Niket, Rakhit, Roby, Davies, Nina, Smit, Angelique, Ahmed, Fozia, Hendry, Cara, Fath-Odoubadi, Farzin, Fraser, Douglas, Mamas, Mamas, Oommen, Anu, Charles, Thabitha, Behan, Miles, Japp, Alan, Rif, Belinda, Jenkins, Nicholas, McClure, Sam, Oates, Pauline, Martin, Karen, Abdelaal, Eltigani, Sarma, Jaydeep, Shastri, Sanjay, Riley, Jo, Giannopoulou, Sarra, Quinn, Sophie, Magapu, Pradeep, Stables, Rod, Wright, David, Barton, Janet, Clarkson, Nichola, Mahmoudi, Michael, Flett, Andrew, Curzen, Nick, Radmore, Judith, Gough, Sam, Ludman, Andrew, Kurdi, Hibba, Keenan, Samantha, Banerjee, Prithwish, Tapp, Luke, Edwards, Nigel, Gibson, Catherine, Kukreja, Neville, Lynch, Mary, Barratt, Claire, de Belder, Mark, Thambyrajah, Jeet, Swanson, Neil, Richardson, Cath, Atkinson, Bev, Viswanathan, Girish, Waugh, Darren, Routledge, Helen, Trevelyan, Jasper, Doughty, Angela, Pegge, Nick, Dhamrait, Sukhbir, Moore, Sally, Galasko, Gavin, Cassidy, Christopher, Waddington, Natalia, Edwards, Tim, Iqbal, Javed, Witherow, Fraser, Birch, Jenny, Munro, Melanie, Wells, Tim, Sinha, Manas, Frost, Linda, Lee, Kaeng, Beattie, James, Pitt, Mike, Chung, Alan, Ramcharitar, Steve, McCafferty, Laura, Martin, Thomas, Irving, John, Iskandar, Zaid, Hutcheon, Anita, Gunn, Julian, Al-Mohammad, Abdallah, Agyemang, Michael, Griffiths, Huw, Kalra, Paul, Howe, Serena, Gray, Tim, Sobolewska, Jolanta, Morby, Louise, Glover, Jason, Beynon, James, Knight, Janet, Das, Paul, Bellamy, Chris, Harman, Emily, Pye, Maurice, Megarry, Simon, McGill, Yvonne, Redfearn, Heidi, Ryan, Matthew, Taylor, Dylan, Dodd, Matthew, Spertus, John A., Kosiborod, Mikhail N., Docherty, Kieran F., Clayton, Tim, and Petrie, Mark C.

Published
2024
Full Text
View/download PDF

22. Development and Validation of the Combined Action Observation and Motor Imagery Ability Questionnaire.

Author

Scott, Matthew W., Esselaar, Maaike, Dagnall, Neil, Denovan, Andrew, Marshall, Ben, Deacon, Aimee S., Holmes, Paul S., and Wright, David J.

Subjects
EXPLORATORY factor analysis, MOTOR ability, RESEARCH personnel
Abstract

Combined use of action observation and motor imagery (AOMI) is an increasingly popular motor-simulation intervention, which involves observing movements on video while simultaneously imagining the feeling of movement execution. Measuring and reporting participant imagery-ability characteristics are essential in motor-simulation research, but no measure of AOMI ability currently exists. Accordingly, the AOMI Ability Questionnaire (AOMI-AQ) was developed to address this gap in the literature. In Study 1, two hundred eleven participants completed the AOMI-AQ and the kinesthetic imagery subscales of the Movement Imagery Questionnaire-3 and Vividness of Motor Imagery Questionnaire-2. Following exploratory factor analysis, an 8-item AOMI-AQ was found to correlate positively with existing motor-imagery measures. In Study 2, one hundred seventy-four participants completed the AOMI-AQ for a second time after a period of 7–10 days. Results indicate a good test–retest reliability for the AOMI-AQ. The new AOMI-AQ measure provides a valid and reliable tool for researchers and practitioners wishing to assess AOMI ability. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

24. Impact of Anatomical and Viability-Guided Completeness of Revascularization on Clinical Outcomes in Ischemic Cardiomyopathy

Author

Perera, Divaka, Chiribiri, Amedeo, Carr-White, Gerry, Pavlidis, Antonis, Redwood, Simon, Clapp, Brian, Rinaldi, Aldo, Rahman, Haseeb, Briceno, Natalia, Arnold, Sophie, Raynsford, Amy, Petrie, Mark, McEntegart, Margaret, Watkins, Stuart, Shaukat, Aadil, Rocchiccioli, Paul, Cowan, Louise, Weerackody, Roshan, Davies, Ceri, Smith, Elliot, Modi, Bhavik, O’Kane, Peter, Din, Jehangir, Hinton, Jonathon, Greenwood, John, Blaxill, Jonathan, Mozid, Abdul, Anderson, Michelle, Dixon, Lana, Walsh, Simon, Spence, Mark, Glover, Patricia, Edwards, Richard, McDiarmid, Adam, Egred, Mohaned, Stevenson, Hannah, Amin-Youssef, George, Shah, Ajay, McDonagh, Theresa, Byrne, Jonathan, Pareek, Nilesh, Breeze, Jonathan, Gershlick, Anthony, McCann, Gerald, Ladwiniec, Andrew, Squire, Iain, Alexander, Donna, De Silva, Kalpa, Strange, Julian, Johnson, Tom, Nightingale, Angus, Gallego, Laura, Spratt, James, Cosgrove, Claudia, Willia, Rupert, Firoozi, Sam, Lim, Pitt, Conway, Dwayne, Swoboda, Peter, Brooksby, Paul, Cotton, James, Horton, Richard, Metherell, Stella, Hogrefe, Kai, Cheng, Adrian, Sidgwick, Sian, Lockie, Tim, Patel, Niket, Rakhit, Roby, Ahmed, Fozia, Hendry, Cara, Fath-Odoubadi, Farzin, Fraser, Douglas, Mamas, Mamas, Behan, Miles, Japp, Alan, Jenkins, Nicholas, McClure, Sam, Martin, Karen, Abdelaal, Eltigani, Sarma, Jaydeep, Sastry, Sanjay, Riley, Jo, Magapu, Pradeep, Stables, Rod, Wright, David, Mahmoudi, Michael, Flett, Andrew, Curzen, Nick, Gough, Sam, Nicholas, Zoe, Ludman, Andrew, Kurdi, Hibba, Keenan, Sam, Thorpe, Kevin, Banerjee, Prithwish, Tapp, Luke, Panicker, Abeesh, de Belder, Mark, Thambyrajah, Jeet, Swanson, Neil, Kukreja, Neville, Lynch, Mary, Viswanathan, Girish, Jones, Elaine, Norman, Sarah, Routledge, Helen, Trevelyan, Jasper, Pegge, Nick, Dhamrait, Sukhbir, Wells, Tim, Sinha, Manas, Galasko, Gavin, Cassidy, Christopher, Edwards, Tim, Iqbal, Javed, Witherow, Fraser, Lee, Kaeng, Beattie, James, Pitt, Mike, Gunn, Julian, Al-Mohammad, Abdallah, Denney, Helen, Griffiths, Huw, Kalra, Paul, Gray, Tim, Sobolewska, Jolanta, Ramcharitar, Steve, McCafferty, Laura, Martin, Thomas, Irving, John, Iskandar, Zaid, Glover, Jason, Beynon, James, Pye, Maurice, Megarry, Simon, Das, Paul, Bellamy, Chris, Ezad, Saad M., Dodd, Matthew, Didagelos, Matthaios, Sidik, Novalia, Li Kam Wa, Matthew, Morgan, Holly P., Walsh, Simon J., Spratt, James C., Ludman, Peter, Clayton, Tim, and Petrie, Mark C.

Published
2024
Full Text
View/download PDF

25. Description and Cross-Sectional Analyses of 25,880 Adults and Children in the UK National Registry of Rare Kidney Diseases Cohort

Author

Abat, Sharirose, Adalat, Shazia, Agbonmwandolor, Joy, Ahmad, Zubaidah, Alejmi, Abdulfattah, Almasarwah, Rashid, Annear, Nicholas, Asgari, Ellie, Ayers, Amanda, Baharani, Jyoti, Balasubramaniam, Gowrie, Kpodo, Felix Jo-Bamba, Bansal, Tarun, Barratt, Alison, Barratt, Jonathan, Bates, Megan, Bayne, Natalie, Bendle, Janet, Benyon, Sarah, Bergmann, Carsten, Bhandari, Sunil, Bingham, Coralie, Boddana, Preetham, Bond, Sally, Braddon, Fiona, Bramham, Kate, Branson, Angela, Brearey, Stephen, Brocklebank, Vicky, Budwal, Sharanjit, Byrne, Conor, Cairns, Hugh, Camilleri, Brian, Campbell, Gary, Capell, Alys, Carmody, Margaret, Carson, Marion, Cathcart, Tracy, Catley, Christine, Cesar, Karine, Chan, Melanie, Chea, Houda, Chess, James, Cheung, Chee Kay, Chick, Katy-Jane, Chitalia, Nihil, Christian, Martin, Chrysochou, Tina, Clark, Katherine, Clayton, Christopher, Clissold, Rhian, Cockerill, Helen, Coelho, Joshua, Colby, Elizabeth, Colclough, Viv, Conway, Eileen, Cook, H. Terence, Cook, Wendy, Cooper, Theresa, Coward, Richard J., Crosbie, Sarah, Cserep, Gabor, Date, Anjali, Davidson, Katherine, Davies, Amanda, Dhaun, Neeraj, Dhaygude, Ajay, Diskin, Lynn, Dixit, Abhijit, Doctolero, Eunice Ann, Dorey, Suzannah, Downard, Lewis, Drayson, Mark, Dreyer, Gavin, Dutt, Tina, Etuk, Kufreabasi, Evans, Dawn, Finch, Jenny, Flinter, Frances, Fotheringham, James, Francis, Lucy, Gale, Daniel P., Gallagher, Hugh, Game, David, Garcia, Eva Lozano, Gavrila, Madita, Gear, Susie, Geddes, Colin, Gilchrist, Mark, Gittus, Matt, Goggolidou, Paraskevi, Goldsmith, Christopher, Gooden, Patricia, Goodlife, Andrea, Goodwin, Priyanka, Grammatikopoulos, Tassos, Gray, Barry, Griffith, Megan, Gumus, Steph, Gupta, Sanjana, Hamilton, Patrick, Harper, Lorraine, Harris, Tess, Haskell, Louise, Hayward, Samantha, Hegde, Shivaram, Hendry, Bruce, Hewins, Sue, Hewitson, Nicola, Hillman, Kate, Hiremath, Mrityunjay, Howson, Alexandra, Htet, Zay, Huish, Sharon, Hull, Richard, Humphries, Alister, Hunt, David P.J., Hunter, Karl, Hunter, Samantha, Ijeomah-Orji, Marilyn, Inston, Nick, Jayne, David, Jenfa, Gbemisola, Jenkins, Alison, Johnson, Sally, Jones, Caroline A., Jones, Colin, Jones, Amanda, Jones, Rachel, Kamesh, Lavanya, Kanigicherla, Durga, Frankl, Fiona Karet, Karim, Mahzuz, Kaur, Amrit, Kavanagh, David, Kearley, Kelly, Kerecuk, Larissa, Khwaja, Arif, King, Garry, King, Grant, Kislowska, Ewa, Klata, Edyta, Kokocinska, Maria, Lambie, Mark, Lawless, Laura, Ledson, Thomas, Lennon, Rachel, Levine, Adam P., Maggie Lai, Ling Wai, Lipkin, Graham, Lovitt, Graham, Lyons, Paul, Mabillard, Holly, Mackintosh, Katherine, Mahdi, Khalid, Maher, Eamonn, Marchbank, Kevin J., Mark, Patrick B., Masoud, Sherry, Masunda, Bridgett, Mavani, Zainab, Mayfair, Jake, McAdoo, Stephen, Mckinnell, Joanna, Melhem, Nabil, Meyrick, Simon, Moochhala, Shabbir, Morgan, Putnam, Morgan, Ann, Muhammad, Fawad, Murray, Shona, Novobritskaya, Kristina, Ong, Albert CM., Oni, Louise, Osmaston, Kate, Padmanabhan, Neal, Parkes, Sharon, Patrick, Jean, Pattison, James, Paul, Riny, Percival, Rachel, Perkins, Stephen J., Persu, Alexandre, Petchey, William G., Pickering, Matthew C., Pinney, Jennifer, Pitcher, David, Plumb, Lucy, Plummer, Zoe, Popoola, Joyce, Post, Frank, Power, Albert, Pratt, Guy, Pusey, Charles, Rabara, Ria, Rabuya, May, Raju, Tina, Javier, Chadd, Roberts, Ian SD., Roufosse, Candice, Rumjon, Adam, Salama, Alan, Saleem, Moin, Sandford, R.N., Sandu, Kanwaljit S., Sarween, Nadia, Sayer, John A., Sebire, Neil, Selvaskandan, Haresh, Shah, Sapna, Sharma, Asheesh, Sharples, Edward J., Sheerin, Neil, Shetty, Harish, Shroff, Rukshana, Simms, Roslyn, Sinha, Manish, Sinha, Smeeta, Smith, Kerry, Smith, Lara, Srivastava, Shalabh, Steenkamp, Retha, Stott, Ian, Stroud, Katerina, Swift, Pauline, Szklarzewicz, Justyna, Tam, Fred, Tan, Kay, Taylor, Robert, Tischkowitz, Marc, Thomas, Kay, Tse, Yincent, Turnbull, Alison, Turner, A. Neil, Tyerman, Kay, Usher, Miranda, Venkat-Raman, Gopalakrishnan, Walker, Alycon, Walsh, Stephen B., Waters, Aoife, Watt, Angela, Webster, Phil, Wechalekar, Ashutosh, Welsh, Gavin Iain, West, Nicol, Wheeler, David, Wiles, Kate, Willcocks, Lisa, Williams, Angharad, Williams, Emma, Williams, Karen, Wilson, Deborah H., Wilson, Patricia D., Winyard, Paul, Wong, Edwin, Wong, Katie, Wood, Grahame, Woodward, Emma, Woodward, Len, Woolf, Adrian, Wright, David, Downward, Lewis, Griffin, Sian, Hall, Matt, Karet Frankl, Fiona, Maher, Eamonn R., Pinney, Jenny, Tam, Frederick W.K., Wilson, Patricia, Sy, Karla Therese L., Huang, Kui, Ye, Jamie, Nitsch, Dorothea, and Bockenhauer, Detlef

Published
2024
Full Text
View/download PDF

29. Application of a Deep Learning System to Detect Papilledema on Nonmydriatic Ocular Fundus Photographs in an Emergency Department

Author

Fraser, Clare L., Micieli, Jonathan A., Costello, Fiona, Bénard-Séguin, Étienne, Yang, Hui, Chan, Carmen Kar Mun, Cheung, Carol Y, Chan, Noel CY, Hamann, Steffen, Gohier, Philippe, Vautier, Anaïs, Rougier, Marie-Bénédicte, Chiquet, Christophe, Vignal-Clermont, Catherine, Hage, Rabih, Khanna, Raoul Kanav, Tran, Thi Ha Chau, Lagrèze, Wolf Alexander, Jonas, Jost B, Ambika, Selvakumar, Fard, Masoud Aghsaei, La Morgia, Chiara, Carbonelli, Michele, Barboni, Piero, Carelli, Valerio, Romagnoli, Martina, Amore, Giulia, Nakamura, Makoto, Fumio, Takano, Petzold, Axel, Wenniger lj, Maillette de Buy, Kho, Richard, Fonseca, Pedro L., Bikbov, Mukharram M., Milea, Dan, Najjar, Raymond P, Ting, Daniel, Tang, Zhiqun, Loo, Jing Liang, Tow, Sharon, Singhal, Shweta, Vasseneix, Caroline, Wong, Tien Yin, Lamoureux, Ecosse, Yu Chen, Ching, Aung, Tin, Schmetterer, Leopold, Sanda, Nicolae, Thuman, Gabriele, Hwang, Jeong-Min, Vanikieti, Kavin, Suwan, Yanin, Padungkiatsagul, Tanyatuth, Yu-Wai-Man, Patrick, Jurkute, Neringa, Hong, Eun Hee, Biousse, Valerie, Newman, Nancy J., Peragallo, Jason H., Datillo, Michael, Kedar, Sachin, Lin, Mung Yan, Patil, Ajay, Aung, Andre, Boyko, Matthew, Alsakran, Wael Abdulraman, Zayani, Amani, Bouthour, Walid, Banc, Ana, Mosley, Rasha, Labella, Fernando, Miller, Neil R., Chen, John J., Mejico, Luis J., Kilangalanga, Janvier Ngoy, Biousse, Valérie, Najjar, Raymond P., Wright, David W., Keadey, Matthew T., Wong, Tien Y., and Bruce, Beau B.

Published
2024
Full Text
View/download PDF

30. Effects of rare kidney diseases on kidney failure: a longitudinal analysis of the UK National Registry of Rare Kidney Diseases (RaDaR) cohort

Author

Abat, Sharirose, Adalat, Shazia, Agbonmwandolor, Joy, Ahmad, Zubaidah, Alejmi, Abdulfattah, Almasarwah, Rashid, Annear, Nicholas, Asgari, Ellie, Ayers, Amanda, Baharani, Jyoti, Balasubramaniam, Gowrie, Kpodo, Felix, Bansal, Tarun, Barratt, Alison, Barratt, Jonathan, Bates, Megan, Bayne, Natalie, Bendle, Janet, Benyon, Sarah, Bergmann, Carsten, Bhandari, Sunil, Bingham, Coralie, Boddana, Preetham, Bond, Sally, Braddon, Fiona, Bramham, Kate, Branson, Angela, Brearey, Stephen, Brocklebank, Vicky, Budwal, Sharanjit, Byrne, Conor, Cairns, Hugh, Camilleri, Brian, Campbell, Gary, Capell, Alys, Carmody, Margaret, Carson, Marion, Cathcart, Tracy, Catley, Christine, Cesar, Karine, Chan, Melanie, Chea, Houda, Chess, James, Cheung, Chee Kay, Chick, Katy-Jane, Chitalia, Nihil, Christian, Martin, Chrysochou, Tina, Clark, Katherine, Clayton, Christopher, Clissold, Rhian, Cockerill, Helen, Coelho, Joshua, Colby, Elizabeth, Colclough, Viv, Conway, Eileen, Cook, H Terence, Cook, Wendy, Cooper, Theresa, Coward, Richard J, Crosbie, Sarah, Cserep, Gabor, Date, Anjali, Davidson, Katherine, Davies, Amanda, Dhaun, Neeraj, Dhaygude, Ajay, Diskin, Lynn, Dixit, Abhijit, Doctolero, Eunice, Dorey, Suzannah, Downard, Lewis, Drayson, Mark, Dreyer, Gavin, Dutt, Tina, Etuk, Kufreabasi, Evans, Dawn, Finch, Jenny, Flinter, Frances, Fotheringham, James, Francis, Lucy, Gale, Daniel P, Gallagher, Hugh, Game, David, Garcia, Eva, Gavrila, Madita, Gear, Susie, Geddes, Colin, Gilchrist, Mark, Gittus, Matt, Goggolidou, Paraskevi, Goldsmith, Christopher, Gooden, Patricia, Goodlife, Andrea, Goodwin, Priyanka, Grammatikopoulos, Tassos, Gray, Barry, Griffith, Megan, Gumus, Steph, Gupta, Sanjana, Hamilton, Patrick, Harper, Lorraine, Harris, Tess, Haskell, Louise, Hayward, Samantha, Hegde, Shivaram, Hendry, Bruce, Hewins, Sue, Hewitson, Nicola, Hillman, Kate, Hiremath, Mrityunjay, Howson, Alexandra, Htet, Zay, Huish, Sharon, Hull, Richard, Humphries, Alister, Hunt, David P J, Hunter, Karl, Hunter, Samantha, Ijeomah-Orji, Marilyn, Inston, Nick, Jayne, David, Jenfa, Gbemisola, Jenkins, Alison, Johnson, Sally, Jones, Caroline A, Jones, Colin, Jones, Amanda, Jones, Rachel, Kamesh, Lavanya, Kanigicherla, Durga, Karet Frankl, Fiona, Karim, Mahzuz, Kaur, Amrit, Kavanagh, David, Kearley, Kelly, Kerecuk, Larissa, Khwaja, Arif, King, Garry, King, Grant, Kislowska, Ewa, Klata, Edyta, Kokocinska, Maria, Lambie, Mark, Lawless, Laura, Ledson, Thomas, Lennon, Rachel, Levine, Adam P, Lai, Ling Wai Maggie, Lipkin, Graham, Lovitt, Graham, Lyons, Paul, Mabillard, Holly, Mackintosh, Katherine, Mahdi, Khalid, Maher, Eamonn, Marchbank, Kevin J, Mark, Patrick B, Masoud, Sherry, Masunda, Bridgett, Mavani, Zainab, Mayfair, Jake, McAdoo, Stephen, Mckinnell, Joanna, Melhem, Nabil, Meyrick, Simon, Moochhala, Shabbir, Morgan, Putnam, Morgan, Ann, Muhammad, Fawad, Murray, Shona, Novobritskaya, Kristina, Ong, Albert CM, Oni, Louise, Osmaston, Kate, Padmanabhan, Neal, Parkes, Sharon, Patrick, Jean, Pattison, James, Paul, Riny, Percival, Rachel, Perkins, Stephen J, Persu, Alexandre, Petchey, William G, Pickering, Matthew C, Pinney, Jennifer, Pitcher, David, Plumb, Lucy, Plummer, Zoe, Popoola, Joyce, Post, Frank, Power, Albert, Pratt, Guy, Pusey, Charles, Rabara, Ria, Rabuya, May, Raju, Tina, Javier, Chadd, Roberts, Ian S D, Roufosse, Candice, Rumjon, Adam, Salama, Alan, Saleem, Moin, Sandford, Richard, Sandu, Kanwaljit S, Sarween, Nadia, Sayer, John A, Sebire, Neil, Selvaskandan, Haresh, Sharma, Asheesh, Sharples, Edward J, Sheerin, Neil, Shetty, Harish, Shroff, Rukshana, Simms, Roslyn, Sinha, Manish, Sinha, Smeeta, Smith, Kerry, Smith, Lara, Srivastava, Shalabh, Steenkamp, Retha, Stott, Ian, Stroud, Katerina, Swift, Pauline, Szklarzewicz, Justyna, Tam, Fred, Tan, Kay, Taylor, Robert, Tischkowitz, Marc, Thomas, Kay, Tse, Yincent, Turnbull, Alison, Turner, A Neil, Tyerman, Kay, Usher, Miranda, Venkat-Raman, Gopalakrishnan, Walker, Alycon, Walsh, Stephen B, Waters, Aoife, Watt, Angela, Webster, Phil, Wechalekar, Ashutosh, Welsh, Gavin I, West, Nicol, Wheeler, David, Wiles, Kate, Willcocks, Lisa, Williams, Angharad, Williams, Emma, Williams, Karen, Wilson, Deborah H, Wilson, Patricia D, Winyard, Paul, Wong, Edwin, Wong, Katie, Wood, Grahame, Woodward, Emma, Woodward, Len, Woolf, Adrian, Wright, David, Downward, Lewis, Chrysochou, Constantina, Griffin, Sian, Hall, Matt, Maher, Eamonn R, Pinney, Jenny, Tam, Frederick W K, Turner, Andrew Neil, Wilson, Patricia, Taylor, Christopher Mark, Nitsch, Dorothea, and Bockenhauer, Detlef

Published
2024
Full Text
View/download PDF

35. Association of Active Renin Content With Mortality in Critically Ill Patients: A Post hoc Analysis of the Vitamin C, Thiamine, and Steroids in Sepsis (VICTAS) Trial*

Author

Busse, Laurence W., Schaich, Christopher L., Chappell, Mark C., McCurdy, Michael T., Staples, Erin M., Ten Lohuis, Caitlin C., Hinson, Jeremiah S., Sevransky, Jonathan E., Rothman, Richard E., Wright, David W., Martin, Greg S., Khanna, Ashish K., Hager, David N., Bernard, Gordon R., Brown, Samuel M., Buchman, Timothy G., Coopersmith, Craig M., DeWilde, Christine, Wesley Ely, E., Eyzaguirre, Lindsay M., Fowler, Alpha A., Gaieski, David F., Gong, Michelle N., Hall, Alex, Hooper, Michael H., Kelen, Gabor D., Khan, Akram, Levine, Mark A., Lewis, Roger J., Lindsell, Chris J., Marlin, Jessica S., McGlothlin, Anna, Moore, Brooks L., Nugent, Katherine L., Nwosu, Samuel, Polito, Carmen C., Rice, Todd W., Ricketts, Erin P., Rudolph, Caroline C., Sanfilippo, Fred, and Viele, Kert

Published
2024
Full Text
View/download PDF

38. Guidelines for reporting action simulation studies (GRASS): Proposals to improve reporting of research in motor imagery and action observation

Author

Moreno-Verdú, Marcos, Hamoline, Gautier, Van Caenegem, Elise E., Waltzing, Baptiste M., Forest, Sébastien, Valappil, Ashika C., Khan, Adam H., Chye, Samantha, Esselaar, Maaike, Campbell, Mark J., McAllister, Craig J., Kraeutner, Sarah N., Poliakoff, Ellen, Frank, Cornelia, Eaves, Daniel L., Wakefield, Caroline, Boe, Shaun G., Holmes, Paul S., Bruton, Adam M., Vogt, Stefan, Wright, David J., and Hardwick, Robert M.

Published
2024
Full Text
View/download PDF

40. classic returns

Author

Wright, David

Subjects
Fog & Car (Novel) -- Lim, Eugene, Men of Maize (Novel) -- Martin, Gerard -- Asturias, Miguel Angel -- Book reviews, The Fortunate Fall (Novel) -- Reed, Cameron, On Strike Against God (Novel) -- Russ, Joanna -- Poliak, Alec, To After That (TOAF) (Novel) -- Gladman, Renee, Books -- Book reviews, Library and information science
Abstract

THIS SUMMER SEES THE REVIVAL of a wide variety of daring and highly original works from publishers large and small. McNally Editions' latest offerings include Ann Schlee's woefully neglected enchanting [...]

Published
2024

44. Predicting future salinity variability in the Ca Mau Peninsula due to Climate Change

Author

Anh Duong Tran, Gagnon Alexandre S., Tanim Ahad Hasan, Wright David, and Thanh Phong Nguyen

Subjects
climate change, sea level rise, salinity intrusion, ca mau peninsula, mike-11, Environmental sciences, GE1-350
Abstract

The Ca Mau Peninsula (CMP) in Vietnam’s Lower Mekong Delta faces pressing challenges, including sea-level rise (SLR), land subsidence, flooding, and saltwater intrusion. Recent years have witnessed an earlier and more severe dry season, leading to heightened saltwater intrusion. As many CMP provinces rely on the Mekong River for their water supply, they are highly susceptible to prolonged drought and salinization. This study employs the MIKE 11 hydraulic model to project saltwater intrusion scenarios in the CMP up to 2050, based on Vietnam’s 2016 Ministry of Natural Resources and Environment (MONRE) SLR projections, considering water regulation from the Cai Lon-Cai Be sluice system. The modelled discharge, water level and salinity were calibrated and validated successfully based on di_erent statistical measures. The projections indicate that saltwater intrusion during the dry season could start 1 to 1.5 months earlier by 2050, with salinity levels exceeding 30 g/l in February. The findings underscore the importance of developing adaptation strategies to address the challenges of climate change and saltwater intrusion, notably in the region’s significant agricultural sector.

Published
2024
Full Text
View/download PDF

45. The centrality of nursing in realizing high quality palliative care: Exploring Canada's framework on palliative care priorities.

Author

Pesut, Barbara, Thorne, Sally, Wright, David Kenneth, and Banwell, Michael

Subjects
NURSES, HEALTH services accessibility, INTELLECT, COMMUNITY health services, POLICY sciences, PALLIATIVE treatment, OCCUPATIONAL roles, RESEARCH funding, QUALITATIVE research, INTERVIEWING, SERVICES for caregivers, HEALTH planning, SURVEYS, SOUND recordings, THEMATIC analysis, CONCEPTUAL structures, QUALITY assurance, DATA analysis software, CONTINUING education
Abstract

Background: Following an earlier mixed-method survey in which we asked stakeholders to report on their perceptions of the progress made in relation to Canada's Framework on Palliative Care and Action Plan, the purpose of this study was to conduct an in-depth qualitative exploration of the factors influencing that progress, or lack thereof. Methods: This was a qualitative interview study conducted in Canada. Inclusion criteria included experience with palliative care in Canada in a professional or volunteer capacity. Interviews were conducted by telephone using an interview guide that asked specific questions in relation to the Framework on palliative care priorities (e.g., education, caregiver support, and equitable access). Data was analyzed using qualitative descriptive methods. Results: Thirty-five diverse stakeholders with extensive experience in palliative care were interviewed. In relation to palliative education, participants indicated that although there were excellent palliative care resources available across the country there was further need for embedding palliative care in undergraduate education and for mentored opportunities to engage in care across diverse contexts. The identification, development, and strategic positioning of champions was an important strategy for improving palliative care knowledge and capacity. The development of standard competencies was viewed as an important step forward; although, there was a need to include more members of the care-team and to create pathways for life-long learning. In relation to support for family caregivers, even as participants cited numerous community-based resources offered by not-for-profit organizations, they described significant barriers including a shortage of in-home support, lack of understanding of what caregivers do, and policy-based contractual and privacy issues. In relation to palliative care access, participants described a nurse-centered, consult-based, multi-site and multi-provider model of care that was facilitated by technology. Barriers to this model were systemic healthcare issues of siloed, fragmented, and for-profit care. Conclusion: Participants in this study had clear insights into the factors that would support or impede progress to the development of palliative care in Canada. Some of those factors were achievable within current health and educational systems. Other factors were going to require longer term and more comprehensive solutions. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

46. Spike and nucleocapsid antibody dynamics following SARS‐CoV‐2 infection and vaccination: Implications for sourcing COVID‐19 convalescent plasma from routinely collected blood donations.

Author

Di Germanio, Clara, Deng, Xutao, Balasko, Brendan G., Simmons, Graham, Martinelli, Rachel, Grebe, Eduard, Stone, Mars, Spencer, Bryan R., Saa, Paula, Yu, Elaine A., Lanteri, Marion C, Green, Valerie, Wright, David, Lartey, Isaac, Kleinman, Steven, Jones, Jefferson, Biggerstaff, Brad J., Contestable, Paul, and Busch, Michael P.

Abstract

Background: COVID‐19 convalescent plasma (CCP) remains a treatment option for immunocompromised patients; however, the current FDA qualification threshold of ≥200 BAU/mL of spike antibody appears to be relatively low. We evaluated the levels of binding (bAb) and neutralizing antibodies (nAb) on serial samples from repeat blood donors who were vaccinated and/or infected to inform criteria for qualifying CCP from routinely collected plasma components. Methods: Donors were categorized into four groups: (1) infected, then vaccinated, (2) vaccinated then infected during the delta, or (3) omicron waves, (4) vaccinated without infection. IgG Spike and total Nuclecapsid bAb were measured, along with S variants and nAb titers using reporter viral particle neutralization. Results: Mean S IgG bAb peaks after infection alone were lower than after primary and booster vaccinations, and higher after delta and omicron infection in previously vaccinated donors. Half‐lives for S IgG ranged from 34 to 66 days after first infection/vaccination events and up to 108 days after second events. The levels of S IgG bAb and nAb were similar across different variants, except for omicron, which were lower. Better correlations of nAb with bAb were observed at higher levels (hybrid immunity) than at the current FDA CCP qualifying threshold. Discussion: Routine plasma donations from donors with hybrid immunity had high S bAb and potent neutralizing activity for 3–6 months after infection. In donations with high (>4000 BAU/mL) S IgG, >95% had high nAb titers (>500) against ancestral and variant S, regardless of COVID‐19 symptoms. These findings provide the basis for test‐based criteria for qualifying CCP from routine blood donations. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

47. In vivo biomarkers of GABAergic function in epileptic rats treated with the GAT‐1 inhibitor E2730.

Author

Ali, Idrish, Jupp, Bianca, Hudson, Matthew R., Major, Brendan, Silva, Juliana, Yamakawa, Glenn R., Casillas‐Espinosa, Pablo M., Braine, Emma, Thergarajan, Peravina, Haskali, Mohammad B., Vivash, Lucy, Brkljaca, Robert, Shultz, Sandy R., Kwan, Patrick, Fukushima, Kazuyuki, Sachdev, Pallavi, Cheng, Jocelyn Y., Mychasiuk, Richelle, Jones, Nigel C., and Wright, David K.

Abstract

Objective: E2730, an uncompetitive γ‐aminobutyric acid (GABA) transporter‐1 (GAT‐1) inhibitor, has potent anti‐seizure effects in a rodent model of chronic temporal lobe epilepsy, the kainic acid status epilepticus (KASE) rat model. In this study, we examined purported neuroimaging and physiological surrogate biomarkers of the effect of E2730 on brain GABAergic function. Methods: We conducted a randomized cross‐over study, incorporating 1‐week treatments with E2730 (100 mg/kg/day subcutaneous infusion) or vehicle in epileptic post‐KASE rats. KASE rats underwent serial 9.4 T magnetic resonance spectroscopy (MRS) measuring GABA and other brain metabolites, [18F]Flumazenil positron emission tomography (PET) quantifying GABAA receptor availability, quantitative electroencephalography (qEEG) and transcranial magnetic stimulation (TMS)–mediated motor activity, as well as continuous video‐EEG recording to measure spontaneous seizures during each treatment. Age‐matched, healthy control animals treated with E2730 or vehicle were also studied. Results: E2730 treatment significantly reduced spontaneous seizures, with 8 of 11 animals becoming seizure‐free. MRS revealed that E2730‐treated animals had significantly reduced taurine levels. [18F]Flumazenil PET imaging revealed no changes in GABA receptor affinity or density during E2730 treatment. The power of gamma frequency oscillations in the EEG was decreased significantly in the auditory cortex and hippocampus of KASE and control rats during E2730 treatment. Auditory evoked gamma frequency power was enhanced by E2730 treatment in the auditory cortex of KASE and healthy controls, but only in the hippocampus of KASE rats. E2730 did not influence motor evoked potentials triggered by TMS. Significance: This study identified clinically relevant changes in multimodality imaging and functional purported biomarkers of GABAergic activity during E2730 treatment in epileptic and healthy control animals. These biomarkers could be utilized in clinical trials of E2730 and potentially other GABAergic drugs to provide surrogate endpoints, thereby reducing the cost of such trials. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

48. An international estimate of the prevalence of differing visual imagery abilities.

Author

Wright, David J., Scott, Matthew W., Kraeutner, Sarah N., Barhoun, Pamela, Bertollo, Maurizio, Campbell, Mark J., Waltzing, Baptiste M., Dahm, Stephan F., Esselaar, Maaike, Frank, Cornelia, Hardwick, Robert M., Fuelscher, Ian, Marshall, Ben, Hodges, Nicola J., Hyde, Christian, and Holmes, Paul S.

Subjects
RESEARCH personnel, QUESTIONNAIRES
Abstract

The aim of this research was to establish prevalence estimates for aphantasia, hypophantasia, typical imagery ability, and hyperphantasia in a large multi-national cohort. In Study 1, the Vividness of Visual Imagery Questionnaire was completed by 3,049 participants. Results indicated prevalence estimates of 1.2% for aphantasia, 3% for hypophantasia, 89.9% for typical imagery ability, and 5.9% for hyperphantasia. In Study 2, to replicate these findings in a larger sample, the Study 1 data were combined with openly available data from previous prevalence studies to create a total sample of 9,063 participants. Re-analysis of this data confirmed prevalence estimates of 0.9% for aphantasia, 3.3% for hypophantasia, 89.7% for typical imagery ability, and 6.1% for hyperphantasia. These robust and up-to-date estimates provide enhanced clarity to researchers regarding the prevalence of differing visual imagery abilities and provide a platform for future studies exploring the role of visual imagery in various cognitive and behavioral tasks. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

49. Calcium supplementation for the prevention of pre‐eclampsia: Challenging the evidence from meta‐analyses.

Author

Wright, David, Wright, Alan, Magee, Laura A., Von Dadelszen, Peter, and Nicolaides, Kypros H.

Subjects
*SENSITIVITY analysis, *CALCIUM, *CONFIDENCE intervals, *DIETARY supplements, *DATA analysis
Abstract

Objective: To investigate the validity of the conclusion from Cochrane reviews and meta‐analyses that treatment with calcium supplementation during pregnancy reduces the risk for pre‐eclampsia by 55%, which has been influential in international guidelines and future research. Design: Sensitivity analysis of data from Cochrane reviews of trials evaluating high‐dose calcium supplementation (of at least 1 g/day) for reduction of pre‐eclampsia risk. Setting: Systematic review and meta‐analysis. Population: The Cochrane reviews and meta‐analyses included 13 trials enrolling a total of 15 730 women. Random‐effects meta‐analysis of these studies resulted in a mean risk ratio (RR, calcium/placebo) of 0.45 (95% confidence interval [CI] 0.31–0.65; p < 0.0001). Methods: We carried out a sensitivity analysis of evidence from the relevant Cochrane review, to examine the impact of study size. Main outcome measures: pre‐eclampsia. Results: In the three largest studies, accounting for 13 815 (88%) of total recruitment, mean RR was 0.92 (95% CI 0.80–1.06) and there was no evidence of heterogeneity between studies (I2 = 0). With inclusion of the smaller studies, mean RR decreased to 0.45 and I2 increased to 70%. Conclusions: In assessment of the effect of calcium supplementation on pre‐eclampsia risk, the naive focus on the mean of the random‐effects meta‐analysis in the presence of substantial heterogeneity is highly misleading. Linked article: This article is commented on by Thornton pp. 1530–1531 in this issue. To view this article visit https://doi.org/10.1111/1471‐0528.17805. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

50. Bias toward the Accents of Virtual Assistants.

Author

Hercula, Sarah E., Shank, Daniel B., Cundiff, Jessica L., and Wright, David

Abstract

Language bias, both positive and negative, is a well-documented phenomenon exhibited among human interlocutors. We examine whether this bias is exhibited toward virtual assistants, specifically, Apple's Siri and Google Assistant, with various accents. We conducted three studies with different stimuli and designs to investigate U.S. English speakers' attitudes toward Google's British, Indian, and American voices and Apple's Irish, Indian, South African, British, Australian, and American voices. Analysis reveals consistently lower fluency ratings for Irish, Indian, and South African voices (compared with American) but no consistent results of bias related to competence, warmth, or willingness to interact. Moreover, participants often misidentified voices' countries of origin but correctly identified them as artificial. We conclude that this overall lack of bias may be due to two possibilities: lack of humanlikeness of the voices and lack of availability of nonstandardized voices and voices from countries toward which those in the United States typically show bias. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results