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29,989 results on '"Saunders, A. M."'

1. Sensitivity of quantitative diffusion MRI tractography and microstructure to anisotropic spatial sampling

Author

McMaster, Elyssa M., Newlin, Nancy R., Cho, Chloe, Rudravaram, Gaurav, Saunders, Adam M., Krishnan, Aravind R., Remedios, Lucas W., Kim, Michael E., Xu, Hanliang, Schilling, Kurt G., Rheault, François, Cutting, Laurie E., and Landman, Bennett A.

Subjects
Electrical Engineering and Systems Science - Signal Processing
Abstract

Purpose: Diffusion weighted MRI (dMRI) and its models of neural structure provide insight into human brain organization and variations in white matter. A recent study by McMaster, et al. showed that complex graph measures of the connectome, the graphical representation of a tractogram, vary with spatial sampling changes, but biases introduced by anisotropic voxels in the process have not been well characterized. This study uses microstructural measures (fractional anisotropy and mean diffusivity) and white matter bundle properties (bundle volume, length, and surface area) to further understand the effect of anisotropic voxels on microstructure and tractography. Methods: The statistical significance of the selected measures derived from dMRI data were assessed by comparing three white matter bundles at different spatial resolutions with 44 subjects from the Human Connectome Project Young Adult dataset scan/rescan data using the Wilcoxon Signed Rank test. The original isotropic resolution (1.25 mm isotropic) was explored with six anisotropic resolutions with 0.25 mm incremental steps in the z dimension. Then, all generated resolutions were upsampled to 1.25 mm isotropic and 1 mm isotropic. Results: There were statistically significant differences between at least one microstructural and one bundle measure at every resolution (p less than or equal to 0.05, corrected for multiple comparisons). Cohen's d coefficient evaluated the effect size of anisotropic voxels on microstructure and tractography. Conclusion: Fractional anisotropy and mean diffusivity cannot be recovered with basic up sampling from low quality data with gold standard data. However, the bundle measures from tractogram become more repeatable when voxels are resampled to 1 mm isotropic.

Published
2024

2. Comparison and calibration of MP2RAGE quantitative T1 values to multi-TI inversion recovery T1 values

Author

Saunders, Adam M., Kim, Michael E., Gao, Chenyu, Remedios, Lucas W., Krishnan, Aravind R., Schilling, Kurt G., O'Grady, Kristin P., Smith, Seth A., and Landman, Bennett A.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing
Abstract

While typical qualitative T1-weighted magnetic resonance images reflect scanner and protocol differences, quantitative T1 mapping aims to measure T1 independent of these effects. Changes in T1 in the brain reflect chemical and physical changes in brain tissue, such as the demyelination of axons in multiple sclerosis. Magnetization-prepared two rapid acquisition gradient echo (MP2RAGE) is an acquisition protocol that allows for efficient T1 mapping with a much lower scan time per slice compared to multi-TI inversion recovery (IR) protocols. We collect and register B1-corrected MP2RAGE acquisitions with an additional inversion time (MP3RAGE) alongside multi-TI selective inversion recovery acquisitions for four subjects and find a tissue-dependent bias between the derived T1 values. We train a patch-based ResNet-18 to calibrate the MP3RAGE T1 values to the multi-TI IR T1 values, incorporating the standard deviation of T1 calculated from a Monte Carlo simulation as an additional channel. Across four folds, the error between the MP2RAGE and T1 maps varies substantially (RMSE in white matter: 0.30 +/- 0.01 seconds, subcortical gray matter: 0.26 +/- 0.02 seconds, cortical gray matter: 0.36 +/- 0.02 seconds). Our network reduces the RMSE significantly (RMSE in white matter: 0.11 +/- 0.02 seconds, subcortical gray matter: 0.10 +/- 0.02 seconds, cortical gray matter: 0.17 +/- 0.03 seconds). Adding the standard deviation channel does not substantially change the RMSE. Using limited paired training data from both sequences, we can reduce the error between quantitative imaging methods and calibrate to one of the protocols with a neural network., Comment: 20 pages, 10 figures. Submitted to Magnetic Resonance Imaging

Published
2024

3. Influence of Early through Late Fusion on Pancreas Segmentation from Imperfectly Registered Multimodal MRI

Author

Remedios, Lucas W., Liu, Han, Remedios, Samuel W., Zuo, Lianrui, Saunders, Adam M., Bao, Shunxing, Huo, Yuankai, Powers, Alvin C., Virostko, John, and Landman, Bennett A.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Multimodal fusion promises better pancreas segmentation. However, where to perform fusion in models is still an open question. It is unclear if there is a best location to fuse information when analyzing pairs of imperfectly aligned images. Two main alignment challenges in this pancreas segmentation study are 1) the pancreas is deformable and 2) breathing deforms the abdomen. Even after image registration, relevant deformations are often not corrected. We examine how early through late fusion impacts pancreas segmentation. We used 353 pairs of T2-weighted (T2w) and T1-weighted (T1w) abdominal MR images from 163 subjects with accompanying pancreas labels. We used image registration (deeds) to align the image pairs. We trained a collection of basic UNets with different fusion points, spanning from early to late, to assess how early through late fusion influenced segmentation performance on imperfectly aligned images. We assessed generalization of fusion points on nnUNet. The single-modality T2w baseline using a basic UNet model had a Dice score of 0.73, while the same baseline on the nnUNet model achieved 0.80. For the basic UNet, the best fusion approach occurred in the middle of the encoder (early/mid fusion), which led to a statistically significant improvement of 0.0125 on Dice score compared to the baseline. For the nnUNet, the best fusion approach was na\"ive image concatenation before the model (early fusion), which resulted in a statistically significant Dice score increase of 0.0021 compared to baseline. Fusion in specific blocks can improve performance, but the best blocks for fusion are model specific, and the gains are small. In imperfectly registered datasets, fusion is a nuanced problem, with the art of design remaining vital for uncovering potential insights. Future innovation is needed to better address fusion in cases of imperfect alignment of abdominal image pairs., Comment: 13.5 pages of manuscript content

Published
2024

4. Harmonized connectome resampling for variance in voxel sizes

Author

McMaster, Elyssa M., Newlin, Nancy R., Rudravaram, Gaurav, Saunders, Adam M., Krishnan, Aravind R., Remedios, Lucas W., Kim, Michael E., Xu, Hanliang, Archer, Derek B., Schilling, Kurt G., Rheault, François, Cutting, Laurie E., and Landman, Bennett A.

Subjects
Physics - Medical Physics, Electrical Engineering and Systems Science - Image and Video Processing, Electrical Engineering and Systems Science - Signal Processing
Abstract

To date, there has been no comprehensive study characterizing the effect of diffusion-weighted magnetic resonance imaging voxel resolution on the resulting connectome for high resolution subject data. Similarity in results improved with higher resolution, even after initial down-sampling. To ensure robust tractography and connectomes, resample data to 1 mm isotropic resolution.

Published
2024

5. Data-driven Nucleus Subclassification on Colon H&E using Style-transferred Digital Pathology

Author

Remedios, Lucas W., Bao, Shunxing, Remedios, Samuel W., Lee, Ho Hin, Cai, Leon Y., Li, Thomas, Deng, Ruining, Newlin, Nancy R., Saunders, Adam M., Cui, Can, Li, Jia, Liu, Qi, Lau, Ken S., Roland, Joseph T., Washington, Mary K, Coburn, Lori A., Wilson, Keith T., Huo, Yuankai, and Landman, Bennett A.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Understanding the way cells communicate, co-locate, and interrelate is essential to furthering our understanding of how the body functions. H&E is widely available, however, cell subtyping often requires expert knowledge and the use of specialized stains. To reduce the annotation burden, AI has been proposed for the classification of cells on H&E. For example, the recent Colon Nucleus Identification and Classification (CoNIC) Challenge focused on labeling 6 cell types on H&E of the colon. However, the CoNIC Challenge was unable to classify epithelial subtypes (progenitor, enteroendocrine, goblet), lymphocyte subtypes (B, helper T, cytotoxic T), and connective subtypes (fibroblasts). We use inter-modality learning to label previously un-labelable cell types on H&E. We take advantage of multiplexed immunofluorescence (MxIF) histology to label 14 cell subclasses. We performed style transfer on the same MxIF tissues to synthesize realistic virtual H&E which we paired with the MxIF-derived cell subclassification labels. We evaluated the efficacy of using a supervised learning scheme where the input was realistic-quality virtual H&E and the labels were MxIF-derived cell subclasses. We assessed our model on private virtual H&E and public real H&E. On virtual H&E, we were able to classify helper T cells and epithelial progenitors with positive predictive values of $0.34 \pm 0.15$ (prevalence $0.03 \pm 0.01$) and $0.47 \pm 0.1$ (prevalence $0.07 \pm 0.02$) respectively, when using ground truth centroid information. On real H&E we could classify helper T cells and epithelial progenitors with upper bound positive predictive values of $0.43 \pm 0.03$ (parent class prevalence 0.21) and $0.94 \pm 0.02$ (parent class prevalence 0.49) when using ground truth centroid information. This is the first work to provide cell type classification for helper T and epithelial progenitor nuclei on H&E., Comment: arXiv admin note: text overlap with arXiv:2401.05602

Published
2024

11. Super-resolution multi-contrast unbiased eye atlases with deep probabilistic refinement

Author

Lee, Ho Hin, Saunders, Adam M., Kim, Michael E., Remedios, Samuel W., Remedios, Lucas W., Tang, Yucheng, Yang, Qi, Yu, Xin, Bao, Shunxing, Cho, Chloe, Mawn, Louise A., Rex, Tonia S., Schey, Kevin L., Dewey, Blake E., Spraggins, Jeffrey M., Prince, Jerry L., Huo, Yuankai, and Landman, Bennett A.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Purpose: Eye morphology varies significantly across the population, especially for the orbit and optic nerve. These variations limit the feasibility and robustness of generalizing population-wise features of eye organs to an unbiased spatial reference. Approach: To tackle these limitations, we propose a process for creating high-resolution unbiased eye atlases. First, to restore spatial details from scans with a low through-plane resolution compared to a high in-plane resolution, we apply a deep learning-based super-resolution algorithm. Then, we generate an initial unbiased reference with an iterative metric-based registration using a small portion of subject scans. We register the remaining scans to this template and refine the template using an unsupervised deep probabilistic approach that generates a more expansive deformation field to enhance the organ boundary alignment. We demonstrate this framework using magnetic resonance images across four different tissue contrasts, generating four atlases in separate spatial alignments. Results: For each tissue contrast, we find a significant improvement using the Wilcoxon signed-rank test in the average Dice score across four labeled regions compared to a standard registration framework consisting of rigid, affine, and deformable transformations. These results highlight the effective alignment of eye organs and boundaries using our proposed process. Conclusions: By combining super-resolution preprocessing and deep probabilistic models, we address the challenge of generating an eye atlas to serve as a standardized reference across a largely variable population., Comment: Revised for submission to SPIE Journal of Medical Imaging. 26 pages, 6 figures

Published
2024

12. Single-cell, whole-embryo phenotyping of mammalian developmental disorders

Author

Huang, Xingfan, Henck, Jana, Qiu, Chengxiang, Sreenivasan, Varun KA, Balachandran, Saranya, Amarie, Oana V, Hrabě de Angelis, Martin, Behncke, Rose Yinghan, Chan, Wing-Lee, Despang, Alexandra, Dickel, Diane E, Duran, Madeleine, Feuchtinger, Annette, Fuchs, Helmut, Gailus-Durner, Valerie, Haag, Natja, Hägerling, Rene, Hansmeier, Nils, Hennig, Friederike, Marshall, Cooper, Rajderkar, Sudha, Ringel, Alessa, Robson, Michael, Saunders, Lauren M, da Silva-Buttkus, Patricia, Spielmann, Nadine, Srivatsan, Sanjay R, Ulferts, Sascha, Wittler, Lars, Zhu, Yiwen, Kalscheuer, Vera M, Ibrahim, Daniel M, Kurth, Ingo, Kornak, Uwe, Visel, Axel, Pennacchio, Len A, Beier, David R, Trapnell, Cole, Cao, Junyue, Shendure, Jay, and Spielmann, Malte

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Biomedical and Clinical Sciences, Genetics, Human Genome, 1.1 Normal biological development and functioning, Generic health relevance, Animals, Mice, Cell Nucleus, Developmental Disabilities, Embryo, Mammalian, Gain of Function Mutation, Genotype, Loss of Function Mutation, Models, Genetic, Mutation, Phenotype, Single-Cell Gene Expression Analysis, Disease Models, Animal, General Science & Technology
Abstract

Mouse models are a critical tool for studying human diseases, particularly developmental disorders1. However, conventional approaches for phenotyping may fail to detect subtle defects throughout the developing mouse2. Here we set out to establish single-cell RNA sequencing of the whole embryo as a scalable platform for the systematic phenotyping of mouse genetic models. We applied combinatorial indexing-based single-cell RNA sequencing3 to profile 101 embryos of 22 mutant and 4 wild-type genotypes at embryonic day 13.5, altogether profiling more than 1.6 million nuclei. The 22 mutants represent a range of anticipated phenotypic severities, from established multisystem disorders to deletions of individual regulatory regions4,5. We developed and applied several analytical frameworks for detecting differences in composition and/or gene expression across 52 cell types or trajectories. Some mutants exhibit changes in dozens of trajectories whereas others exhibit changes in only a few cell types. We also identify differences between widely used wild-type strains, compare phenotyping of gain- versus loss-of-function mutants and characterize deletions of topological associating domain boundaries. Notably, some changes are shared among mutants, suggesting that developmental pleiotropy might be 'decomposable' through further scaling of this approach. Overall, our findings show how single-cell profiling of whole embryos can enable the systematic molecular and cellular phenotypic characterization of mouse mutants with unprecedented breadth and resolution.

Published
2023

26. Cost Effectiveness of Letermovir for Cytomegalovirus Prophylaxis Compared with Pre-Emptive Therapy in Allogeneic Hematopoietic Stem Cell Transplant Recipients in the United States.

Author

Sepassi, Aryana, Saunders, Ila M, Bounthavong, Mark, Taplitz, Randy A, Logan, Cathy, and Watanabe, Jonathan H

Subjects
Regenerative Medicine, Transplantation, Comparative Effectiveness Research, Cost Effectiveness Research, Good Health and Well Being
Abstract

PurposeThe aim of this study was to assess the cost effectiveness of letermovir prophylaxis with the option for subsequent pre-emptive therapy (PET) for the prevention of cytomegalovirus (CMV) infection compared with a PET-only scenario in adult allogeneic hematopoietic stem cell transplant (allo-HCT) recipients in the United States over a 10-year time horizon.Materials and methodsA publicly available decision tree model was constructed using a commercial third-party payer perspective to simulate an allo-HCT recipient's clinical trajectory in the first-year post-transplant, followed by entry to a Markov model to simulate years 2 through 10. Clinical inputs and utility estimates were derived from published literature. Costs were derived from published literature and US Department of Veterans Affairs Federal Supply Schedule drug pricing. Outcomes assessed included life expectancy, quality-adjusted life-years (QALYs), direct medical costs, and the incremental cost-effectiveness ratio (ICER). One-way and probabilistic sensitivity analyses (PSA) were performed to test the robustness of the findings.ResultsCompared with PET alone, letermovir prophylaxis was projected to increase life-years per person (4.99 vs. 4.70 life-years), and increase QALYs (3.29 vs. 3.08) and costs (US$83.411 vs. US$70,698), yielding an ICER of US$59,356 per QALY gained. One-way sensitivity analyses indicated our model was sensitive to mortality (ICER: $164,771/QALY) and utility (letermovir ICER: $117,447/QALY; PET ICER: $107,290/QALY) in the first-year post-transplant. In 57.1% of the PSA simulations, letermovir was a cost-effective option using a willingness-to-pay threshold of US$100,000 per QALY.ConclusionsLetermovir prophylaxis is cost effective compared with PET alone with a willingness-to-pay threshold of US$100,000 per QALY gained. Sensitivity analysis results indicate future research is required to understand the impact of mortality and quality of life in the first-year post-transplant to arrive at a conclusive decision on letermovir adoption.

Published
2023

27. Simultaneous Bright- and Dark-Field X-ray Microscopy at X-ray Free Electron Lasers

Author

Dresselhaus-Marais, Leora E., Kozioziemski, Bernard, Holstad, Theodor S., Ræder, Trygve Magnus, Seaberg, Matthew, Nam, Daewoong, Kim, Sangsoo, Breckling, Sean, Choi, Seonghyuk, Chollet, Matthieu, Cook, Philip K., Folsom, Eric, Galtier, Eric, Gonzalez, Arnulfo, Gorhover, Tais, Guillet, Serge, Haldrup, Kristoffer, Howard, Marylesa, Katagiri, Kento, Kim, Seonghan, Kim, Sunam, Kim, Sungwon, Kim, Hyunjung, Knudsen, Erik Bergback, Kuschel, Stephan, Lee, Hae-Ja, Lin, Chuanlong, McWilliams, R. Stewart, Nagler, Bob, Nielsen, Martin Meedom, Ozaki, Norimasa, Pal, Dayeeta, Pedro, Ricardo Pablo, Saunders, Alison M., Schoofs, Frank, Sekine, Toshimori, Simons, Hugh, van Driel, Tim, Wang, Bihan, Yang, Wenge, Yildirim, Can, Poulsen, Henning Friis, and Eggert, Jon H.

Subjects
Condensed Matter - Materials Science
Abstract

The structures, strain fields, and defect distributions in solid materials underlie the mechanical and physical properties across numerous applications. Many modern microstructural microscopy tools characterize crystal grains, domains and defects required to map lattice distortions or deformation, but are limited to studies of the (near) surface. Generally speaking, such tools cannot probe the structural dynamics in a way that is representative of bulk behavior. Synchrotron X-ray diffraction based imaging has long mapped the deeply embedded structural elements, and with enhanced resolution, Dark Field X-ray Microscopy (DFXM) can now map those features with the requisite nm-resolution. However, these techniques still suffer from the required integration times due to limitations from the source and optics. This work extends DFXM to X-ray free electron lasers, showing how the $10^{12}$ photons per pulse available at these sources offer structural characterization down to 100 fs resolution (orders of magnitude faster than current synchrotron images). We introduce the XFEL DFXM setup with simultaneous bright field microscopy to probe density changes within the same volume. This work presents a comprehensive guide to the multi-modal ultrafast high-resolution X-ray microscope that we constructed and tested at two XFELs, and shows initial data demonstrating two timing strategies to study associated reversible or irreversible lattice dynamics.

Published
2022
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29. Slowed progression: The utility of Test to Treat initiatives in improving the neglected inequities of COVID-19 among racially/ethnically minoritized groups

Author

Abdul-Mutakabbir, Jacinda C, Tillman, Frank, Marcelin, Jasmine R, Saunders, Ila M, and Arya, Vibhuti

Subjects
Health Services and Systems, Health Sciences, Infectious Diseases, Infection, Good Health and Well Being, Humans, United States, COVID-19, Social Group, Pandemics, Vaccination, Pharmacology and Pharmaceutical Sciences, Public Health and Health Services, Pharmacology & Pharmacy, Health services and systems
Abstract

In the United States, coronavirus disease 2019 (COVID-19) has resulted in more than 95 million infections and 1 million deaths (as of September 2022), with individuals of racially/ethnically minoritized groups being disproportionately represented among these numbers. Despite the apparent pandemic fatigue in many communities, systemic and structural racism continue to place racially/ethnically minoritized groups at a disadvantage for overcoming the virus, especially as it relates to receiving vaccinations and COVID-19 targeted therapeutics. Test to Treat programs have the potential to mitigate these disparities by rapidly identifying the presence of a COVID-19 infection and readily offering treatment options. Nonetheless, Test to Treat programs must be optimized to adequately address the limitations to care within racially/ethnically minoritized communities.

Published
2023

30. Estimation of Kidney Function in Patients With Multiple Myeloma: Implications for Lenalidomide Dosing

Author

Salama, Engie, Lam, Stepfanie, Gonsalves, Wilson I, Tzachanis, Dimitrios, Momper, Jeremiah D, and Saunders, Ila M

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Clinical Research, Kidney Disease, Hematology, Renal and urogenital, Humans, Glomerular Filtration Rate, Lenalidomide, Creatinine, Multiple Myeloma, Renal Insufficiency, Chronic, Kidney, Body Weight, multiple myeloma, pharmacokinetics, lenalidomide, renal impairment, Pharmacology and Pharmaceutical Sciences, Pharmacology & Pharmacy, Clinical sciences, Pharmacology and pharmaceutical sciences
Abstract

BackgroundLenalidomide is an immunomodulatory drug used to treat multiple myeloma that requires renal dosing adjustment based on Cockcroft-Gault (CG). Various equations to estimate kidney function exist and pose a potential issue with lenalidomide dosing.ObjectiveThe objective of this analysis was to evaluate the impact of estimating kidney function in newly diagnosed multiple myeloma patients with CG, Modification of Diet in Renal Disease (MDRD), and Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) and their potential impact on lenalidomide dosing.MethodsData from 1121 multiple myeloma patients at the time of diagnosis acquired from the Mayo Clinic were used to calculate creatinine clearance (CrCl) using Cockcroft-Gault with actual body weight (CGABW), ideal body weight (CGIBW), or adjusted body weight (CGAdjBW); MDRD; and CKD-EPI for each subject. Discordances in dosing were then analyzed, and lenalidomide exposure was calculated for each subject to assess impact on pharmacokinetics of lenalidomide for patients who received discordant doses.ResultsOverall, approximately 16% of patients received a discordant dose when using MDRD or CKD-EPI instead of CGABW. The most common dose discordance was the decrease of a full dose of lenalidomide 25 mg when using CGABW down to 10 mg and when using MDRD or CKD-EPI with 53.8% to 55.6% of all discordances in this category. When assessing different body weights, the most common discordance was a decrease from 25 to 10 mg when using CGIBW instead of CGABW; the same trend was observed when using CGAdjBW instead as well. Patients were also at risk of over- or underexposure based on area under the concentration versus time curve (AUC) for discordant dosing.Conclusion and relevanceA significant proportion of patients are at risk of under- or overdose of lenalidomide if CKD-EPI or MDRD are used instead of CGABW. Physicians should use CGABW when estimating renal function to dose lenalidomide.

Published
2023

32. Cognitive and psychiatric symptom trajectories 2–3 years after hospital admission for COVID-19: a longitudinal, prospective cohort study in the UK

Author

Lone, Nazir, Baillie, Kenneth, Pairo-Castineira, Erola, Avramidis, Nikos, Wain, Louise, Guillen-Guio, Beatriz, Leavy, Olivia, Jones, S, Armstrong, Lisa, Hairsine, Brigid, Henson, Helen, Kurasz, Claire, Shaw, Alison, Shenton, Liz, Dobson, Hannah, Dell, Amanda, Fairbairn, Sara, Hawkings, Nancy, Haworth, Jill, Hoare, Michaela, Lewis, Victoria, Lucey, Alice, Mallison, Georgia, Nassa, Heeah, Pennington, Chris, Price, Andrea, Price, Claire, Storrie, Andrew, Willis, Gemma, Young, Susan, Poinasamy, Krisnah, Walker, Samantha, Jarrold, Ian, Rawlik, Konrad, Sanderson, Amy, Chong-James, K, David, C, James, W Y, Pfeffer, Paul, Zongo, O, Martineau, Adrian, Manisty, C, Armour, Cherie, Brown, Vanessa, Busby, John, Connolly, Bronwen, Craig, Thelma, Drain, Stephen, Heaney, Liam, King, Bernie, Magee, Nick, Major, E, McAulay, Danny, McGarvey, Lorcan, McGinness, Jade, Peto, Tunde, Stone, Roisin, Bolger, Annette, Davies, Ffyon, Haggar, Ahmed, Lewis, Joanne, Lloyd, Arwel, Manley, R, McIvor, Emma, Menzies, Daniel, Roberts, K, Saxon, W, Southern, David, Subbe, Christian, Whitehead, Victoria, Bularga, Anda, Mills, Nicholas, Dawson, Joy, El-Taweel, Hosni, Robinson, Leanne, Brear, Lucy, Regan, Karen, Saralaya, Dinesh, Storton, Kim, Amoils, Shannon, Bermperi, Areti, Cruz, Isabel, Dempsey, K, Elmer, Anne, Fuld, Jonathon, Jones, H, Jose, Sherly, Marciniak, Stefan, Parkes, M, Ribeiro, Carla, Taylor, Jessica, Toshner, Mark, Watson, L, Worsley, J, Broad, Lauren, Evans, Teriann, Haynes, Matthew, Jones, L, Knibbs, Lucy, McQueen, Alison, Oliver, Catherine, Paradowski, Kerry, Sabit, Ramsey, Williams, Jenny, Jones, Ian, Milligan, Lea, Harris, Edward, Sampson, Claire, Davies, Ellie, Evenden, Cerys, Hancock, Alyson, Hancock, Kia, Lynch, Ceri, Rees, Meryl, Roche, Lisa, Stroud, Natalie, Thomas-Woods, T, Heller, Simon, Chalder, Trudie, Shah, Kamini, Robertson, Elizabeth, Young, Bob, Babores, Marta, Holland, Maureen, Keenan, Natalie, Shashaa, Sharlene, Wassall, Helen, Austin, Liam, Beranova, Eva, Cosier, Tracey, Deery, Joanne, Hazelton, Tracy, Price, Carly, Ramos, Hazel, Solly, Reanne, Turney, Sharon, Weston, Heather, Coughlan, Eamon, Ralser, Markus, Pearce, Lorraine, Pugmire, S, Stoker, Wendy, Wilson, Ann, McCormick, W, Fraile, Eva, Ugoji, Jacinta, Aguilar Jimenez, Laura, Arbane, Gill, Betts, Sarah, Bisnauthsing, Karen, Dewar, A, Hart, Nicholas, Kaltsakas, G, Kerslake, Helen, Magtoto, Murphy, Marino, Philip, Martinez, L M, Ostermann, Marlies, Rossdale, Jennifer, Solano, Teresa, Alvarez Corral, Maria, Arias, Ava Maria, Bevan, Emily, Griffin, Denise, Martin, Jane, Owen, J, Payne, Sheila, Prabhu, A, Reed, Annabel, Storrar, Will, Williams, Nick, Wrey Brown, Caroline, Burdett, Tracy, Featherstone, James, Lawson, Cathy, Layton, Alison, Mills, Clare, Stephenson, Lorraine, Ellis, Yvette, Atkin, Paul, Brindle, K, Crooks, Michael, Drury, Katie, Easom, Nicholas, Flockton, Rachel, Holdsworth, L, Richards, A, Sykes, D L, Thackray-Nocera, Susannah, Wright, C, Coetzee, S, Davies, Kim, Hughes, Rachel Ann, Loosley, Ronda, McGuinness, Heather, Mohamed, Abdelrahman, O'Brien, Linda, Omar, Zohra, Perkins, Emma, Phipps, Janet, Ross, Gavin, Taylor, Abigail, Tench, Helen, Wolf-Roberts, Rebecca, Burden, L, Calvelo, Ellen, Card, Bethany, Carr, Caitlin, Chilvers, Edwin, Copeland, Donna, Cullinan, P, Daly, Patrick, Evison, Lynsey, Fayzan, Tamanah, Gordon, Hussain, Haq, Sulaimaan, Jenkins, Gisli, King, Clara, Kon, Onn Min, March, Katherine, Mariveles, Myril, McLeavey, Laura, Mohamed, Noura, Moriera, Silvia, Munawar, Unber, Nunag, Jose Lloyd, Nwanguma, Uchechi, Orriss-Dib, Lorna, Ross, Alexandra, Roy, Maura, Russell, Emily, Samuel, Katherine, Schronce, J, Simpson, Neil, Tarusan, Lawrence, Thomas, David, Wood, Chloe, Yasmin, Najira, Altmann, Danny, Howard, Luke, Johnston, Desmond, Lingford-Hughes, Anne, Man, William, Mitchell, Jane, Molyneaux, Philip, Nicolaou, Christos, O'Regan, D P, Price, L, Quint, Jenni, Smith, David, Thwaites, Ryan, Valabhji, Jonathon, Walsh, Simon, Efstathiou, Claudia, Liew, Felicity, Frankel, Anew, Lightstone, Liz, McAdoo, Steve, Wilkins, Martin, Willicombe, Michelle, Touyz, R, Guerdette, Anne-Marie, Hewitt, Melanie, Reddy, R, Warwick, Katie, White, Sonia, McMahon, Aisling, Adeyemi, Oluwaseun, Adrego, Rita, Assefa-Kebede, Hosanna, Breeze, Jonathon, Byrne, S, Dulawan, Pearl, Hoare, Amy, Jolley, Caroline, Knighton, Abigail, Patale, Sheetal, Peralta, Ida, Powell, Natassia, Ramos, Albert, Shevket, K, Speranza, Fabio, Te, Amelie, Malim, M, Bramham, Kate, Brown, M, Ismail, Khalida, Nicholson, Tim, Pariante, Carmen, Sharpe, Claire, Wessely, Simon, Whitney, J, Shah, Ajay, Chiribiri, A, O'Brien, C, Hayday, A, Ashworth, Andrew, Beirne, Paul, Clarke, Jude, Coupland, C, Dalton, Matthhew, Favager, Clair, Glossop, Jodie, Greenwood, John, Hall, Lucy, Hardy, Tim, Humphries, Amy, Murira, Jennifer, Peckham, Dan, Plein, S, Rangeley, Jade, Saalmink, Gwen, Tan, Ai Lyn, Wade, Elaine, Whittam, Beverley, Window, Nicola, Woods, Janet, Coakley, G, Turtle, Lance, Allerton, Lisa, Allt, Ann Marie, Beadsworth, M, Berridge, Anthony, Brown, Jo, Cooper, Shirley, Cross, Andy, Defres, Sylviane, Dobson, S L, Earley, Joanne, French, N, Greenhalf, William, Hainey, Kera, Hardwick, Hayley, Hawkes, Jenny, Highett, Victoria, Kaprowska, Sabina, Key, Angela, Lavelle-Langham, Lara, Lewis-Burke, N, Madzamba, Gladys, Malein, Flora, Marsh, Sophie, Mears, Chloe, Melling, Lucy, Noonan, Matthew, Poll, L, Pratt, James, Richardson, Emma, Rowe, Anna, Semple, Calum, Shaw, Victoria, Tripp, K A, Wajero, Lilian, Williams-Howard, S A, Wootton, Dan, Wyles, J, Diwanji, Shalin, Gurram, Sambasivarao, Papineni, Padmasayee, Quaid, Sheena, Tiongson, Gerlynn, Watson, Ekaterina, Briggs, Andrew, Marks, Michael, Hastie, Claire, Rogers, Natalie, Smith, Nikki, Stensel, David, Bishop, Lettie, McIvor, Katherine, Rivera-Ortega, Pilar, Al-Sheklly, Bashar, Avram, Cristina, Blaikely, John, Buch, M, Choudhury, N, Faluyi, David, Felton, T, Gorsuch, T, Hanley, Neil, Horsley, Alex, Hussell, Tracy, Kausar, 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McShane, H, Megson, Clare, Neubauer, Stefan, Nicoll, Debby, Ogg, G, Pacpaco, Edmund, Pavlides, M, Peng, Yanchun, Petousi, Nayia, Pimm, John, Rahman, Najib, Raman, Betty, Rowland, M J, Saunders, Kathryn, Sharpe, Michael, Talbot, Nick, Tunnicliffe, E M, Korszun, Ania, Kerr, Steven, Barker, R E, Cristiano, Daniele, Dormand, N, George, P, Gummadi, Mahitha, Kon, S, Liyanage, Kamal, Nolan, C M, Patel, B, Patel, Suhani, Polgar, Oliver, Shah, P, Singh, Suver, Walsh, J A, Gibbons, Michael, Ahmad, Shanaz, Brill, Simon, Hurst, John, Jarvis, Hannah, Lim, Lai, Mandal, S, Matila, Darwin, Olaosebikan, Olaoluwa, Singh, Claire, Laing, C, Baxendale, Helen, Garner, Lucie, Johnson, C, Mackie, J, Michael, Alice, Newman, J, Pack, Jamie, Paques, K, Parfrey, H, Parmar, J, Reddy, A, Halling-Brown, Mark, Dark, P, Diar-Bakerly, Nawar, Evans, D, Hardy, E, Harvey, Alice, Holgate, D, Knight, Sean, Mairs, N, Majeed, N, McMorrow, L, Oxton, J, Pendlebury, Jessica, Summersgill, C, Ugwuoke, R, Whittaker, S, Matimba-Mupaya, Wadzanai, Strong-Sheldrake, Sophia, Chowienczyk, Phillip, Bagshaw, J, Begum, M, Birchall, K, Butcher, Robyn, Carborn, H, Chan, Flora, Chapman, Kerry, Cheng, Yutung, Chetham, Luke, Clark, Cameron, Coburn, Zach, Cole, Joby, Dixon, Myles, Fairman, Alexandra, Finnigan, J, Foot, H, Foote, David, Ford, Amber, Gregory, Rebecca, Harrington, Kate, Haslam, L, Hesselden, L, Hockridge, J, Holbourn, Ailsa, Holroyd-Hind, B, Holt, L, Howell, Alice, Hurditch, E, Ilyas, F, Jarman, Claire, Lawrie, Allan, Lee, Ju Hee, Lee, Elvina, Lenagh, Rebecca, Lye, Alison, Macharia, Irene, Marshall, M, Mbuyisa, Angeline, McNeill, J, Megson, Sharon, Meiring, J, Milner, L, Misra, S, Newell, Helen, Newman, Tom, Norman, C, Nwafor, Lorenza, Pattenadk, Dibya, Plowright, Megan, Porter, Julie, Ravencroft, Phillip, Roddis, C, Rodger, J, Rowland-Jones, Sarah, Saunders, Peter, Sidebottom, J, Smith, Jacqui, Smith, Laurie, Steele, N, Stephens, G, Stimpson, R, Thamu, B, Thompson, A. A. Roger, Tinker, N, Turner, Kim, Turton, Helena, Wade, Phillip, Walker, S, Watson, James, Wilson, Imogen, Zawia, Amira, Allsop, Lynne, Bennett, Kaytie, Buckley, Phil, Flynn, Margaret, Gill, Mandy, Goodwin, Camelia, Greatorex, M, Gregory, Heidi, Heeley, Cheryl, Holloway, Leah, Holmes, Megan, Hutchinson, John, Kirk, Jill, Lovegrove, Wayne, Sewell, Terri Ann, Shelton, Sarah, Sissons, D, Slack, Katie, Smith, Susan, Sowter, D, Turner, Sarah, Whitworth, V, Wynter, Inez, Tomlinson, Johanne, Warburton, Louise, Painter, Sharon, Palmer, Sue, Redwood, Dawn, Tilley, Jo, Vickers, Carinna, Wainwright, Tania, Breen, G, Hotopf, M, Aul, Raminder, Forton, D, Ali, Mariam, Dunleavy, A, Mencias, Mark, Msimanga, N, Samakomva, T, Siddique, Sulman, Tavoukjian, Vera, Teixeira, J, Ahmed, Rubina, Francis, Richard, Connor, Lynda, Cook, Amanda, Davies, Gwyneth, Rees, Tabitha, Thaivalappil, Favas, Thomas, Caradog, McNarry, M, Williams, N, Lewis, Keir, Coulding, Martina, Jones, Heather, Kilroy, Susan, McCormick, Jacqueline, McIntosh, Jerome, Turner, Victoria, Vere, Joanne, Butt, Al-Tahoor, Savill, Heather, Kon, Samantha, Landers, G, Lota, Harpreet, Portukhay, Sofiya, Nasseri, Mariam, Daniels, Alison, Hormis, Anil, Ingham, Julie, Zeidan, Lisa, Chablani, Manish, Osborne, Lynn, Aslani, Shahab, Banerjee, Amita, Batterham, R, Baxter, Gabrielle, Bell, Robert, David, Anthony, Denneny, Emma, Hughes, Alun, Lilaonitkul, W, Mehta, P, Pakzad, Ashkan, Rangelov, Bojidar, Williams, B, Willoughby, James, Xu, Moucheng, Ahwireng, Nyarko, Bang, Dongchun, Basire, Donna, Brown, Jeremy, Chambers, Rachel, Checkley, A, Evans, R, Heightman, M, Hillman, T, Jacob, Joseph, Jastrub, Roman, Lipman, M, Logan, S, Lomas, D, Merida Morillas, Marta, Plant, Hannah, Porter, Joanna, Roy, K, Wall, E, Treibel, T, Ahmad Haider, N, Atkin, Catherine, Baggott, Rhiannon, Bates, Michelle, Botkai, A, Casey, Anna, Cooper, B, Dasgin, Joanne, Dawson, Camilla, Draxlbauer, Katharine, Gautam, N, Hazeldine, J, Hiwot, T, Holden, Sophie, Isaacs, Karen, Jackson, T, Kamwa, Vicky, Lewis, D, Lord, Janet, Madathil, S, McGee, C, Mcgee, K, Neal, Aoife, Newton-Cox, Alex, Nyaboko, Joseph, Parekh, Dhruv, Peterkin, Z, Qureshi, H, Ratcliffe, Liz, Sapey, Elizabeth, Short, J, Soulsby, Tracy, Stockley, J, Suleiman, Zehra, Thompson, Tamika, Ventura, Maximina, Walder, Sinead, Welch, Carly, Wilson, Daisy, Yasmin, S, Yip, Kay Por, Chaudhuri, N, Childs, Caroline, Djukanovic, R, Fletcher, S, Harvey, Matt, Jones, Mark, Marouzet, Elizabeth, Marshall, B, Samuel, Reena, Sass, T, Wallis, Tim, Wheeler, Helen, Steeds, R, Beckett, Paul, Dickens, Caroline, Nanda, Uttam, Aljaroof, M, Armstrong, Natalie, Arnold, H, Aung, Hnin, Bakali, Majda, Bakau, M, Baldry, E, Baldwin, Molly, Bourne, Charlotte, Bourne, Michelle, Brightling, Chris, Brunskill, Nigel, Cairns, P, Carr, Liesel, Charalambou, Amanda, Christie, C, Davies, Melanie, Daynes, Enya, Diver, Sarah, Dowling, Rachael, Edwards, Sarah, Edwardson, C, Elneima, Omer, Evans, H, Evans, Rachael, Finch, J, Glover, Sarah, Goodman, Nicola, Gooptu, Bibek, Greening, Neil, Hadley, Kate, Haldar, Pranab, Hargadon, Beverley, Harris, Victoria, Houchen-Wolloff, Linzy, Ibrahim, W, Ingram, L, Khunti, Kamlesh, Lea, A, Lee, D, McAuley, Hamish, McCann, Gerry, McCourt, P, Mcnally, Teresa, Mills, George, Monteiro, Will, Pareek, Manish, Parker, S, Prickett, Anne, Qureshi, I N, Rowland, A, Russell, Richard, Sereno, Marco, Shikotra, Aarti, Siddiqui, Salman, Singapuri, Ananga, Singh, Sally, Skeemer, J, Soares, M, Stringer, E, Thornton, T, Tobin, Martin, Ward, T J C, Woodhead, F, Yates, Tom, Yousuf, A J, Broome, Mattew, McArdle, Paul, Thickett, David, Upthegrove, Rachel, Wilkinson, Dan, Moss, Paul, Wraith, David, Evans, Jonathon, Bullmore, Ed, Heeney, Jonathon, Langenberg, Claudia, Schwaeble, William, Summers, Charlotte, Weir McCall, J, Adeloye, Davies, Newby, D E, Pius, Riinu, Rudan, Igor, Shankar-Hari, Manu, Sudlow, Catherine, Thorpe, Mat, Walmsley, Sarah, Zheng, Bang, Allan, Louise, Ballard, Clive, McGovern, Andrew, Dennis, J, Cavanagh, Jonathon, MacDonald, S, O'Donnell, Kate, Petrie, John, Sattar, Naveed, Spears, Mark, Guthrie, Elspeth, Henderson, Max, Allen, Richard, Bingham, Michelle, Brugha, Terry, Finney, Selina, Free, Rob, Jones, Don, Lawson, Claire, Lucy, Gardiner, Moss, Alistair, Mukaetova-Ladinska, Elizabeta, Novotny, Petr, Overton, Charlotte, Pearl, John, Plekhanova, Tatiana, Richardson, M, Samani, Nilesh, Sargant, Jack, Sharma, M, Steiner, Mike, Taylor, Chris, Terry, Sarah, Tong, C, Turner, E, Wormleighton, J, Zhao, Bang, Ntotsis, Kimon, Saunders, Ruth, Lozano-Rojas, Daniel, Goemans, Anne, Cuthbertson, D, Kemp, G, McArdle, Anne, Michael, Benedict, Reynolds, Will, Spencer, Lisa, Vinson, Ben, Ashworth, M, Abel, Kathryn, Chinoy, H, Deakin, Bill, Harvie, M, Miller, C A, Stanel, Stefan, Barran, Perdita, Trivedi, Drupad, McAllister-Williams, Hamish, Paddick, Stella-Maria, Rostron, Anthony, Taylor, John Paul, Baguley, David, Coleman, Chris, Cox, E, Fabbri, Laura, Francis, Susan, Hall, Ian, Hufton, E, Johnson, Simon, Khan, Fasih, Kitterick, Paaig, Morriss, Richard, Selby, Nick, Wright, Louise, Antoniades, Charalambos, Bates, A, Beggs, M, Bhui, Kamaldeep, Breeze, Katie, Channon, K M, Clark, David, Fu, X, Husain, Masud, Li, X, Lukaschuk, E, McCracken, Celeste, McGlynn, K, Menke, R, Motohashi, K, Nichols, T E, Ogbole, Godwin, Piechnik, S, Propescu, I, Propescu, J, Samat, A A, Sanders, Z B, Sigfrid, Louise, Webster, M, Kingham, Lucy, Klenerman, Paul, Lamlum, Hanan, Taquet, Maxime, Carson, G, Finnigan, L, Saunders, Laura, Wild, James, Calder, P C, Huneke, Nathan, Simons, Gemma, Baldwin, David, Bain, Steve, Daines, Luke, Bright, E, Crisp, P, Dharmagunawardena, Ruvini, Stern, M, Bailey, Elisabeth, Reddington, Anne, Wight, Andrew, Ashish, A, Cooper, Josh, Robinson, Emma, Broadley, Andrew, Barman, Laura, Brookes, Claire, Elliott, K, Griffiths, L, Guy, Zoe, Howard, Kate, Ionita, Diana, Redfearn, Heidi, Sarginson, Carol, Turnbull, Alison, Skorniewska, Zuzanna, De Deyn, Thomas, Hampshire, Adam, Trender, William R, Hellyer, Peter J, Chalmers, James D, Ho, Ling-Pei, Leavy, Olivia C, Richardson, Matthew, McAuley, Hamish J C, Singapuri, Amisha, Saunders, Ruth M, Harris, Victoria C, Greening, Neil J, Mansoori, Parisa, Harrison, Ewen M, Docherty, Annemarie B, Lone, Nazir I, Quint, Jennifer, Brightling, Christopher E, Wain, Louise V, Evans, Rachael A, Geddes, John R, and Harrison, Paul J

Published
2024
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36. Acute blood biomarker profiles predict cognitive deficits 6 and 12 months after COVID-19 hospitalization

Author

Taquet, Maxime, Skorniewska, Zuzanna, Hampshire, Adam, Chalmers, James D., Ho, Ling-Pei, Horsley, Alex, Marks, Michael, Poinasamy, Krisnah, Raman, Betty, Leavy, Olivia C., Richardson, Matthew, Elneima, Omer, McAuley, Hamish J. C., Shikotra, Aarti, Singapuri, Amisha, Sereno, Marco, Saunders, Ruth M., Harris, Victoria C., Houchen-Wolloff, Linzy, Greening, Neil J., Mansoori, Parisa, Harrison, Ewen M., Docherty, Annemarie B., Lone, Nazir I., Quint, Jennifer, Sattar, Naveed, Brightling, Christopher E., Wain, Louise V., Evans, Rachael E., Geddes, John R., and Harrison, Paul J.

Published
2023
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38. Interactions of laser-driven tin ejecta microjets over phase transition boundaries.

Author

Saunders, Alison M., Sun, Yuchen, Horwitz, Jeremy A. K., Ali, Suzanne J., Eggert, Jon H., Mackay, Kyle K., Morgan, Brandon E., Najjar, Fady M., Park, Hye-Sook, Ping, Yuan, and Pino, Jesse

Subjects
*PHASE transitions, *VERY light jets, *TIN, *PARTICLE interactions, *PHYSICS
Abstract

Ejecta microjets offer an experimental methodology to study high-speed particle laden-flow interactions, as microjets consist of millions of particulates traveling at velocities of several kilometers per second and are easily generated by most common shock drives. Previous experiments on the OMEGA Extended Performance laser found that collisions between two counter-propagating laser-driven tin ejecta microjets varied as a function of drive pressure; jets generated near shock pressures of 10 GPa passed through each other without interacting, whereas jets generated at shock pressures of over 100 GPa interacted strongly, forming a cloud around the center interaction point. In this paper, we present a more systematic scan of tin ejecta microjet collisions over intermediate pressure regimes to identify how and at what shock pressure interaction behavior onsets. Radiographs of interacting microjets at five different laser drive energies qualitatively demonstrate that interaction behavior onsets slowly as a function of laser drive energy. Quantitative mass and density metrics from each radiograph provide trends on jet characteristics and collisional mass dispersion. It is observed that jetting mass, jet densities, and mass dispersion increase with increasing drive pressures and that the increased jet density at the higher drive energies may account for the increased mass dispersion. This work provides an important step in the understanding of tin ejecta microjet collisions and paves the way for future studies on the physics dominating high-speed particle-laden flow interactions. [ABSTRACT FROM AUTHOR]

Published
2024
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40. Effects of financial toxicity on prescription drug use and mental well-being in cancer patients.

Author

Chan, Kelly, Sepassi, Aryana, Saunders, Ila M, Goodman, Aaron, and Watanabe, Jonathan H

Subjects
Cancer, Financial toxicity, Value-based pricing, Clinical Research, Rehabilitation, Good Health and Well Being
Abstract

BackgroundIn the US, medical costs for cancer patients have grown from $27 billion in 1990 to $174 billion in 2020. The increased financial strain that cancer patients and survivors endure is referred to as financial toxicity.ObjectiveTo quantify the relationship between indicators of financial toxicity and health utilization and quality of life in patients ever diagnosed with cancer.MethodsAdult cancer patients and survivors in 2017 were identified using the Medical Expenditure Panel Survey. Multiple logistic regression models were used to quantify the relationship between three financial toxicity exposures (concern for keeping an income, paying large medical bills, and going into debt or borrowing money) and two discrete outcomes of being able to purchase prescriptions and often worrying that cancer would worsen or come back.ResultsThis study assessed 609 respondents. After survey weighting was applied, that represented 16,215,673 individuals. Patients who reported concern for keeping an income were at 2.91 (95% Confidence Interval [CI], 1.16 to 7.31) and 2.97 (95% CI, 2.01 to 2.67) times increased odds to report avoiding purchase of prescriptions and worry of cancer status, respectively, versus those who did not. Patients who reported worry about paying large medical bills were at 4.46 (95% CI, 2.15 to 9.24) and 2.80 (95% CI, 1.98 to 3.96) times increased odds to report avoiding purchase of prescriptions and worry of cancer status, respectively, versus those who did not. Patients who reported borrowing money or going into debt were at 3.04 (95% CI, 1.19 to 7.76) and 2.42 (95% CI, 1.54 to 3.18) times increased odds to report avoiding purchase of prescriptions and worry of cancer status, respectively, versus those who did not.ConclusionsFinancial toxicity is associated with decreased prescription utilization and quality of life in the form of excessive worry among cancer patients including cancer survivors.

Published
2022

41. National Trends in Federal Student Loan Borrowing by Income Group and First-Generation Status. The AIR Professional File, Spring 2020. Article 148

Author

Association for Institutional Research (AIR), Chan, Monnica, Kwon, Jihye, Nguyen, David, Saunders, Katherine M., Shah, Nilkamal, and Smith, Katie N.

Abstract

Students are increasingly likely to use student loans to finance their postsecondary education. This article examines how students' use of federal loans changed from 2000 to 2016 by students' family income group and parental education level. We use logistic regression analysis and nationally representative data from the National Postsecondary Student Aid Study. We find that the odds of a student taking out a loan have converged over time across family income groups and across parental education levels, even after controlling for institutional sector and student demographic characteristics. Low-to-moderate-income students are now just as likely to borrow as are low-income students; likewise, continuing-generation college students are just as likely to borrow as are first-generation college students. Converging borrowing behavior across student groups has important implications for how we measure and benchmark college affordability. [This paper is from the 2018 NCES Data Institute.]

Published
2020

42. Effects of sleep disturbance on dyspnoea and impaired lung function following hospital admission due to COVID-19 in the UK: a prospective multicentre cohort study

Author

Jackson, C, Stewart, I D, Plekhanova, T, Cunningham, P S, Hazel, A L, Al-Sheklly, B, Aul, R, Bolton, C E, Chalder, T, Chalmers, J D, Chaudhuri, N, Docherty, A B, Donaldson, G, Edwardson, C L, Elneima, O, Greening, N J, Hanley, N A, Harris, V C, Harrison, E M, Ho, L-P, Houchen-Wolloff, L, Howard, L S, Jolley, C J, Jones, M G, Leavy, O C, Lewis, K E, Lone, N I, Marks, M, McAuley, H J C, McNarry, M A, Patel, B, Piper-Hanley, K, Poinasamy, K, Raman, B, Richardson, M, Rivera-Ortega, P, Rowland-Jones, S L, Rowlands, A V, Saunders, R M, Scott, J T, Sereno, M, Shah, A M, Shikotra, A, Singapuri, A, Stanel, S C, Thorpe, M, Wootton, D G, Yates, T, Jenkins, G, Singh, S J, Man, W D-C, Brightling, C E, Wain, L V, Porter, J C, Thompson, R, Horsley, A, Molyneaux, P L, Evans, R A, Jones, S E, Rutter, M K, Blaikley, J F, Abel, K, Adamali, H, Adeloye, D, Adeyemi, O, Adrego, R, Aguilar Jimenez, L A, Ahmad, S, Ahmad Haider, N, Ahmed, R, Ahwireng, N, Ainsworth, M, Alamoudi, A, Ali, M, Aljaroof, M, All, AM, Allan, L, Allen, R J, Allerton, L, Allsop, L, Almeida, P, Altmann, D, Alvarez Corral, M, Amoils, S, Anderson, D, Antoniades, C, Arbane, G, Arias, A, Armour, C, Armstrong, L, Armstrong, N, Arnold, D, Arnold, H, Ashish, A, Ashworth, A, Ashworth, M, Aslani, S, Assefa-Kebede, H, Atkin, C, Atkin, P, Aung, H, Austin, L, Avram, C, Ayoub, A, Babores, M, Baggott, R, Bagshaw, J, Baguley, D, Bailey, L, Baillie, J K, Bain, S, Bakali, M, Bakau, M, Baldry, E, Baldwin, D, Baldwin, M, Ballard, C, Banerjee, A, Bang, B, Barker, R E, Barman, L, Barratt, S, Barrett, F, Basire, D, Basu, N, Bates, M, Bates, A, Batterham, R, Baxendale, H, Bayes, H, Beadsworth, M, Beckett, P, Beggs, M, Begum, M, Beirne, P, Bell, D, Bell, R, Bennett, K, Beranova, E, Bermperi, A, Berridge, A, Berry, C, Betts, S, Bevan, E, Bhui, K, Bingham, M, Birchall, K, Bishop, L, Bisnauthsing, K, Blaikely, J, Bloss, A, Bolger, A, Bonnington, J, Botkai, A, Bourne, C, Bourne, M, Bramham, K, Brear, L, Breen, G, Breeze, J, Briggs, A, Bright, E, Brill, S, Brindle, K, Broad, L, Broadley, A, Brookes, C, Broome, M, Brown, A, Brown, J, Brown, J S, Brown, M, Brown, V, Brugha, T, Brunskill, N, Buch, M, Buckley, P, Bularga, A, Bullmore, E, Burden, L, Burdett, T, Burn, D, Burns, G, Burns, A, Busby, J, Butcher, R, Butt, A, Byrne, S, Cairns, P, Calder, P C, Calvelo, E, Carborn, H, Card, B, Carr, C, Carr, L, Carson, G, Carter, P, Casey, A, Cassar, M, Cavanagh, J, Chablani, M, Chambers, R C, Chan, F, Channon, K M, Chapman, K, Charalambou, A, Checkley, A, Chen, J, Cheng, Y, Chetham, L, Childs, C, Chilvers, E R, Chinoy, H, Chiribiri, A, Chong-James, K, Choudhury, G, Choudhury, N, Chowienczyk, P, Christie, C, Chrystal, M, Clark, D, Clark, C, Clarke, J, Clohisey, S, Coakley, G, Coburn, Z, Coetzee, S, Cole, J, Coleman, C, Conneh, F, Connell, D, Connolly, B, Connor, L, Cook, A, Cooper, B, Cooper, J, Cooper, S, Copeland, D, Cosier, T, Coulding, M, Coupland, C, Cox, E, Craig, T, Crisp, P, Cristiano, D, Crooks, M G, Cross, A, Cruz, I, Cullinan, P, 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2023
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49. Simultaneous bright- and dark-field X-ray microscopy at X-ray free electron lasers

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Dresselhaus-Marais, Leora E., Kozioziemski, Bernard, Holstad, Theodor S., Ræder, Trygve Magnus, Seaberg, Matthew, Nam, Daewoong, Kim, Sangsoo, Breckling, Sean, Choi, Sungwook, Chollet, Matthieu, Cook, Philip K., Folsom, Eric, Galtier, Eric, Gonzalez, Arnulfo, Gorkhover, Tais, Guillet, Serge, Haldrup, Kristoffer, Howard, Marylesa, Katagiri, Kento, Kim, Seonghan, Kim, Sunam, Kim, Sungwon, Kim, Hyunjung, Knudsen, Erik Bergbäck, Kuschel, Stephan, Lee, Hae Ja, Lin, Chuanlong, McWilliams, R. Stewart, Nagler, Bob, Nielsen, Martin Meedom, Ozaki, Norimasa, Pal, Dayeeta, Pablo Pedro, Ricardo, Saunders, Alison M., Schoofs, Frank, Sekine, Toshimori, Simons, Hugh, van Driel, Tim, Wang, Bihan, Yang, Wenge, Yildirim, Can, Poulsen, Henning Friis, and Eggert, Jon H.

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2023
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