Author: "Andrea Edwards" / Language: english - Searchworks@Jio Institute Digital Library Search Results

31 results on '"Andrea Edwards"'

1. Quitting smoking after a cancer diagnosis is associated with high-risk neutrophil-to-lymphocyte ratio among tobacco use-related cancer survivors

Author: You Lu, Katherine Kwong, James Wells, Andrea Edwards, Zhong Chen, Tung-Sung Tseng, and Kun Zhang
Subjects: Medicine, Science
Abstract: Abstract Quitting smoking could potentially minimize the risk of a high neutrophil-to-lymphocyte ratio (NLR) among tobacco use-related (TUR) cancer survivors. A total of 1263 TUR cancer survivors aged 20 to 85 years old were investigated using data from the National Health and Nutritional Examination Survey 1999–2018. The primary outcome was the NLR, which was defined as having two levels: high-risk (≥ 3) and low-risk (
Published: 2023
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2. Influence of CYP2D6 metabolizer status on ondansetron efficacy in pediatric patients undergoing hematopoietic stem cell transplantation: A case series

Author: Andrea Edwards, Ashley Teusink‐Cross, Lisa J. Martin, Cynthia A. Prows, Parinda A. Mehta, and Laura B. Ramsey
Subjects: Therapeutics. Pharmacology, RM1-950, Public aspects of medicine, RA1-1270
Abstract: Abstract Chemotherapy‐induced nausea and vomiting (CINV) is commonly experienced by patients receiving antineoplastic agents prior to hemopoietic stem cell transplant (HSCT). Ondansetron, a 5‐HT3 antagonist metabolized by CYP2D6, is an antiemetic prescribed to treat short‐term CINV, but some patients still experience uncontrolled nausea and vomiting while taking ondansetron. Adult CYP2D6 ultrarapid metabolizers (UMs) are at higher risk for CINV due to rapid ondansetron clearance, but similar studies have not been performed in pediatric patients. We performed a retrospective chart review of 128 pediatric HSCT recipients who received ondansetron for CINV prevention and had CYP2D6 genotyping for 20 alleles and duplication detection. The number of emetic episodes for each patient was collected from the start of chemotherapy through 7 days after HSCT. The average age of the cohort was 6.6 years (range: 0.2–16.7) and included three UMs, 72 normal metabolizers, 47 intermediate metabolizers, and six poor metabolizers. Because UMs are the population at risk for inefficacy, we describe the course of treatment for these three patients, as well as the factors influencing emesis: chemotherapy emetogenicity, diagnosis, and duration of ondansetron administration. The cases described support guidelines recommending non‐CYP2D6 metabolized antiemetics (e.g., granisetron) when a patient is a known CYP2D6 UM, but pediatric studies with a larger sample of CYP2D6 UMs are needed to validate our findings.
Published: 2022
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3. Cot-side imaging of functional connectivity in the developing brain during sleep using wearable high-density diffuse optical tomography

Author: Julie Uchitel, Borja Blanco, Liam Collins-Jones, Andrea Edwards, Emma Porter, Kelle Pammenter, Jem Hebden, Robert J Cooper, and Topun Austin
Subjects: High-density diffuse optical tomography, HD-DOT, Cot-side neuroimaging, Newborn neuroimaging, Resting state functional connectivity, Functional connectivity, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract: Studies of cortical function in newborn infants in clinical settings are extremely challenging to undertake with traditional neuroimaging approaches. Partly in response to this challenge, functional near-infrared spectroscopy (fNIRS) has become an increasingly common clinical research tool but has significant limitations including a low spatial resolution and poor depth specificity. Moreover, the bulky optical fibres required in traditional fNIRS approaches present significant mechanical challenges, particularly for the study of vulnerable newborn infants. A new generation of wearable, modular, high-density diffuse optical tomography (HD-DOT) technologies has recently emerged that overcomes many of the limitations of traditional, fibre-based and low-density fNIRS measurements. Driven by the development of this new technology, we have undertaken the first cot-side study of newborn infants using wearable HD-DOT in a clinical setting. We use this technology to study functional brain connectivity (FC) in newborn infants during sleep and assess the effect of neonatal sleep states, active sleep (AS) and quiet sleep (QS), on resting state FC. Our results demonstrate that it is now possible to obtain high-quality functional images of the neonatal brain in the clinical setting with few constraints. Our results also suggest that sleep states differentially affect FC in the neonatal brain, consistent with prior reports.
Published: 2023
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4. Trajectory of post-traumatic stress and depression among children and adolescents following single-incident trauma

Author: Joyce Zhang, Richard Meiser-Stedman, Bobby Jones, Patrick Smith, Tim Dalgleish, Adrian Boyle, Andrea Edwards, Devasena Subramanyam, Clare Dixon, Lysandra Sinclaire-Harding, Susanne Schweizer, Jill Newby, and Anna McKinnon
Subjects: ptsd, depression, comorbidity, trajectory, gbtm, lcga, computational phenotyping, Psychiatry, RC435-571
Abstract: Objective Post-traumatic stress disorder and depression have high comorbidity. Understanding their relationship is of clinical and theoretical importance. A comprehensive way to understand post-trauma psychopathology is through symptom trajectories. This study aims to look at the developmental courses of PTSD and depression symptoms and their interrelationship in the initial months post-trauma in children and adolescents. Methods Two-hundred-and-seventeen children and adolescents aged between eight and 17 exposed to single-event trauma were included in the study. Post-traumatic stress symptoms (PTSS) and depression symptoms were measured at 2 weeks, 2 months and 9 months, with further psychological variables measured at the 2-week assessment. Group-based trajectory modelling (GBTM) was applied to estimate the latent developmental clusters of the two outcomes. Logistic regression was used to identify predictors associated with high symptom groups. Results The GBTM yielded a three-group model for PTSS and a three-group model for depression. PTSS trajectories showed symptoms reduced to a non-clinical level by 9 months for all participants (if they were not already in the non-clinical range): participants were observed to be resilient (42.4%) or recovered within 2 months (35.6%), while 21.9% experienced high level PTSS but recovered by 9 months post-trauma. The depression symptom trajectories predicted a chronic non-recovery group (20.1%) and two mild symptom groups (45.9%, 34.0%). Further analysis showed high synchronicity between PTSS and depression groups. Peri-event panic, negative appraisals, rumination and thought suppression at 2 weeks predicted slow recovery from PTSS. Pre-trauma wellbeing, post-trauma anxiety and negative appraisals predicted chronic depression. Conclusions Post-trauma depression was more persistent than PTSS at 9 months in the sampled population. Cognitive appraisal was the shared risk factor to high symptom groups of both PTSS and depression.
Published: 2022
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5. Insufficient Lycopene Intake Is Associated With High Risk of Prostate Cancer: A Cross-Sectional Study From the National Health and Nutrition Examination Survey (2003–2010)

Author: You Lu, Andrea Edwards, Zhong Chen, Tung-Sung Tseng, Mirandy Li, Gabrielle V. Gonzalez, and Kun Zhang
Subjects: prostate cancer, PSA, lycopene, obesity, living status, Public aspects of medicine, RA1-1270
Abstract: Although lycopene intake and risk of prostate cancer have been explored for decades, recent studies show that Non-Hispanic Black Prostate Cancer (PCa) patients benefit less than Non-Hispanic White patients from a lycopene intake intervention program. This study examined whether a lycopene intake-related racial disparity exists in reducing the risk of PCa in healthy adults. Data on healthy, cancer-free Non-Hispanic Black (NHB) men (n = 159) and Non-Hispanic White (NHW) men (n = 478) from the 2003 to 2010 NHANES dataset were analyzed. Total lycopene intake from daily diet, age, living status, race/ethnicity, education level, poverty income ratio, body mass index, and smoking status were studied as independent variables. The combination of total Prostate-Specific Antigen (PSA) level and the ratio of free PSA was set as criteria for evaluating the risk of PCa. Multivariable logistic regression was used in race-stratified analyses to compute odds ratios (OR) and 95% confidence intervals (95% CI) comparing high PCa risk with low PCa risk. We found, in the whole population, race/ethnicity was the only factor that influenced lycopene intake from the daily diet. NHB men consumed less lycopene than NHW men (3,716 vs. 6,487 (mcg), p = 0.01). Sufficient lycopene intake could reduce the risk of PCa (OR: 0.40, 95% CI: 0.18–0.85, p = 0.02). Men aged between 66 and 70 had high PCa risk (OR: 3.32, 95% CI: 1.12–9.85, p = 0.03). Obesity served as a protective factor against the high risk of PCa (OR: 0.25, 95% CI: 0.12–0.54, p = 0.001). NHW men aged between 66 and 70 had a high risk of PCa (OR: 4.01, 95% CI: 1.02–15.73, p = 0.05). Obese NHW men also had lower risk of PCa (OR: 0.18, 95% CI: 0.07–0.47 p = 0.001). NHB men had a high risk of PCa compared to NHW men (OR: 2.27, 95% CI: 1.35–3.81 p = 0.004). NHB men who were living without partners experienced an even higher risk of PCa (OR: 3.35, 95% CI: 1.01–11.19 p = 0.07). Sufficient lycopene intake from daily food could serve as a protector against PCa. Such an association was only observed in NHW men. Further studies are needed to explore the dose-response relationship between lycopene intake and the association of PCa risk in NHB men.
Published: 2021
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6. Seeking the exclusive binding region of phenylalkylamine derivatives on human T‐type calcium channels via homology modeling and molecular dynamics simulation approach

Author: You Lu, Ming Li, Gi Young Lee, Na Zhao, Zhong Chen, Andrea Edwards, and Kun Zhang
Subjects: homology modeling, phenylalkylamine, selective binding, T‐type calcium channels, virtual drug screening, Therapeutics. Pharmacology, RM1-950
Abstract: Abstract Pharmaceutical features of phenylalkylamine derivatives (PAAs) binding to calcium channels have been studied extensively in the past decades. Only a few PAAs have the binding specificity on calcium channels, for example, NNC 55‐0396. Here, we created the homology models of human Cav3.2, Cav3.3 and use them as a receptor on the rigid docking tests. The nonspecific calcium channel blocker mibefradil showed inconsistent docking preference across four domains; however, NNC 55‐0396 had a unique binding pattern on domain II specifically. The subsequent molecular dynamics (MD) simulations identified that Cav3.1, Cav3.2, and Cav3.3 share domain II when Ca2+ appearing in the neighbor region of selective filters (SFs). Moreover, free‐energy perturbation analysis suggests single mutation of lysine at P‐loop domain III, or threonine at the P‐loop domain II largely reduced the total amount of hydration‐free energy in the system. All these findings suggest that P‐loop and segment six domain II in the T‐type calcium channels (TCCs) are crucial for attracting the PAAs with specificity as the antagonist.
Published: 2021
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7. Functional imaging of the developing brain with wearable high-density diffuse optical tomography: A new benchmark for infant neuroimaging outside the scanner environment

Author: Elisabetta Maria Frijia, Addison Billing, Sarah Lloyd-Fox, Ernesto Vidal Rosas, Liam Collins-Jones, Maria Magdalena Crespo-Llado, Marta Perapoch Amadó, Topun Austin, Andrea Edwards, Luke Dunne, Greg Smith, Reuben Nixon-Hill, Samuel Powell, Nicholas L. Everdell, and Robert J. Cooper
Subjects: High-Density Diffuse Optical Tomography, Functional Near-Infrared Spectroscopy, Optical Neuroimaging, Infant Cognitive Development, Infant Neuroimaging, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract: Studies of cortical function in the awake infant are extremely challenging to undertake with traditional neuroimaging approaches. Partly in response to this challenge, functional near-infrared spectroscopy (fNIRS) has become increasingly common in developmental neuroscience, but has significant limitations including resolution, spatial specificity and ergonomics. In adults, high-density arrays of near-infrared sources and detectors have recently been shown to yield dramatic improvements in spatial resolution and specificity when compared to typical fNIRS approaches. However, most existing fNIRS devices only permit the acquisition of ~20–100 sparsely distributed fNIRS channels, and increasing the number of optodes presents significant mechanical challenges, particularly for infant applications. A new generation of wearable, modular, high-density diffuse optical tomography (HD-DOT) technologies has recently emerged that overcomes many of the limitations of traditional, fibre-based and low-density fNIRS measurements. Driven by the development of this new technology, we have undertaken the first study of the infant brain using wearable HD-DOT. Using a well-established social stimulus paradigm, and combining this new imaging technology with advances in cap design and spatial registration, we show that it is now possible to obtain high-quality, functional images of the infant brain with minimal constraints on either the environment or on the infant participants. Our results are consistent with prior low-density fNIRS measures based on similar paradigms, but demonstrate superior spatial localization, improved depth specificity, higher SNR and a dramatic improvement in the consistency of the responses across participants. Our data retention rates also demonstrate that this new generation of wearable technology is well tolerated by the infant population.
Published: 2021
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8. Inferring Personalized and Race-Specific Causal Effects of Genomic Aberrations on Gleason Scores: A Deep Latent Variable Model

Author: Zhong Chen, Andrea Edwards, Chindo Hicks, and Kun Zhang
Subjects: deep latent variable model, causal effect inference, prostate cancer, racial disparity, genomic aberrations, Gleason scores, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract: Extensive research has examined socioeconomic factors influencing prostate cancer (PCa) disparities. However, to what extent molecular and genetic mechanisms may also contribute to these inequalities still remains elusive. Although various in vitro, in vivo, and population studies have originated to address this issue, they are often very costly and time-consuming by nature. In this work, we attempt to explore this problem by a preliminary study, where a joint deep latent variable model (DLVM) is proposed to in silico quantify the personalized and race-specific effects that a genomic aberration may exert on the Gleason Score (GS) of each individual PCa patient. The core of the proposed model is a deep variational autoencoder (VAE) framework, which follows the causal structure of inference with proxies. Extensive experimental results on The Cancer Genome Atlas (TCGA) 270 European-American (EA) and 43 African-American (AA) PCa patients demonstrate that ERG fusions, somatic mutations in SPOP and ATM, and copy number alterations (CNAs) in ERG are the statistically significant genomic factors across all low-, intermediate-, and high-grade PCa that may explain the disparities between these two groups. Moreover, compared to a state-of-the-art deep inference method, our proposed method achieves much higher precision in causal effect inference in terms of the impact of a studied genomic aberration on GS. Further validation on an independent set and the assessment of the genomic-risk scores along with corresponding confidence intervals not only validate our results but also provide valuable insight to the observed racial disparity between these two groups regarding PCa metastasis. The pinpointed significant genomic factors may shed light on the molecular mechanism of cancer disparities in PCa and warrant further investigation.
Published: 2020
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9. Identification of Single Nucleotide Polymorphism in Red Clover (Trifolium pratense L.) Using Targeted Genomic Amplicon Sequencing and RNA-seq

Author: Wenli Li, Heathcliffe Riday, Christina Riehle, Andrea Edwards, and Randy Dinkins
Subjects: red clover, single-nucleotide polymorphism, targeted amplicon sequencing, RNA-seq, self-incompatibility, Plant culture, SB1-1110
Abstract: Red clover (Trifolium pratense L.) is a diploid, naturally cross-pollinated, cool-season species. As a perennial forage legume, red clover is mostly cultivated in temperate regions worldwide. Being a non-model crop species, genomic resources for red clover have been underdeveloped. Thus far, genomic analysis used in red clover has mainly relied on simple sequence repeat (SSR) markers. However, SSR markers are sparse in the genome and it is often difficult to unambiguously map them using short reads generated by next generation sequencing technology. Single nucleotide polymorphisms (SNPs) have been successfully applied in genomics assisted breeding in several agriculturally important species. Due to increasing importance of legumes in forage production, there is a clear need to develop SNP based markers for red clover that can be applied in breeding applications. In this study, we first developed an analytical pipeline that can confidently identify SNPs in a set of 72 different red clover genotypes using sequences generated by targeted amplicon sequencing. Then, with the same filtering stringency used in this pipeline, we used sequences from publicly available RNA-seq data to identify confident SNPs in different red clover varieties. Using this strategy, we have identified a total of 69,975 SNPs across red clover varieties. Among these, 28% (19,116) of them are missense mutations. Using Medicago truncatula as the reference, we annotated the regions affected by these missense mutations. We identified 2,909 protein coding regions with missense mutations. Pathway analysis of these coding regions indicated several biological processes impacted by these mutations. Specifically, three domains (homeobox domain, pentatricopeptide repeat containing plant-like, and regulator of Vps4 activity) were identified with five or more missense SNPs. These domain might also be a functional contributor in the molecular mechanisms of self-incompatibility in red clover. Future in-depth sequence diversity analysis of these three genes may yield valuable insights into the molecular mechanism involved in self-incompatibility in red clover.
Published: 2019
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10. Significant Prognostic Features and Patterns of Somatic Mutations in Human Cancers

Author: Wensheng Zhang, Andrea Edwards, Erik K Flemington, and Kun Zhang
Subjects: Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract: TP53 is the most frequently altered gene in human cancers. Numerous retrospective studies have related its mutation and abnormal p53 protein expression to poor patient survival. Nonetheless, the clinical significance of TP53 ( p53 ) status has been a controversial issue. In this work, we aimed to characterize TP53 somatic mutations in tumor cells across multiple cancer types, primarily focusing on several less investigated features of the mutation spectra, and determine their prognostic implications. We performed an integrative study on the clinically annotated genomic data released by The Cancer Genome Atlas. Standard statistical methods, such as the Cox proportional hazards model and logistic regression, were used. This study resulted in several novel findings. They include the following: (1) similar to previously reported cases in breast cancer, the mutations in exons 1 to 4 of TP53 were more lethal than those in exons 5 to 9 for the patients with lung adenocarcinomas; (2) TP53 mutants tended to be negatively selected in mammalian evolution, but the evolutionary conservation had various clinical implications for different cancers; (3) conserved correlation patterns (ie, consistent co-occurrence or consistent mutual exclusivity) between TP53 mutations and the alterations in several other cancer genes (ie, PIK3CA, PTEN, KRAS, APC, CDKN2A , and ATM ) were present in several cancers in which prognosis was associated with TP53 status and/or the mutational characteristics; (4) among TP53 -mutated tumors, the total mutation burden in other driver genes was a predictive signature ( P < .05, false discovery rate
Published: 2017
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11. Mapping cortical haemodynamics during neonatal seizures using diffuse optical tomography: A case study

Author: Harsimrat Singh, Robert J. Cooper, Chuen Wai Lee, Laura Dempsey, Andrea Edwards, Sabrina Brigadoi, Dimitrios Airantzis, Nick Everdell, Andrew Michell, David Holder, Jeremy C. Hebden, and Topun Austin
Subjects: Diffuse optical tomography (DOT), Neonatal seizures, Functional near infrared spectroscopy (fNIRS), Hypoxic–ischaemic encephalopathy (HIE), Computer applications to medicine. Medical informatics, R858-859.7, Neurology. Diseases of the nervous system, RC346-429
Abstract: Seizures in the newborn brain represent a major challenge to neonatal medicine. Neonatal seizures are poorly classified, under-diagnosed, difficult to treat and are associated with poor neurodevelopmental outcome. Video-EEG is the current gold-standard approach for seizure detection and monitoring. Interpreting neonatal EEG requires expertise and the impact of seizures on the developing brain remains poorly understood. In this case study we present the first ever images of the haemodynamic impact of seizures on the human infant brain, obtained using simultaneous diffuse optical tomography (DOT) and video-EEG with whole-scalp coverage. Seven discrete periods of ictal electrographic activity were observed during a 60 minute recording of an infant with hypoxic–ischaemic encephalopathy. The resulting DOT images show a remarkably consistent, high-amplitude, biphasic pattern of changes in cortical blood volume and oxygenation in response to each electrographic event. While there is spatial variation across the cortex, the dominant haemodynamic response to seizure activity consists of an initial increase in cortical blood volume prior to a large and extended decrease typically lasting several minutes. This case study demonstrates the wealth of physiologically and clinically relevant information that DOT–EEG techniques can yield. The consistency and scale of the haemodynamic responses observed here also suggest that DOT–EEG has the potential to provide improved detection of neonatal seizures.
Published: 2014
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12. Somatic mutations favorable to patient survival are predominant in ovarian carcinomas.

Author: Wensheng Zhang, Andrea Edwards, Erik Flemington, and Kun Zhang
Subjects: Medicine, Science
Abstract: Somatic mutation accumulation is a major cause of abnormal cell growth. However, some mutations in cancer cells may be deleterious to the survival and proliferation of the cancer cells, thus offering a protective effect to the patients. We investigated this hypothesis via a unique analysis of the clinical and somatic mutation datasets of ovarian carcinomas published by the Cancer Genome Atlas. We defined and screened 562 macro mutation signatures (MMSs) for their associations with the overall survival of 320 ovarian cancer patients. Each MMS measures the number of mutations present on the member genes (except for TP53) covered by a specific Gene Ontology (GO) term in each tumor. We found that somatic mutations favorable to the patient survival are predominant in ovarian carcinomas compared to those indicating poor clinical outcomes. Specially, we identified 19 (3) predictive MMSs that are, usually by a nonlinear dose-dependent effect, associated with good (poor) patient survival. The false discovery rate for the 19 "positive" predictors is at the level of 0.15. The GO terms corresponding to these MMSs include "lysosomal membrane" and "response to hypoxia", each of which is relevant to the progression and therapy of cancer. Using these MMSs as features, we established a classification tree model which can effectively partition the training samples into three prognosis groups regarding the survival time. We validated this model on an independent dataset of the same disease (Log-rank p-value < 2.3 × 10(-4)) and a dataset of breast cancer (Log-rank p-value < 9.3 × 10(-3)). We compared the GO terms corresponding to these MMSs and those enriched with expression-based predictive genes. The analysis showed that the GO term pairs with large similarity are mainly pertinent to the proteins located on the cell organelles responsible for material transport and waste disposal, suggesting the crucial role of these proteins in cancer mortality.
Published: 2014
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13. Inferring polymorphism-induced regulatory gene networks active in human lymphocyte cell lines by weighted linear mixed model analysis of multiple RNA-Seq datasets.

Author: Wensheng Zhang, Andrea Edwards, Erik K Flemington, and Kun Zhang
Subjects: Medicine, Science
Abstract: Single-nucleotide polymorphisms (SNPs) contribute to the between-individual expression variation of many genes. A regulatory (trait-associated) SNP is usually located near or within a (host) gene, possibly influencing the gene's transcription or/and post-transcriptional modification. But its targets may also include genes that are physically farther away from it. A heuristic explanation of such multiple-target interferences is that the host gene transfers the SNP genotypic effects to the distant gene(s) by a transcriptional or signaling cascade. These connections between the host genes (regulators) and the distant genes (targets) make the genetic analysis of gene expression traits a promising approach for identifying unknown regulatory relationships. In this study, through a mixed model analysis of multi-source digital expression profiling for 140 human lymphocyte cell lines (LCLs) and the genotypes distributed by the international HapMap project, we identified 45 thousands of potential SNP-induced regulatory relationships among genes (the significance level for the underlying associations between expression traits and SNP genotypes was set at FDR < 0.01). We grouped the identified relationships into four classes (paradigms) according to the two different mechanisms by which the regulatory SNPs affect their cis- and trans- regulated genes, modifying mRNA level or altering transcript splicing patterns. We further organized the relationships in each class into a set of network modules with the cis- regulated genes as hubs. We found that the target genes in a network module were often characterized by significant functional similarity, and the distributions of the target genes in three out of the four networks roughly resemble a power-law, a typical pattern of gene networks obtained from mutation experiments. By two case studies, we also demonstrated that significant biological insights can be inferred from the identified network modules.
Published: 2013
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14. The sequence structures of human microRNA molecules and their implications.

Author: Zhide Fang, Ruofei Du, Andrea Edwards, Erik K Flemington, and Kun Zhang
Subjects: Medicine, Science
Abstract: The count of the nucleotides in a cloned, short genomic sequence has become an important criterion to annotate such a sequence as a miRNA molecule. While the majority of human mature miRNA sequences consist of 22 nucleotides, there exists discrepancy in the characteristic lengths of the miRNA sequences. There is also a lack of systematic studies on such length distribution and on the biological factors that are related to or may affect this length. In this paper, we intend to fill this gap by investigating the sequence structure of human miRNA molecules using statistics tools. We demonstrate that the traditional discrete probability distributions do not model the length distribution of the human mature miRNAs well, and we obtain the statistical distribution model with a decent fit. We observe that the four nucleotide bases in a miRNA sequence are not randomly distributed, implying that possible structural patterns such as dinucleotide (trinucleotide or higher order) may exist. Furthermore, we study the relationships of this length distribution to multiple important factors such as evolutionary conservation, tumorigenesis, the length of precursor loop structures, and the number of predicted targets. The association between the miRNA sequence length and the distributions of target site counts in corresponding predicted genes is also presented. This study results in several novel findings worthy of further investigation that include: (1) rapid evolution introduces variation to the miRNA sequence length distribution; (2) miRNAs with extreme sequence lengths are unlikely to be cancer-related; and (3) the miRNA sequence length is positively correlated to the precursor length and the number of predicted target genes.
Published: 2013
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15. miRNA-mRNA correlation-network modules in human prostate cancer and the differences between primary and metastatic tumor subtypes.

Author: Wensheng Zhang, Andrea Edwards, Wei Fan, Erik K Flemington, and Kun Zhang
Subjects: Medicine, Science
Abstract: Recent studies have shown the contribution of miRNAs to cancer pathogenesis. Prostate cancer is the most commonly diagnosed cancer in men. Unlike other major types of cancer, no single gene has been identified as being mutated in the majority of prostate tumors. This implies that the expression profiling of genes, including the non-coding miRNAs, may substantially vary across individual cases of this cancer. The within-class variability makes it possible to reconstruct or infer disease-specific miRNA-mRNA correlation and regulatory modular networks using high-dimensional microarray data of prostate tumor samples. Furthermore, since miRNAs and tumor suppressor genes are usually tissue specific, miRNA-mRNA modules could potentially differ between primary prostate cancer (PPC) and metastatic prostate cancer (MPC). We herein performed an in silico analysis to explore the miRNA-mRNA correlation network modules in the two tumor subtypes. Our analysis identified 5 miRNA-mRNA module pairs (MPs) for PPC and MPC, respectively. Each MP includes one positive-connection (correlation) module and one negative-connection (correlation) module. The number of miRNAs or mRNAs (genes) in each module varies from 2 to 8 or from 6 to 622. The modules discovered for PPC are more informative than those for MPC in terms of the implicated biological insights. In particular, one negative-connection module in PPC fits well with the popularly recognized miRNA-mediated post-transcriptional regulation theory. That is, the 3'UTR sequences of the involved mRNAs (∼620) are enriched with the target site motifs of the 7 modular miRNAs, has-miR-106b, -191, -19b, -92a, -92b, -93, and -141. About 330 GO terms and KEGG pathways, including TGF-beta signaling pathway that maintains tissue homeostasis and plays a crucial role in the suppression of the proliferation of cancer cells, are over-represented (adj.p
Published: 2012
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16. miRNA-mediated relationships between Cis-SNP genotypes and transcript intensities in lymphocyte cell lines.

Author: Wensheng Zhang, Andrea Edwards, Dongxiao Zhu, Erik K Flemington, Prescott Deininger, and Kun Zhang
Subjects: Medicine, Science
Abstract: In metazoans, miRNAs regulate gene expression primarily through binding to target sites in the 3' UTRs (untranslated regions) of messenger RNAs (mRNAs). Cis-acting variants within, or close to, a gene are crucial in explaining the variability of gene expression measures. Single nucleotide polymorphisms (SNPs) in the 3' UTRs of genes can affect the base-pairing between miRNAs and mRNAs, and hence disrupt existing target sites (in the reference sequence) or create novel target sites, suggesting a possible mechanism for cis regulation of gene expression. Moreover, because the alleles of different SNPs within a DNA sequence of limited length tend to be in strong linkage disequilibrium (LD), we hypothesize the variants of miRNA target sites caused by SNPs potentially function as bridges linking the documented cis-SNP markers to the expression of the associated genes. A large-scale analysis was herein performed to test this hypothesis. By systematically integrating multiple latest information sources, we found 21 significant gene-level SNP-involved miRNA-mediated post-transcriptional regulation modules (SNP-MPRMs) in the form of SNP-miRNA-mRNA triplets in lymphocyte cell lines for the CEU and YRI populations. Among the cognate genes, six including ALG8, DGKE, GNA12, KLF11, LRPAP1, and MMAB are related to multiple genetic diseases such as depressive disorder and Type-II diabetes. Furthermore, we found that ~35% of the documented transcript intensity-related cis-SNPs (~950) in a recent publication are identical to, or in significant linkage disequilibrium (LD) (p
Published: 2012
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17. Azithromycin in patients admitted to hospital with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial

Author: Eugenia Abaleke, Mustafa Abbas, Sadia Abbasi, Alfie Abbott, Ashraf Abdelaziz, Sherif Abdelbadiee, Mohamed Abdelfattah, Basir Abdul, Althaf Abdul Rasheed, Rezan Abdul-Kadir, Abdulfatahi Abdulmumeen, Niyaz Abdulshukkoor, Kula Abdusamad, Yazeed Abed El Khaleq, Mai Abedalla, Abeer UA Abeer Ul Amna, Adebanke Aboaba, Hani Abo-Leyah, Ahmed Abou-Haggar, Mahmoud Abouibrahim, Miriam Abraham, Tizzy Abraham, Abraheem Abraheem, Judith Abrams, Hyacinth-John Abu, Ahmad Abu-Arafeh, Syed M Abubacker, Akata Abung, Yaa Aceampong, Devikumar Acharya, Janet Acheson, Andres Acosta, Catherine Acton, Jacqueline Adabie-Ankrah, Fiona Adam, Matthew Adam, Huzaifa Adamali, Carol Adams, Kate Adams, Richard Adams, Tim Adams, Malgorzata Adamus, Kirsty Adcock, Aderonke Adebiyi, Ken Adegoke, Vicki Adell, Aldrin Adeni, Sherna Adenwalla, Oluwasegun A Adesemoye, Emmanuel O Adewunmi, Joyce Adeyemi, Elizabeth Adeyeye, Gabrielle Adkins, Adnan Adnan, John Aeron-Thomas, Lynn Afari, Debbie Affleck, Carmel Afnan, Deborah Afolabi, Muhammad Afridi, Rachel Agbeko, Chris Agbo, Sunil Aggarwal, Arameh Aghababaie, Judith Agwada-Akeru, Kwame A Agyapong, Shafana Ahamed Sadiq, Mohamed H Ahammed Nazeer, Jonathan Ah-Chuen, Mahin Ahmad, Ashar Ahmed, Bilal Ahmed, Forizuddin Ahmed, Iram Ahmed, Irshad Ahmed, Liban Ahmed, Maria C Ahmed, Muhammad S Ahmed, Naseer Ahmed, Nausheen Ahmed, Osama Ahmed, Rajia A Ahmed, Rizwan Ahmed, Saif Ahmed, Sammiya Ahmed, Sara Ahmed, Syed H Ahmed, Roa Ahmed Ali, Sana Ahmer, Dhiraj Ail, Adam Ainsley, Mark Ainsworth, Myriam Aissa, Lucy Aitchson, Lindianne Aitken, Bini Ajay, Abdulakeem Ajibode, Ayesha Ajmi, Muhammad N Akhtar, Nasim Akhtar, Suha Akili, Oludoyinsola Akindolie, Yinka Akinfenwa, Olugbenga Akinkugbe, Ibrahim Akinpelu, Umeh Akudo, Asma Al Balushi, Majd Al Dakhola, Narendra Aladangady, Sajid Alam, Abbas Al-Asadi, Kyriaki Alatzoglou, Lorraine Albon, Stephen Alcorn, Aggie Aldana, David Alderdice, Rayan Aldouri, Jonathan Aldridge, Nicolas Aldridge, Ana Alegria, Alison Alexander, John Alexander, Peter DG Alexander, Julyan Al-Fori, Bahij Al-Hakim, Shams Al-Hity, Ali Ali, Anyat Ali, Fawzia R Ali, Jawad Ali, Mariam Ali, Mohammad Ali, Oudai Ali, Sakina Ali, Syed Ali, Abid Alina, Katrin Alizaedeh, Maithem Al-Jibury, Moutaz Alkhusheh, Alison Allanson, Robert Allcock, Eireann Allen, Jonathan Allen, Kerry Allen, Louise Allen, Rebecca Allen, Sam Allen, Sharon Allen, Simon Allen, Kathryn Allison, Bethan Allman, Lynne Allsop, Christine Almadenboyle, Hassan Al-Moasseb, Magda Al-Obaidi, Lina Alomari, Akram Al-Rabahi, Bahar Al-Ramadhani, Zayneb Al-Saadi, Warkaq Al-Shamkhani, Bashar Al-Sheklly, Mary Alvarez, Maysaa Alzetani, Susan Amamou, Sakarai Ambalavanan, Sarah-Jayne Ambler, Robert Ambrogetti, Chris Ambrose, Amir Ameen, Maria R Amezaga, Allison Amin, Amina Amin, Kanish Amin, Amjad Amjad, Victoria Amosun, Khaled Amsha, Atul Anand, Samantha Anandappa, Julie Anderson, Laura Anderson, Michelle Anderson, Nicola Anderson, Rachel Anderson, Rory Anderson, Prematie Andreou, Angela Andrews, Jill Andrews, Kanayochukwu Aneke, Andrew Ang, Wan Wei Ang, Tammy Angel, Paola Angelini, Lazarus Anguvaa, Oleg Anichtchik, Millicent Anim-Somuah, Krishnan Aniruddhan, Alpha Anthony, Aaron Anthony-Pillai, Philip Antill, Zhelyazkova Antonina, Varghese Anu, Muhammad Anwar, Aristeidis Apostolopoulos, Sarah Appleby, Diane Appleyard, Bianca Araba, Angela Aramburo, Ann Archer, Denise Archer, Simon Archer, Christian Ardley, Ana-Maria Arias, Ryoki Arimoto, Charlotte Arkley, Charlotte Armah, Ilianna Armata, Adam Armitage, Ceri Armstrong, Maureen Armstrong, Sonia Armstrong, Philippa Armtrong, Heike Arndt, Clare Arnison-Newgass, David Arnold, Rachael Arnold, Dhawal Arora, Pardeep Arora, Rishi Arora, Arslam Arter, Ganesh Arunachalam, Rita Arya, Salma Asam, Denisa Asandei, Adeeba Asghar, Catherine Ashbrook-Raby, Helen Ashby, Jan Ashcroft, John Ashcroft, Ayesha Ashfaq, Abdul Ashish, Sally Ashman-Flavell, Sundar Ashok, Muhammad Z Ashraf, Saima Ashraf, Mohammad B Ashraq, Deborah Ashton, Susan Ashton, Andrew Ashworth, Rebecca Ashworth, Harshini Asogan, Julia Asplin, Atif Asrar, Omar Assaf, Raine Astin-Chamberlain, Deborah Athorne, Christopher Atkins, Stacey Atkins, John Atkinson, Vicki Atkinson, Ahmed Attiq, Paula Aubrey, Suzannah August, Aye CT Aung, Hnin Aung, Kyaw Thu Aung, Nini Aung, Zaw Myo Aung, Emily Austin, Karen Austin, Miriam Avery, Joanne Avis, Cristina Avram, Paula Avram, Gabriel Awadzi, Aszad Aya, Eman Ayaz, Amanda Ayers, Vivek Ayra, Jawwad Azam, Mohammed Azharuddin, Ghazala Aziz, Ashaari Azman Shah, Giada Azzopardi, Hocine Azzoug, Nasaruilla Babajan, Fiyinfoluwa Babatunde, Melvin Babi, Babiker Babiker, Gayna Babington, Matthew Babirecki, Adetona O Babs-Osibodu, Gina Bacon, Jenny Bacon, Bibi Badal, Gurpreet R Badhan, Shreya Badhrinarayanan, Joseph Bae, Alice Baggaley, Amy Baggott, Graham Bagley, Dinesh Bagmane, Kasra Bahadori, Gayathri Baijiu, Charles Bailey, Julie Bailey, Katie Bailey, Lindsey Bailey, Liz Bailey, Morgan Bailey, Peter Bailey, Hamish Baillie, Kenneth Baillie, Jennifer Bain, Karen Bain, Becky Bainton, David Baird, Yolanda Baird, Aiysha Bajandouh, Evelyn Baker, Johanne Baker, Kenneth Baker, Pearl Baker, Hugh Bakere, Nawar Bakerly, Michelle Baker-Moffatt, Panos Bakoulas, Abhijit Bal, Niranjan Balachandran, Irvin Balagosa, Andrea Balan, Theodosios Balaskas, Madhu Balasubramaniam, Alison Balcombe, Cheryl Baldwick, Alexander Baldwin, Ashley Baldwin, Danielle Baldwin, Lisa Baldwin, Rebekah Baldwin-Jones, James Balfour, Ceri Ball, Craig Balmforth, Gabby Bambridge, Alasdair Bamford, Amy Bamford, Peter Bamford, Adefunke Bamgboye, Kasun Bamunuarachchi, Elizabeth Bancroft, Hollie Bancroft, Joyce Banda, Srini Bandi, Nageswar Bandla, Somaditya Bandyopadhyam, Ritwik Banerjee, Sandip Banerjee, Harrison Banks, Luke Banks, Daniel Banner, Oliver Bannister, Christopher Bannon, Laura Banton, Mariamma Baptist, Tanya Baqai, Ananya M Baral, Desislava Baramova, Russell Barber, Emma Barbon, Monica Barbosa, Jamie Barbour, Alexander Barclay, Charlotte Barclay, Stephanie Bareford, Shahedal Bari, Amy Barker, Debbie Barker, Joseph B Barker, Leon Barker, Oliver Barker, Kerry Barker-Williams, Sinha Barkha, Juliana Barla, Gavin Barlow, Richard Barlow, James Barnacle, Alex Barnard, Debi Barnes, Nicky Barnes, Amy Barnett, Debra Barnett, Ashton Barnett-Vanes, William Barnsley, Andrew Barr, David Barr, Shaney Barratt, Manuella Barrera, Fiona Barrett, Jessica Barrett, Jazz Bartholomew, Sarah Bartholomew, Claire Bartlett, Georgina Bartlett, Greg Barton, Jill Barton, Rachael Barton, Rosaleen Baruah, Sonia Baryschpolec, Archana Bashyal, Betsy Basker, Ayten Basoglu, Amira Bassaly, G Bassett, Bengisu Bassoy, Anupam Basumatary, Adam Bataineh, Freddie Batalla, Tristan Bate, Harry John Bateman, Kathryn Bateman, Vhairi Bateman, Eleanor Bates, Hayley Bates, Michelle Bates, Rizvan Batha, Sally Batham, Ana Batista, Amit Batla, Dushyant Batra, Harry Batty, Thomas Batty, Miranda Baum, Carina Bautista, Fatima S Bawani, Simon Bax, Matt Baxter, Nicola Baxter, Hannah Bayes, Farid Bazari, Rohit Bazaz, Ahmad Bazli, Laura Beacham, Hannah Beadle, Wendy Beadles, Philip Beak, Andy Beale, Kathy Beardsal, Jack Bearpark, Karen Beaumont, Matthew Beaumont, Dawn Beaumont-Jewell, Theresa Beaver, Sarah Beavis, Christy Beazley, Sarah Beck, Virginia Beckett, Rosie Beckitt, Heidi Beddall, Seonaid Beddows, Deborah Beeby, Michelle Beecroft, Sally Beer, Jane Beety, Gabriela Bega, Alison Begg, Susan Begg, Sara Beghini, Ayesha Begum, Safia Begum, Teresa Behan, Roya Behrouzi, Jon Beishon, Claire Beith, James Belcher, Holly Belfield, Katherine Belfield, Ajay Belgaumkar, Dina Bell, Gareth Bell, Gillian Bell, Lauren Bell, Louise Bell, Nicholas Bell, Stephanie Bell, Jennifer L Bell, Mary Bellamy, Arianna Bellini, Amanda Bellis, Fionn Bellis, Lesley Bendall, Naveena Benesh, Nicola Benetti, Leonie Benham, Guy Benison-Horner, Ann Bennett, Caroline Bennett, Gillian Bennett, Kristopher Bennett, Lorraine Bennett, Sara Bennett, Vivienne Benson, Andrew Bentley, Ian Benton, Eva Beranova, Matthew Beresford, Colin Bergin, Malin Bergstrom, Jolanta Bernatoniene, Thomas Berriman, Zoe Berry, Kimberley Best, Yvonne Beuvink, Emily Bevan, Sarah Bevins, Tom Bewick, Helen Bexhell, Andrew Bexley, Sonay Beyatli, Fenella Beynon, Arjun Bhadi, Sanjay Bhagani, Shweta Bhagat, Shiv Bhakta, Rekha Bhalla, Mahesh Bhalme, Khushpreet Bhandal, Kulbinder Bhandal, Ravina Bhanot, Prashanth Bhat, Nikhil Bhatia, Rahul Bhatnagar, Janki Bhayani, Deepika Bhojwani, Salimuzzaman Bhuiyan, Anna Bibby, Naheeda Bibi, Salma Bibi, Tihana Bicanic, Julie Bigg, Sarah Biggs, Alphonsa Biju, Andras Bikov, Sophie Billingham, Jessica Billings, Alice Binns, Oliver Bintcliffe, Catherine Birch, Janine Birch, Jenny Birch, Katherine Birchall, Sam Bird, Sumedha Bird, Mark Birt, Kilanalei Bishop, Linda Bishop, Lisa Bishop, Nibedan Biswas, Sahar Biuk, Karen Blachford, Ethel Black, Helen Black, Karen Black, Mairead Black, Polly Black, Sabrina Black, Bethan Blackledge, Joanne Blackler, Samantha Blackley, Helen Blackman, Caroline Blackstock, Francesca Blakemore, Helen Blamey, Sujata Blane, Simon Blankley, Parry Blaxill, Jane Blazeby, Natalie Blencowe, Ben Bloom, Angela Bloss, Hannah Bloxham, Louise Blundell, Susara Blunden, Mark Blunt, Ian Blyth, Kevin Blyth, Andrew Blythe, Karen Bobruk, Pritesh Bodalia, Neena Bodasing, Gabriele Boehmer, Marta Boffito, Sumit Bokhandi, Maria Bokhar, Saba Bokhari, Sakina Bokhari, Syed Owais Bokhari, Ambrose Boles, Matthew Bolton, Helena Bond, Stuart Bond, Thomas Bond, Alice Bone, Georgia Boniface, Lizzy Bonney, Joanne Borbone, Stephanie Borg, Catherine Borra, Samuel Bosompem, Liam Botfield, Fiona Bottrill, Hannah Bouattia, Laura Bough, Hayley Boughton, Zoe Boult, Miriam Bourke, Karen Bourne, Michelle Bourne, Rachel Bousefield, Lucy Boustred, Alexandra Bowes, Amy Bowes, Philip Bowker, Louise Bowman, Simon Bowman, Angie Bowring, Geetha Boyapati, Jenny Boyd, Laura Boyd, Namoi Boyle, Pauline Boyle, Rosalind Boyle, Louise Boyles, Leanna Brace, Jodie Bradder, Clare J Bradley, Pamela Bradley, Patrick Bradley, Joanne Bradley-Potts, Lynne Bradshaw, Zena Bradshaw, Rebecca Brady, Shirin Brady, Marie Branch, Emily Brandl-Salutz, Christina Branfield, Jamie Brannigan, Louise Brassington, Fiona Bray, Nancy Bray, Manny Brazil, Lucy Brear, Tracy Brear, Stephen Brearey, Morwenna Brend, Andrew Brereton, Chris Brewer, Gavin Bridgwood, Sara Brigham, John Bright, Christopher Brightling, Elaine Brinkworth, Robin Brittain-Long, Vianne Britten, Lauren Broad, Sarah Broad, Rosie Broadhurst, Andrew Broadley, Marie Broadway, Christopher Brockelsby, Megan Brocken, Tomos Brockley, Fiona Brogan, Felicity Brokke, Jacob Brolly, David Bromley, Hannah Brooke-Ball, Verity Brooker, Matthew Brookes, Alison Brooks, Kate Brooks, Nicole Brooks, Rachel Brooks, Sophie Brooks, Natalie Broomhead, Chloe Broughton, Nathaniel Broughton, Matt Brouns, Alison Brown, Carly Brown, Catrin Brown, Ellen Brown, Heather Brown, Janet Brown, Louise Brown, Niall Brown, Pauline Brown, Richard Brown, Robert Brown, Steven Brown, Tom Brown, Charlotte Browne, Duncan Browne, Rachel Browne, Stephen Brownlee, David Bruce, Johanna Bruce, Michelle Bruce, Wojciech Brudlo, Nigel Brunskill, Luke Brunton, Margaret Brunton, Jade Bryant, Mark Bryce, Maya Buch, Ruaridh Buchanan, Amanda Buck, Elizabeth Buckingham, Laura Buckley, Philip Buckley, Sarah Buckley, Carol Buckman, George Bugg, Ramadan Bujazia, Shanzay Bukhari, Richard Bulbulia, Alex Bull, Damian Bull, Rhian Bull, Thomas Bull, Sam Bullard, Naomi Bulteel, Katherine Bunclark, Roneleeh Bungue-Tuble, Caroline Burchett, Christy Burden, Thomas G Burden, Sarah Burge, Mika Burgess, Sophia Burgess, Emma Burke, Sara Burnard, Caroline Burnett, Amy Burns, Collette Burns, James Burns, Karen Burns, Daniel Burrage, Kate Burrows, Claire Burston, Ben Burton, Fiona Burton, Angus Butchart, Aaron Butler, Jo Butler, Joanne Butler, Joshua Butler, Peter Butler, Susan Butler, Al-Tahoor Butt, Caryl Butterworth, Nicola Butterworth-Cowin, Robert Buttery, Heather Button, Daniel Buttress, Jane Byrne, Wendy Byrne, Victoria Byrne-Watts, Ruth Cade, Donna Cairney, Claire Calderwood, James Calderwood, Giorgio Calisti, Debbie Callaghan, Jennifer Callaghan, Claire Callens, Caroline Calver, Melissa Cambell-Kelly, Tracey Camburn, David R Cameron, Eleanor Cameron, Fraser Cameron, Sheena Cameron, Christian Camm, Renee FD Cammack, Alison Campbell, Amy Campbell, Barbara Campbell, Bridget Campbell, Debbie Campbell, Helen Campbell, Hilary Campbell, Jonathan Campbell, Mark Campbell, Robyn Campbell, Wynny Campbell, Quentin Campbell Hewson, Laura Camrasa, Julie Camsooksai, Shaula M Candido, Cielito Caneja, Johnathon Cann, Ruby Cannan, Emma Cannon, Michael Cannon, Petra Cannon, Vivienne Cannons, Jane Cantliff, Ben Caplin, Angelika Capp, Thomas Capstick, Simon Carley, Tammy Carlin, Samantha Carmichael, Margaret Carmody, Mandy Carnahan, Charlotte Caroline, Patrick Carr, Sharon Carr, Anna Carrasco, Zoe Carrington, Jonathan Carter, Paul Carter, Penny Carter, Douglas Cartwright, Claire Carty, Jaime Carungcong, Paula Carvelli, Aisling Cashell, Barbara Cassimon, Teresa Castiello, Gail Castle, Melanie Caswell, Ana Maria Catana, Heidi Cate, Susanne Cathcart, Katrina Cathie, Christine Catley, Laura Catlow, Kerry Causer, Luke Cave, Frianne Cawa, Kathryn Cawley, Philippa Cawley, Chloe Caws, Hankins Cendl, Jeva Cernova, Ed Cetti, Stephanie Chabane, Manish Chablani, Cathleen Chabo, David Chadwick, Julie Chadwick, Robert Chadwick, Ela Chakkarapani, Arup Chakraborty, Mallinath Chakraborty, Mollika Chakravorty, James Chalmers, Georgina Chamberlain, Sarah Chamberlain, Carol Chambers, Emma Chambers, Jonathan Chambers, Lucy Chambers, Naomi Chambers, Shreekant Champanerkar, Carmen Chan, Cheuk Chan, Evelyn Chan, Kimberley Chan, Ping Chan, Rebekah (Pui-Ching) Chan, Xin H Chan, Chris Chandler, Kim J Chandler, Zoe Chandler, Badrinathan Chandrasekaran, Josephine Chaplin, Graeme Chapman, John Chapman, Katie Chapman, Laura Chapman, Lianne Chapman, Polly Chapman, Timothy Chapman, Lucy Chappell, Amanda Charalambou, Bethan Charles, Dianne Charlton, Kevin Chatar, Calvin Chatha, Priyanka Chaturvedi, Muhammad YN Chaudhary, Iram Chaudhry, Zain Chaudhry, Nazia Chaudhuri, Muhammad Chaudhury, Anoop Chauhan, Milan Chauhan, Ruchi S Chauhan, Nicola Chavasse, James Cheaveau, Michelle Cheeseman, Fang Chen, Hui Min Chen, Terence Chen, Lok Y Cheng, Zhihang Cheng, Ashok Chengappa, Helen Chenoweth, Chun H Cheong, Shiney Cherian, Helen Cheshire, Sarah Chessell, Chee Kay Cheung, Elaine Cheung, Claire Cheyne, Swati Chhabra, Eric Chiang, Angela Chiapparino, Rosavic Chicano, Sam Chilcott, Phillipa Chimbo, KokWai Chin, Wen Jie Chin, Amol Chingale, Heather Chisem, Ben Chisnall, Sunder Chita, Nihil Chitalia, Chipo Chitsenga, Matthew Chiu, Brenda Chivima, Soha Choi, Willy Choon Kon Yune, Khayzer Choudhary, Vandana Choudhary, Gary Chow, Muhibbur Chowdhury, Alexander Christides, Alex Christie, Daniel Christmas, Thereza Christopherson, Mark Christy, Paris Chrysostomou, Yunli Chua, Dip Chudgar, Richard Chudleigh, Srikanth Chukkambotla, Izu Chukwulobelu, Favour Chukwunonyerem, Chi Y Chung, Jonathan Chung, Elaine Church, Sara R Church, David Churchill, Paola Cicconi, Zdenka Cipinova, Bessie Cipriano, Sarah Clamp, Melanie Clapham, Edel Clare, Sarbjit Clare, Andrew Clark, Charlotte Clark, Diane Clark, Felicity Clark, Gabrielle Clark, James Clark, Katherine Clark, Lucy Clark, Matthew Clark, Patricia Clark, Richard Clark, Thomas Clark, Zoe Clark, Andrea Clarke, Paul Clarke, Robert Clarke, Roseanne Clarke, Samantha Clarke, Sheron Clarke, Alleyna Claxton, Elizabeth Clayton, Olivia Clayton, Jill Clayton-Smith, Chris Cleaver, Jayne Clemens, Carlota Clemente de la Torre, Suzanne Clements, Sarah Clifford, Amelia Clive, Jonathan Clouston, Samantha Clueit, Andrea Clyne, Peter GL Coakley, Kathryn Cobain, Susan Coburn, Alexandra Cochrane, Patricia Cochrane, Samantha Cockburn, Helen Cockerill, Shirley Cocks, Rhodri Codd, Rachel Codling, Adam Coe, Samantha Coetzee, David Coey, Paul F Cofie, Danielle Cohen, Jonathan Cohen, Oliver Cohen, Mike Cohn, Louise Coke, Nicholas Colbeck, Roghan Colbert, Esther Cole, Jade Cole, Joby Cole, Nicholas Cole, Garry Coleman, Matt Coleman, Holly Coles, Julie Colley, Dawn Collier, Heather Collier, Paul Collini, Emma Collins, Jaimie Collins, Joanne Collins, Nicola Collins, Sally Collins, Vicky Collins, Andrew Collinson, Jennifer Collinson, Madeleine Colmar, Hayley E Colton, James Colton, Katie Colville, Carolyn Colvin, Edward Combes, David Comer, Dónal Concannon, Robin Condliffe, Lynne Connell, Natalie Connell, Gavin Connolly, Emma Connor, Antonia Conroy, Veronica Conteh, Rory Convery, Grainne Conway, Rhiannon Conway, Jo-Anna Conyngham, Eloise Cook, Gemma Cook, Helen Cook, Graham Cooke, Katrina Cooke, Catherine Coombs, Chris Cooper, Jamie Cooper, Joshua Cooper, Lauren Cooper, Rowena Cooper, Sophie Cooper, Thomas Cope, Carolyn Corbett, John Corcoran, Jessica Cordle, Alasdair Corfield, John Corless, Alison Corlett, Pamela Corlett, Michael Cornwell, Diana Corogeanu, Ruth Corrigan, Rita Corser, Jon Cort, Denise Cosgrove, Patricia Costa, Charlie Coston, Susannah Cotgrove, Zoe Coton, Lisa-Jayne Cottam, Rhiannon Cotter, Donna Cotterill, Caroline Cotton, James Coulson, David Counter, Cherry Coupland, Ellie Courtney, Julia Courtney, Elena Cowan, Louise Cowen, Steve Cowman, Amanda Cowton, Ellie Cox, Giles Cox, Karina Cox, Rebecca Cox, Karen Coy, Victoria Craig, Matthew Cramp, Emma Crawford, Isobel Crawford, Sarah Crawshaw, Ben Creagh-Brown, Andrew Creamer, Peter Creber, Joanne Cremona, Janet Cresswell, Mark Cribb, Charles Crichton, Declan Crilly, Lauren Crisp, Nikki Crisp, Dominic Crocombe, Maria Croft, Jennifer Crooks, Harriet Crosby, Tim Cross, Stephen Crotty, Susan Crouch, Madeleine Crow, Rory Crowder, Becky Crowe, Rebecca Croysdill, Irena Cruickshank, James Cruise, Carina Cruz, Trino Cruz Cervera, Dominic Cryans, Guanguo Cui, Gillian Cummings-Fosong, Victoria Cunliffe, Caroline Cunningham, Mishell Cunningham, Neil Cunningham, Nicola Cunningham, Jason Cupitt, Hollie Curgenven, Gerens Curnow, Simon Curran, Scarlett Currie, Michelle Curtin, Jonathan Curtis, Katrina Curtis, Olivia Curtis, Rebecca Cuthbertson, Sean Cutler, Patrycja Czylok, Joana da Rocha, Sura Dabbagh, Helen Daggett, Jacqui Daglish, Anne Dale, Katie Dale, Michaela Dale, Sam Dale, Jolyon Dales, Helen Dalgleish, Dermot Dalton, Aoife Daly, Vaishnavi Dandavate, Akila Danga, Amelia Daniel, Priya Daniel, Allison Daniels, Sandra Danso-Bamfo, Alex Darbyshire, Janet Darbyshire, Paul Dark, Kate Darlington, Tom Darton, Guledew Darylile, Manjusha Das, Sukamal Das, Martin Daschel, Joanne Dasgin, Dibyendu Datta, Anna Daunt, Mark Davey, Miriam Davey, Anette David, Mini David, Sarah David, Alexander Davidson, Laura Davidson, Neil Davidson, Richard Davidson, Albert Davies, Amanda Davies, Carolyn Davies, Catrin Davies, Drew Davies, Elaine Davies, Ffyon Davies, Helen Davies, Jim Davies, Karen Davies, Kelly Davies, Kim Davies, Louisa Davies, Matthew Davies, Michelle Davies, Owen Davies, Patrick Davies, Rachel Davies, Rhys Davies, Ruth Davies, Sarah Davies, Simon Davies, Wendy Davies, Gwyneth Davis, Illinos Davis, Peter Davis, Alexander Davison, Christine Dawe, Danielle Dawson, Elizabeth Dawson, Joy Dawson, Tom Dawson, Andrew Daxter, Andrew Day, Jacob Day, Jeremy Day, Parijat De, Henry de Berker, Duneesha de Fonseka, Toni de Freitas, Frederico de Santana Miranda, Eleanor de Sausmarez, Shanika de Silva, Thushan de Silva, James de Souza, Anthony de Soyza, Natasha de Vere, Johannes de Vos, Bethan Deacon, Sharon Dealing, Anna Dean, Julie Dean, Katrina Dean, Jill Deane, James Dear, Effie Dearden, Catherine Deas, Vashist Deelchand, Matthew Deeley, Joanne Deery, Emmanuel Defever, Manuela Del Forno, Arnold Dela Rosa, Amanda Dell, Marilyn DeLuna, Carrie Demetriou, David DeMets, Jane Democratis, Jacqueline Denham, Emmanuelle Denis, Craig Denmade, Kathy Dent, Martin Dent, Elise Denton, Tom Denwood, Nishigandh Deole, Darshita Depala, Susan Dermody, Amisha Desai, Purav Desai, Sanjeev Deshpande, Vai Deshpande, Sirjana Devkota, Prakash Dey, Vishal Dey, Rogin Deylami, Gurlove K Dhaliwal, Kevin Dhaliwal, Sundip Dhani, Amandeep Dhanoa, Mili Dhar, Sukhjit Dhariwal, Devesh Dhasmana, Ekanjali Dhillon, Reiss Dhillon, Kayleigh Diaz-Pratt, Pamela Dicks, Matthew Dickson, Sean Dillane, Sarah Diment, Paul Dimitri, Alex Dipper, Laura Dirmantaite, Lisa Ditchfield, Sarah Diver, Nandita Divikar, Lavanya Diwakar, Masroor Diwan, Caroline Dixon, Catherine Dixon, Petr Dlouhy, Paul Dmitri, Marinela Dobranszky Oroian, Charlotte Dobson, Lee Dobson, Marie Docherty, Morven Dockery, Emma Docks, James Dodd, Jackie Dodds, Rebecca Dodds, Steve Dodds, Richi Dogra, Erin Doherty, Warren Doherty, Yumiko Doi, Iain Doig, Daniel Dolan, Mark Dolman, Rozzie Dolman, Lisa Donald, Callum Donaldson, Denise Donaldson, Kate Donaldson, Sinead Donlon, Joanne Donnachie, Christopher Donnelly, Eilish Donnelly, Emma Dooks, Andrew Dooley, Kane Dorey, Sharon Dorgan, Davinder Dosanjh, Paula Dospinescu, Andrew Dougherty, Katie Douglas, Lucy Dowden, Michelle Dower, Sud Dowling, Hayley Downe, Nicola Downer, Charlotte Downes, Rob Downes, Thomas Downes, Damian Downey, Louise Downs, Simon Dowson, Cornel Dragan, Cristina Dragos, Chelsea Drake, Victoria Drew, Olivia Drewett, Celine Driscoll, Helena Drogan, Ronald Druyeh, Simon Drysdale, Hazel Dube, Judith Dube, Stephen Duberley, Simon Dubrey, Roger Duckitt, Hayley Duckles-Leech, Nicola Duff, Helen Duffy, Lionel Dufour, Annette Duggan, Parveen Dugh, Janice Duignan, Simon Dummer, Andrew Duncan, Barrie Duncan, Christopher Duncan, Fullerton Duncan, Alessia Dunn, Damian Dunn, Laura Dunn, Karen Dunne, Fiona Dunning, Aidan Dunphy, Venkat Duraiswamy, Beatriz Duran, Ingrid DuRand, Alison Durie, Emily Durie, Laura Durrans, Hannah Durrington, Akshay Dwarakanath, Laasya Dwarakanath, Ellen Dwyer, Claudia Dyball, Harvey Dymond, Tom Dymond, Chris Eades, Melissa Earwaker, Nicholas Easom, Clare East, Jack Easton, Ruth Eatough, Oluwadamilola Ebigbola, Martin Ebon, Sinan Eccles, Chloe Eddings, Michael Eddleston, Maureen Edgar, Katharine Edgerley, Mary Edmondson, Tracy Edmunds, Alexandra Edwards, Andrea Edwards, Catherine Edwards, Joy Edwards, Kennedy Edwards, Mandy Edwards, Steven Edwards, Jenny Eedle, Dawn Egginton, Sarah Eisen, Ugochukwu Ekeowa, Mohamed Ekoi, Ayomide Ekunola, Kate El Bouzidi, Ashley Elden, Jennifer Elder, Haifa Eldew, Diana Eleanor, Maysoon Elfadil, Eman Elfar, Mayy M Elgamal, Amr Elgohary, Stellios Elia, Jennifer Elias, Tania Elias, Nadia Elkaram, Amin Elkhawad, Andrew V Elkins, Julie Ellam, Nikki Ellard, Laura Nicola Ellerton, Amy Elliott, Fiona Elliott, Kerry Elliott, Valmai Elliott, Annie Ellis, Hayley Ellis, Kaytie Ellis, Tak-Yan Ellis, Yvette Ellis, Rahma Elmahdi, Einas Elmahi, Omer Elneima, Mohamed Elokl, Ahmed Elradi, Mohamed Elsaadany, Sally El-Sayeh, Hana El-Sbahi, Tarek Elsefi, Karim El-Shakankery, Sarah Elyoussfi, Jonathan Emberson, John Emberton, Julian Emmanuel, Ingrid Emmerson, Charis Emmett, Michael Emms, Marieke Emonts, Angila Engden, Katy English, Nicola Entwistle, Hene Enyi, Ruth Erin Jones, Agota Ermenyi, Hanif Esmail, Ajmal Eusuf, Brynach Evans, Chris Evans, Debra Evans, Gail Evans, Gareth Evans, Jennifer Evans, Lynn Evans, Melanie Evans, Mim Evans, Morgan Evans, Ranoromanana Evans, Teriann Evans, Terry J Evans, Caroline Everden, Lynsey Evison, Jacqueline Faccenda, Leila Fahel, Youstina Fahmay, Sara Fairbairn, Andy Fairclough, Louise Fairlie, Anne Fajardo, Euan Falconer, John Fallon, David Faluyi, Qayyum Farah, Novin Fard, Amr Farg, Adam Farmer, Katie Farmer, Toni Farmery, Frances Farnworth, Samantha Farnworth, Faiyaz Farook, Hadia Farooq, Sidrah Farooq, Fiona Farquhar, Aaron Farrell, Barbara Farrell, James Farthing, Syeda Farzana, Rahmatu Fasina, Azam Fatemi, Mina Fatemi, Nibah Fatimah, Saul N Faust, Joe Fawke, Sinmidele Fawohunre, Alex Feben, Federico Fedel, Christopher Fegan, Mae Felongco, Lynsey Felton, Tim Felton, Kate Fenlon, Andrea Fenn, Isabelle Fenner, Ciara Fenton, Melisa Fenton, Cameron Ferguson, Jenny Ferguson, Kathryn Ferguson, Katie Ferguson, Lisa Ferguson, Susan Ferguson, Susie Ferguson, Victoria Ferguson, Denzil Fernandes, Candida Fernandez, Eduardo Fernandez, Sonia Fernandez Lopez, Ahmed Feroz, Pietro Ferranti, Thais Ferrari, Catarina Ferreira-De Almeida, Alexandra Ferrera, Emma Ferriman, Nicholas Fethers, Ben Field, Janet Field, Andra Fielding, Julie Fielding, Sarah Fielding, Asma Fikree, Sarah Ann Filson, Joanne Finch, Laurie Finch, Natalie Fineman, Adam Finn, Joanne Finn, Sofia Fiouni, Jo Fiquet, James Fisher, Neil Fisher, Daniel Fishman, Krystofer Fishwick, Marie Fisk, Jan Flaherty, Michael Flanagan, Charles Flanders, Julie Fleming, Lucy Fleming, Paul Fleming, William Flesher, Alison Fletcher, Lucy Fletcher, Sophie Fletcher, Christopher Flood, Jonathan Flor, Vincent Florence, Sharon Floyd, Adama Fofana, Georgina Fogarty, Linda Folkes, Aiwyne Foo, Andrew Foot, Jayne Foot, Jane Forbes, Kathryn Forcer, Jamie Ford, Jennifer Foreman, Caroline Fornolles, Adam Forrest, Ellie Forsey, Thomas Forshall, Elliot Forster, Julian Forton, Emily Foster, Joseph Foster, Rachel A Foster, Tracy Foster, Angela Foulds, Ian Foulds, Folakemi Fowe, Emily Fowler, Robert Fowler, Stephen Fowler, Caroline Fox, Claire Fox, Heather Fox, Jonathan Fox, Lauren Fox, Natalie Fox, Simon Fox, Sarah-Jane Foxton, Rebecca Frake, Alex Francioni, Olesya Francis, Rebecca Francis, Sarah Francis, Theodora Francis-Bacon, Victoria Francois, Sharon Frayling, Martyn Fredlund, Michael Freeborn, Carol Freeman, Elaine Freeman, Hannah Freeman, Nicola Freeman, Clare Freer, Eleanor French, Anastasia Fries, Matthew Frise, Renate Fromson, Claire Froneman, John Frost, Victoria Frost, Rachel Frowd, Arun Fryatt, Bridget Fuller, Elizabeth Fuller, Tracy Fuller, Duncan Fullerton, Sarah Funnell, John Furness, Hassina Furreed, Waqas Gaba, Elizabeth Gabbitas, Claire Gabriel, Joshua Gahir, Katarzyna Gajewska-Knapik, Christopher Gale, Hugo Gale, 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Julia Greig, Rebecca Grenfell, Teena Grenier, Susan Grevatt, Glaxy Grey, Andrew Gribbin, Ben Griffin, Denise Griffin, Mel Griffin, Sian Griffith, Andrew Griffiths, Daniel Griffiths, David Griffiths, Donna Griffiths, Isabel Griffiths, Nicola Griffiths, Oliver Griffiths, Sofia Grigoriadou, Steph Grigsby, Russell Gritton, Evelina Grobovaite, Clarissa Grondin, Rachel Groome, Liliana Grosu, Jenny Grounds, Jayne Groves, Neil Grubb, Julie Grundy, Francesca Guarino, Sharada Gudur, Shivang Gulati, Vikas Gulia, Pumali Gunasekera, Kirun Gunganah, Jessica Gunn, Emma Gunter, Alok Gupta, Atul Gupta, Rajeev Gupta, Richa Gupta, Tarun Gupta, Vineet Gupta, Ankur Gupta-Wright, Sambasivarao Gurram, Ishy Gurung, Shraddha Gurung, Hazel Guth, Ruth Habibi, Pamela Hackney, Christian Hacon, Aiman Haddad, Denise Hadfield, Michalis Hadjiandreou, Anna Haestier, Nauman Hafiz, Rana Hafiz-Ur-Rehman, Samantha Hagan, Jack W Hague, Rosemary Hague, Andrew Haigh, Kate Haigh, Christina Haines, Scott Hainey, Morton Hair, 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Charlie H Harper, Heather Harper, Peter Harper, Rosemary Harper, Sarah Harrhy, Sian Harrington, Yasmin Harrington-Davies, Claire Harris, Jade Harris, Julie Harris, Laura Harris, Marie-Clare Harris, Nichola Harris, David Harrison, Laura Harrison, Melanie Harrison, Rowan Harrison, Susie Harrison, Tom Harrison, Wendy Harrison, Elizabeth Harrod, Ciaran Hart, Dominic Hart, Rosemary Hartley, Ruth Hartley, Tom Hartley, William Hartrey, Hans Hartung, Alice Harvey, Angela Harvey, Max Harvey, Catherine Harwood, Helen Harwood, Neda Hasan, Brigitte Haselden, Mohammed Hashimm, Imranullah Hashmi, Zena Haslam, Abdulhakim Hassan, Adil Hassan, Ali Hassan, Waqar Ul Hassan, Philip Hassell, Alex Hastings, Bethany Hastings, Jonathan Hatton, Jennifer Haugh, May Havinden-Williams, Stefan Havlik, Dan Hawcutt, Liz Hawes, Nicola Hawes, Annie Hawkins, Nancy Hawkins, Daniel Hawley, Edward Haworth, Cathy Hay, Jamal Hayat, Anne Hayes, Melony Hayes, Fiona Hayes, Antara Hayman, Melanie Hayman, Matthew Haynes, Richard 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Highgate, Mondy Hikmat, Amanda Hill, Helen Hill, Joanne Hill, Phoebe Hill, Annette Hilldrith, Catherine Hillman-Cooper, Zoe Hilton, Alice Hindmarsh, Paul Hine, Kim Hinshaw, Clare Hird, Jemma Hives, Benson Ho, David Hobden, Gill Hobden, Maria Hobrok, Simon Hobson, Simon Hodge, Lesley Hodgen, Holly Hodgkins, Sally Hodgkinson, David Hodgson, Helen Hodgson, Luke Hodgson, Sheila Hodgson, Gemma Hodkinson, Kenneth Hodson, Matthew Hogben, Lucy Hogg, Lee Hoggett, Abigail Holborow, Becky Holbrook, Catherine Holbrook, Catherine Holden, Jill Holden, Melinda Holden, Thomas Holder, Hannah Holdsworth, Lisa Holland, Maureen Holland, Nicky Holland, Marie Hollands, Elizabeth Holliday, Nina Holling, Nigel Hollister, Laszlo Hollos, Evelyn Holmes, Megan Holmes, Raphael Holmes, Rebecca Holmes, Kelly Holroyd, Lyndsey Holt, Siobhan Holt, Alexandra Holyome, Marie Home, Kate Hong, Andrew Hood, Clare Hooper, Samantha Hope, Susan Hope, Bridget Hopkins, Peter W Horby, Stephanie Horler, Anil Hormis, Rachel Horn, Nicola Hornby, Sophie Horrocks, Latoya Horsford, Megan Horsford, Valana Horsham, Alex Horsley, Ashley Horsley, Elizabeth Horsley, Sarah Horton, Jane Hosea, Toby Hoskins, Ejaz Hossain, Muhammad S Hossain, Rashed Hossain, Maxine Hough, Sarah Hough, Kathryn Houghton, Rebecca Houlihan, Gordon Houston, Hamish Houston, Eleanor Hoverd, Roseanna Hovvels, Lee How, Laura Howaniec, Laura Howard, Linda Howard, Lucy Howard, Sarah Howard, Richard Howard-Griffin, Serena Howe, Mark Howells, Lyn Howie, Alex Howlett, Sophie Howlett, Josh Hrycaiczuk, Desmond Hsu, Hein Htet, Zaw Htet, Naing Z Htoon, Su Htwe, Ying Hu, Abby Huckle, Shahzya Huda, Alison Hudak, Lisa Hudig, Heather Hudson, Alison Hufton, Conor Huggins, Alistair Hughes, Emma Hughes, Gareth Hughes, Heather Hughes, Luke Hughes, Rachel Hughes, Rhian Hughes, Samantha Hughes, Stephen Hughes, Vikki Hughes, Wesley Hughes, Lukas Huhn, Ching Hui, Ruth Hulbert, Diana Hull, Robert Hull, Amanda Hulme, Peter Hulme, Wendy Hulse, George Hulston, Ryan Hum, Charlotte Humphrey, Alasdair Humphries, Joanne Humphries, David Hunt, Fiona Hunt, Jane Hunt, Kristen Hunt, Luke Hunt, Karl Hunter, Neil Hunter, Elizabeth Hurditch, Cian Hurley, Katrina Hurley, Mohammed A Husain, Syeda Y Husaini, Coralie Huson, Ibraar Hussain, Mohammad Hussain, Muhammad Hussain, Reda Hussain, Sajid Hussain, Samia Hussain, Sanniah Hussain, Yasmin Hussain, Mohammed Hussam El-Din, Rebecca Hussey, Christopher Hutchcroft, Camille Hutchinson, Dorothy Hutchinson, Elizabeth Hutchinson, John Hutchinson, Claire Hutsby, Paula Hutton, Daniella Hydes, Jamie Hyde-Wyatt, Niamh Hynes, Megan Hyslop, Ahmed Ibrahim, Asil Ibrahim, Kamal Ibrahim, Mohamed Ibrahim, Wadah Ibrahim, Muhammad Idrees, Hina Iftikhar, Chukwuemeka Igwe, Mohammad Ijaz, Amaju Ikomi, Clare Iles, Stamatina Iliodromiti, Lorna Ilves, La'ali Imam-Gutierrez, Christopher Imray, Haider Imtiaz, Jack Ingham, Julie Ingham, Rory Ingham, Tejas Ingle, Anne Ingram, Luke Ingram, Peter Inns, Ken Inweregbu, Andreea A Ionescu, Ana Ionita, Anil Ipe, Madiha Iqbal, Mohammed Iqbal, Faisal Iqbal Sait, Jane Ireland, Robert Irons, Mohannad Irshad, Muhammad S Irshad, Janice Irvine, Val Irvine, Robert Irving, Mina Ishak, Aminul Islam, Munim Islam, Abdurrahman Islim, Ali Ismail, Omar Ismail, M'hamedi Israa, Sharon Isralls, Shabana Issa, Monica Ivan, Chineze Ivenso, Ashleigh Ivy, Sophie Iwanikiw, Karen Ixer, Mia Iyer, Sriram Iyer, Alex Jackson, Amanda Jackson, Ben Jackson, Ella Jackson, Helen Jackson, Lauren Jackson, Melanie Jackson, Nicola Jackson, Shane Jackson, Patricia Jacob, Reni Jacob, Nicola Jacques, Anisa Jafar, Ali Jaffery, Vijay Jagannathan, Mandeep Jagpal, Fernandez R Jaime, Neemisha Jain, Seema Jain, Susan Jain, Sanjay Jaiswal, Danyal Jajbhay, Thomas Jaki, Bintou Jallow, Yusuf Jaly, Sabine Jamal, Yasmin Jameel, Albie James, Anneli James, Linda James, Mark James, Nicholas James, Olivia James, Ruth James, Tracy James, Jack Jameson, Eleana Jamieson, Aaron Jamison, Phoebe Jane, Azara Janmohamed, Lauren Jansen, 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Muhammad U Khalid, Amir Khalil, Asma Khalil, Sijjad Khalil, Ali Khan, Al-Imran Khan, Asad Khan, Burhan Khan, Fatimah Khan, Kausik Khan, Lubna Khan, Malik A Khan, Marria Khan, Mohammad Khan, Mohammed R Khan, Nayeem Khan, Omar Khan, Rahila Khan, Shabana Khan, Shahul Khan, Tasaduksultan Khan, Waseem Khan, Usman F Khatana, Jibran Khatri, Jyoti Khatri, Sagira Khatun, Taslima Khatun, Chuen R Khaw, Htet HE Khin, Kiran Khokhar, Najaf Khota, Chloe Khurana, Shruti Khurana, Faith Kibutu, Andrew Kidd, Michelle Kidd, Joe Kidney, Shane Kidney, Will Kieffer, Caroline Kilby, Eliz Kilich, Jee Whang Kim, Sarah Kimber, Andy King, Barbara King, Jennifer King, Kirsten King, Rachel King, Sam King, Victoria King, Emily King-Oakley, Laura Kingsmore, Fiona Kinney, Jeremy Kirk, Amy Kirkby, Emily Kirkham, Gemma Kirkman, Ursula Kirwan, Toby Kitching, Laura Kitto, Lauren Kittridge, Sarah Klaczek, Susan Kmachia, Christopher Knapp, Lucy Knibbs, Alicia Knight, Fraser Knight, Marian Knight, Sarah Knight, Steven 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Nyan M Kyi, Laura Kyle, Stuart Kyle, Karali-Tsilimpari Kyriaki, Julius Labao, Louise Lacey, Nikki Lack, Emma Ladlow, Heather Lafferty, Shondipon Laha, Sushil Lahane, Clement Lai, James Lai, Robert Laing, Inez Laing-Faiers, Emily Laity, Nicki Lakeman, David Lalloo, Fiona Lalloo, Sarah-Jane Lam, Fiona Lamb, Thomas Lamb, Pauline Lambert, Claudia Lameirinhas, Mohammed KG Lami, Michal Lamparski, Christine Lanaghan, Geraldine Landers, Martin J Landray, Matthew Lane, Nicholas Lane, Matilda Lang, Stephen Lang, Daniel Langer, Maggie Langley, Charles Langoya, Emily Langthorne, Helen Langton, Scott Latham, John Latham-Mollart, Afzal Latheef, Nang Latt, Dawn Lau, Eva Lau, Myra Laurenson, Hou Law, Jessica Law, Penny Law, Richard Law, Emma Lawrence, Ryan Lawrie, Michael Lay, Stephen Laybourne, Christine Laycock, Reina Layug, Maria Lazo, Amelia Lea, William Lea, Ian Leadbitter, Samuel Leafe, Thomas Leahy, Richard Lean, Lorna Leandro, Darren Leaning, Todd Leckie, Marie Anne Ledingham, Emma Lee, Hannah 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MacDonald, Sheila MacFadyen, James G MacFarlane, Jill Macfarlane, Laura Macfarlane, Lisa MacInnes, Jill MacIntyre, Kirsten Mack, Callum Mackay, Euan Mackay, Alexander Mackenzie, Matt Mackenzie, Robert MacKenzie Ross, Ami Mackey, Fiona Mackie, Robert Mackie, Carolyn Mackinlay, Claire Mackintosh, Katherine Mackintosh, Colin MacLeod, Mary J MacLeod, Michael Macmahon, Andrew MacNair, Catherine Macphee, Iain Macpherson, Allan MacRaild, Alannah Madden, Mary Madden, Norman Madeja, Paula Madgwick, Madhavi Madhusudhana, Alpha Madu, Lorraine Madziva, Marion Mafham, Emma Magavern, Negar Maghsoodi, Christopher Magier, Marios Magriplis, Natasha Mahabir, Subramanian Mahadevan-Bava, Anjanie Maharajh, Ajit Mahaveer, Bal Mahay, Hibo Mahdi, Thushika Mahendiran, Siva Mahendran, Sarah Maher, Shameera Maheswaran, Parisa Mahjoob-Afag, Ahmed Mahmood, Farhana Mahmood, Hager Mahmoud, Mian Mahmood, Waheed Mahmood, Ewan Mahony, Luke Mair, James Maitland, Rupert Major, Jaydip Majumdar, Mohammad KH Majumder, 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Martineau, Anthony Martinelli, Lauren Martinez, Jose Carlos Martinez Garrido, Vijay K Maruthamuthu, Roman Mary-Genetu, Sam Maryosh, Vidan Masani, Diego Maseda, Sheila Mashate, Yasaman Mashhoudi, Sanna Masih, Nick Maskell, Perry Maskell, Jon Maskill, Matthew Masoli, Richard Mason, Ruth Mason, Mohammad Masood, Mohammad T Masood, Syed Masood, Syed SME Masood, Lear Matapure, Cristina Matei, Manraj Matharu, Stephy Mathen, Nicole Mather, Jonathan Mathers, Joanna Matheson, Anna Mathew, Verghese Mathew, Jesha Mathews, Kate Mathias, Wadzanai Matimba-Mupaya, Nashaba Matin, Elina Matisa, Max Matonhodze, Elijah Matovu, Szeto Matthew, Alison Jane Matthews, Edgar Maureen, Maria-Emanuela Maxan, Adam Maxwell, Veronica Maxwell, James May, Joanne May, Peter May, Philippa May, Matthew Maycock, Shelley Mayor, Paul McAlinden, Audrey McAlpine, Graeme McAlpine, Jonathan McAndrew, Hamish McAuley, Sarah McAuliffe, James McBurney, Amanda McCairn, Jake McCammon, Conor McCann, Alexandra McCarrick, Brendan 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Southworth, Jason Sowter, Claudia Spalding, Enti Spata, Katie Spears, Mark Spears, Branwell Spencer, Gisele Spencer, Sue Spencer, Helen Spickett, Jennifer Spillane, William Spiller, Kerry Spinks, Michelle Spinks, Nick Spittle, Johanna Sporrer, Oliver Spring, Gemma Squires, Jack Squires, Rebecca Squires, Ram Sreenivasan, Ramesh Srinivasan, Asha Srirajamadhuveeti, Vino Srirathan, Sybil Stacpoole, Louise Stadon, Jennifer Stahle, Nikki Staines, Roxana Stanciu, Grazyna Stanczuk, Simon Stapley, Natalie Staplin, Michelle Starr, Rachael Stead, Conor Steele, Paula Stefanowska, Alison Stephens, David Stephensen, Courtney Stephenson, Elaine Stephenson, Monique Sterrenburg, Will Stevens, Amy Stevenson, Andrew Stevenson, Daniel Stevenson, Lesley Stevenson, Sarah Stevenson, Claire Stewart, Grant Stewart, Richard Stewart, Jo Stickley, Beverley Stidolph, Gemma Stiller, Sarah Stirrup, Sarah Stock, Alexander Stockdale, Lynne Stockham, Emma Stoddard, Chris Stokes, Ben Stone, Sarah Stone, Sophia Stone, Imogen Storey, Kim Storton, Frederick Stourton, Angela Strachan, Emma Stratton, Jane Stratton, Sam Straw, Dieter Streit, Emma Stride, Sally Stringer, Sophia Strong-Sheldrake, Siske Struik, Carmel Stuart, Anna Stubbs, Harrison Stubbs, Ann Sturdy, Sharon Sturney, Matt Stuttard, Cristina Suarez, Karuna Subba, Chris Subbe, Manjula Subramanian, Vaidyanathan Subramanian, Venkatram Subramanian, Chinari Subudhi, Rebecca Suckling, Srivatsan Sudershan, Taku Sugai, Peter Sugden, Rudresh Sukla, Ali Suliman, Fatimah Suliman, Sugrah Sultan, Alasdair Summers, Samyukta Sundar, Reka Sundhar, Nadia Sunni, Jay Suntharalingam, Amitava Sur, Dharmic Suresh, Shilpa Suresh, Michael Surtees, Danielle Suter, Helen Sutherland, Rebecca Sutherland, Dovile Sutinyte, Deborah Sutton, Sam Sutton, Amy Sutton-Cole, Mihaela Sutu, Marie-Louise Svensson, Sima Svirpliene, Andrew Swain, Thomas Swaine, Keshav Swarnkar, Tirion Swart, Ealish Swift, Pauline Swift, Rachael Swift, Rachel Swingler, Sophie Swinhoe, Katarzyna Swist-Szulik, Luke Swithenbank, Yasir Syed, Catriona Sykes, Daisy Sykes, Dominic Symon, Lesley Symon, Jen Syson, Gemma Szabo, Tamas Szakmany, Megan Szekely, Matthew Szeto, Maria Tadros, Lucy Tague, Hasan Tahir, Abigail Takyi, James Talbot-Ponsonby, Bradley Tallon, Bee TT Tan, Hock Tan, Huey Tan, WeiTeen Tan, Anand Tana, Rebecca Tangney, Christina Tanney, Tabitha Tanqueray, Emma Tanton, Hayley Tarft, Syed Tariq, David Tarpey, Antonia Tasiou, Elizabeth Tatam, Margaret Louise Tate, Paul Tate, Kate Tatham, Vera Tavoukjian, Alexander Taylor, Andrew Taylor, Beverley Taylor, Claire Taylor, David Taylor, Jennifer Taylor, Joanne Taylor, Julie Taylor, Karen Taylor, Leanne Taylor, Margaret Taylor, Matthew Taylor, Melanie Taylor, Natalie Taylor, Rachael Taylor, Rachel Taylor, Samantha Taylor, Suzanne Taylor, Tracey Taylor, Vicky Taylor, Michelle Taylor-Siddons, Thomas Taynton, Amelia Te, Jessica Teasdale, Julie Tebbutt, Caroline Tee, Adam Telfer, Vibha Teli, Jennifer Tempany, Julie Temple, Natalie Temple, Helen Tench, Yi He Teoh, Lynne Terrett, Louise Terry, Dariusz Tetla, Daniel Tewkesbury, Manish Thakker, Mini Thankachen, Dushen Tharmaratnam, Hilary Thatcher, Andrew Thayanandan, Krishna Thazhatheyil, Eaint Thein, Byron Theron, Phyu Thet, Kapeendran Thevarajah, Brinda Thillainathan, Yvette Thirlwall, Muthu Thirumaran, Alice Thomas, Amy Thomas, Andrew Thomas, Emma Thomas, Enson Thomas, Esther Thomas, Helen Thomas, James Thomas, Karen Thomas, Koshy Thomas, Lucy Thomas, Megan Thomas, Rachel Thomas, Rebecca Thomas, Rhys Thomas, Ruth Thomas, Sarah Thomas, Sherine Thomas, Tessy Thomas, Vicky Thomas, Rhian Thomas-Turner, Catherine Thompson, Christopher Thompson, Clara Thompson, Fiona Thompson, Katharine Thompson, Laura Thompson, Liz Thompson, Orla Thompson, Roger Thompson, Jason Thomson, Nicola Thomson, Natasha Thorn, Charlotte Thorne, Nicola Thorne, Jim Thornton, Richard Thornton, Sarah Thornton, Susan Thornton, Thomas Thornton, Christopher Thorpe, Sarah Thorpe, Paradeep Thozthumparambil, Laura Thrasyvoulou, Hannah Thraves, Guy Thwaites, Simon Tiberi, Serena Tieger, Carey Tierney, Caroline Tierney, Mark Tighe, Sorrell Tilbey, Amanda Tiller, Elizabeth Timlick, Hayley Timms, Anne-Marie Timoroksa, Samakomva Tinashe, Heather Tinkler, Marianne Tinkler, Jacqui Tipper, Sophie Tisi, Helen Tivenan, Suzy Tluk, Helen T-Michael, Anne Todd, Jackie Todd, Stacy Todd, Mohamed Tohfa, Melanie Tolson, Ana L Tomas, Natalia Tomasova, Sharon Tomlin, Simon Tomlins, Ivan Tonna, Kirsty Topham, Mathew Topping, Parisa Torabi, M Estee Torok, Mark Toshner, Ruhaif Tousis, Peter Tovey, Gareth Towersey, Jill Townley, Richard Tozer, Myat T Trafford, Helen Tranter, Christopher Travill, Sarah Traynor, Ascanio Tridente, Sanchia Triggs, Fiona Trim, Alex Trimmings, Tom Trinick, Maria Tripouki, Ashifa Trivedi, Dawn Trodd, Amy Trotter, Madeleine Trowsdale Stannard, Nigel Trudgill, Daniel Trushell-Pottinger, Maria Truslove, Shaun Trussell, Tariq Trussell, Kyriaki Tsakiridou, Christine Tsang, Peter Tsang, Kyriaki K Tsilimpari, Georgios Tsinaslanidis, Simon Tso, Sally Tucker, Aisha Tufail, Redmond Tully, Killian Turbitt, Rezon Turel, Tolga Turgut, Claudia Turley, Alison Turnbull, Aine Turner, Ash Turner, Charlotte Turner, Gail Turner, Kate Turner, Kelly Turner, Lucy Turner, Mark Turner, Patricia Turner, Sally Turner, Samantha Turner, Victoria Turner, Sharon Turney, Jon Turvey, Emma Twohey, Bhavya Tyagi, Vedang Tyagi, Abigail Tyer, Jayne Tyler, Jennifer Tyler, Alison Tyzack, Petros Tzavaras, Mohammad S Uddin, Ruhama Uddin, Salamat Ullah, Sanda Ullah, Athavan Umaipalan, Judith Umeadi, Akudo Umeh, Wilfred Umeojiako, Lizzie Undrell, Adam Unsworth, Veerpal SU Uppal, Gerry Upson, Alison Uriel, Sebastian Urruela, Hiromi Uru, Miranda Usher, Rebecca Usher, Andrew Ustianowski, Linda C Vaccari, Abhay Vaidya, Erika Vainieri, Bernardas Valecka, Jennifer Valentine, Balan Valeria, Luke Vamplew, Ekaterini Vamvakiti, Joannis Vamvakopoulos, Maud van de Venne, Alex van der Meer, Nora van der Stelt, Lynne Van Koutrik, Joseph Vance-Daniel, Rama Vancheeswaran, Samuel I Vandeyoon, Padma Vankayalapati, Chloe Vansomeren, William Van't Hoff, Sejal Vara, Anu Varghese, Maria Varghese, Teena Varghese, William Varney, Giulia Varnier, Valeria Vasadi, Vimal Vasu, Vasanthi Vasudevan, Ramu Vathenen, Manu Vatish, Heloyes Vayalaman, Christopher Vaz, Niki Veale, Bar Velan, Swati Velankar, Luxmi Velauthar, Neyme Veli, Nicola Vella, Anitha Velusamy, Ian Venables, Mavi Venditti, Ramya Venkataramakrishnan, Richard Venn, Robert Venn, Lyn Ventilacion, Mark Veres, Stefania Vergnano, Will Verling, Amit Verma, Britney Vernon, Nathan Vernon, Mark Vertue, Natalie Vethanayagam, Lucy Veys, Jennifer Vidler, Denise Vigni, Vinod W Vijayaraghavan Nalini, Enric Vilar, Neringa Vilimiene, Joseph Villiers, Sylvia Vinay, Latha Vinayakarao, Rachel Vincent, Rosie Vincent, Lisa Vincent Smith, Emma Virgilio, Abdullah M Virk, Elisa Visentin, Karunakaran Vithian, Alain Vuylsteke, Eleftheria Vyras, Richard Wach, Beverley Wadams, Susan Wadd, Natalia Waddington, Kirsten Wadsworth, Syed EI Wafa, Daniel Wagstaff, Lynda Wagstaff, Dalia Wahab, Zaroug Wahbi, Khalilullah Wahdati, Sawan Waidyanatha, Rachel Wake, Alice Wakefield, Emma Wakefield, Harry Wakefield, William Wakeford, Fiona Wakinshaw, Andrew Walden, Lorna Walding, Claire Walker, Ian Walker, Kevin Walker, Kim Walker, Linda Walker, Marie Walker, Rachel Walker, Susan Walker, Elaine Wall, Rebecca Wallbutton, Jessica Wallen, Karl Wallendszus, Arabella Waller, Michael Waller, Rosemary Waller, Gabriel Wallis, Louise Wallis, Donna Walsh, Elizabeth Walsh, Lani Walshaw, Daniel Walter, Holt Walters, Jocelyn Walters, Eileen Walton, Maggie Walton, Michael Walton, Olivia Walton, Susan Walton, Mandy Wan, Thin Wan, Mary Wands, Rachel Wane, Frank Wang, Nick Wang, Ran Wang, Deborah Warbrick, Samantha Warburton, Deborah Ward, Emma Ward, Katie Ward, Luke Ward, Rachael Ward, Thomas Ward, Scott A Warden, Steve Wardle, Hassan Wardy, Tobias Wareham, Scott Waring, Jenny Warmington, Ben Warner, Christian Warner, Lewis Warnock, Sarah Warran, Lisa Warren, Yolanda Warren, Hannah Warren-Miell, Hazel J Watchorn, Holly Waterfall, Abby Waters, Donald Waters, Mark Waterstone, Catherine Watkins, Catrin Watkins, Eleanor Watkins, Karen Watkins, Lynn Watkins, Adam JR Watson, Ekaterina Watson, Eleanor Watson, Paul Watson, Robert Watson, Malcolm Watters, Donna Watterson, Daniel Watts, John Watts, Merlin Watts, Victoria Waugh, Emma Wayman, Akhlaq Wazir, Nick Weatherly, Hayley Webb, Kathryn Webb, Stephen Webb, Ian Webster, Tim Webster, Ling Wee, Thanuja Weerasinghe, Janaka Weeratunga, Maria Weetman, Shuying Wei, Freshtah Weidi, Hugh Welch, James Welch, Leanne Welch, Steven Welch, Samantha Weller, Claire Wells, Susan Wellstead, Berni Welsh, RIchard Welsh, Ingeborg Welters, Rachael Welton, Lauren Wentworth, Kate Wesseldine, Magdelena West, Raha West, Ruth West, Sophie West, Heather Weston, Alice Westwood, Bill Wetherall, Helen Wheeler, Matthew Whelband, Amanda Whileman, Jenny Whitbread, Benjamin White, Catherine White, Christopher White, Duncan White, James White, Jonathan White, Katie White, Marie White, Nick White, Sarah White, Sonia White, Tracey White, Catherine Whitehead, Anne Whitehouse, Claire Whitehouse, Tony Whitehouse, Sophie Whiteley, Gabriel Whitlingum, Elizabeth Whittaker, Lindsay Whittam, Ashley Whittington, Helen Whittle, Eunice Wiafe, Lou Wiblin, John Widdrington, Jason Wieboldt, Hannah Wieringa, Cornelia Wiesender, Laura Wiffen, Andrew Wight, Christopher Wignall, Danielle Wilcock, Emma Wilcock, Louise Wilcox, Karen Wild, Laura Wild, Stephen Wild, Michael Wilde, Peter Wilding, Tracey Wildsmith, Joe Wileman, Joy Wiles, Kate Wiles, Elva Wilhelmsen, Thomas Wiliams, David Wilkin, Hannah Wilkins, Joy Wilkins, Suzanne Wilkins, Iain Wilkinson, Lesley Wilkinson, Nicola Wilkinson, Sophia Wilkinson, Susan Wilkinson, Tim Wilkinson, Sylvia Willetts, Alexandra Williams, Alison Williams, Angharad Williams, Ava Williams, Carl Williams, Caroline V Williams, Claire Williams, Dewi Williams, Felicity Williams, Gail Williams, Hannah Williams, James Williams, Jennie Williams, John Williams, Joseph JR Williams, Karen Williams, Kathryn Williams, Marie Williams, Matthew Williams, Patricia Williams, Penny Williams, Samson Williams, Sarah Williams, Sophie Williams, Tamanna Williams, Annie Williamson, Cath Williamson, Catherine Williamson, Dawn Williamson, James D Williamson, Elizabeth Willis, Emily Willis, Heather Willis, Herika Willis, Joanna Willis, Louise Wills, Lucy Willsher, Francesca Willson, Alison Wilson, Andrea Wilson, Antoinette Wilson, Debbie Wilson, James Wilson, Kate Wilson, Lucinda Wilson, Mark Wilson, Toni Wilson, Tim Wilson, Marlar Win, Tin T Win, Lucinda Winckworth, Laura Winder, Piers Winder, Nicola Window, Simon Winn, Carmen Winpenny, Helen Winslow, Martin Winstanley, Helen Winter, Jonathan Winter, Barbara Winter-Goodwin, Stephen Wisdom, Martin Wiselka, Sophie Wiseman, Steven Wishart, Eric Witele, Nicholas Withers, Janet Wittes, Donna Wixted, Nicola Wolff, Kirsten Wolffsohn, Rebecca Wolf-Roberts, Elena Wolodimeroff, Chi-Hung Wong, Edwin Wong, Jessica SY Wong, Kit Y Wong, Nick Wong, Sam Wong, Caroline Wood, Dianne Wood, Fiona Wood, Hannah Wood, Jennifer Wood, Joe Wood, Lisa Wood, Louise Wood, Michelle Wood, Stephen Wood, Tracy Wood, Katharine Woodall, Rebecca Woodfield, Christopher Woodford, Jill Woodford, Louise Woodhead, Timothy Woodhead, Philip Woodland, Marc Woodman, Jane Woods, Katherine Woods, Sarah Woods, Elizabeth Woodward, Zoe Woodward, Megan Woolcock, Gemma Wooldridge, Rebecca Woolf, Chris Woollard, Louisa Woollen, Emma Woolley, Jade Woolley, Daniel Woosey, Dan Wootton, Joanne Wootton, Stephy Worton, Jonathan Wraight, Lynn Wren, Caroline Wrey Brown, Demi Wright, Francesca Wright, Imogen Wright, Lianne Wright, Rachel Wright, Caroline Wroe, Henry Wu, Peishan Wu, Pensee Wu, Johnathan Wubetu, Retno Wulandari, Kim Wyness, Frederick Wyn-Griffiths, Inez Wynter, Bindhu Xavier, Zhongyang Xia, Masseh Yakubi, May Yan, Michael Yanney, Salima Yasmin, Bryan Yates, David Yates, Edward Yates, Helen Yates, Mark Yates, Charlotte Yearwood Martin, Khin Yein, Robert Yellon, Fiona Yelnoorkar, Peter Yew, Kawai Yip, Laura Ylquimiche Melly, Inez Ynter, Cissy Yong, Jemma Yorke, Abdel Younes Ibrahim, Gail Young, Louise Young, Sajeda Youssouf, Ahmed Yousuf, Chrissie Yu, Bernard Yung, Daniel Yusef, Anna-Sophia Zafar, Silvia Zagalo, Su Zaher, Kareem Zaki, Nabhan Zakir, Kasia Zalewska, Ane Zamalloa, Mohsin Zaman, Shakir Zaman, Julie Zamikula, Louise Zammit, Marie Zammit-Mangion, Ausra Zdanaviciene, Esther Zebracki, Daniel Zehnder, Lisa Zeidan, Xiaobei Zhao, Dongling Zheng, Jane Zhixin, Doreen Zhu, Madiha Zia, Omar Zibdeh, Rabia Zill-E-Huma, Ei T Zin, Vivian Zinyemba, Christos Zipitis, Arkadiusz Zmierczak, Azam Zubir, Naz Zuhra, Rasha Zulaikha, Carol Zullo, Ana Zuriaga-Alvaro, National Institute for Health Research, UK Research and Innovation, University of St Andrews. School of Medicine, Horby, PW, Roddick, A, Spata, E, Staplin, N, Emberson, JR, Pessoa-Amorim, G, Peto, L, Day, J, Thwaites, G, Mafham, M, Haynes, R, and Landray, MJ
Subjects: Male, medicine.medical_treatment, 030204 cardiovascular system & hematology, Azithromycin, ResearchInstitutes_Networks_Beacons/humanitarian_conflict_response_institute, Rate ratio, chemistry.chemical_compound, 0302 clinical medicine, RA0421, Oxygen therapy, RA0421 Public health. Hygiene. Preventive Medicine, 030212 general & internal medicine, Hospital Mortality, 11 Medical and Health Sciences, azithromycin, education.field_of_study, Covid19, clinical trial, General Medicine, 3rd-DAS, Middle Aged, Hospitalization, Survival Rate, Treatment Outcome, Humanitarian and Conflict Response Institute, Female, Life Sciences & Biomedicine, medicine.medical_specialty, RM, Population, COVID-19/drug therapy, Azithromycin/therapeutic use, 03 medical and health sciences, Pharmacotherapy, Tocilizumab, Medicine, General & Internal, SDG 3 - Good Health and Well-being, Internal medicine, General & Internal Medicine, medicine, Humans, education, Survival rate, Mechanical ventilation, Science & Technology, business.industry, COVID-19, NIS, Length of Stay, R1, United Kingdom, COVID-19 Drug Treatment, RM Therapeutics. Pharmacology, RECOVERY Collaborative Group, chemistry, Relative risk, business, RA, Anaesthesia Pain and Critical Care
Abstract: The RECOVERY trial is supported by a grant to the University of Oxford from UK Research and Innovation (Medical Research Council) and NIHR (MC_PC_19056) and by core funding provided by NIHR Oxford Biomedical Research Centre, Wellcome, the Bill & Melinda Gates Foundation, the Department for International Development, Health Data Research UK, the Medical Research Council Population Health Research Unit, the NIHR Health Protection Unit in Emerging and Zoonotic Infections, and NIHR Clinical Trials Unit Support Funding. TJ is supported by a grant from UK Medical Research Council (MC_UU_0002/14) and an NIHR Senior Research Fellowship (NIHR-SRF-2015-08-001). WSL is supported by core funding provided by NIHR Nottingham Biomedical Research Centre. Background: Azithromycin has been proposed as a treatment for COVID-19 on the basis of its immunomodulatory actions. We aimed to evaluate the safety and efficacy of azithromycin in patients admitted to hospital with COVID-19. Methods: In this randomised, controlled, open-label, adaptive platform trial (Randomised Evaluation of COVID-19 Therapy [RECOVERY]), several possible treatments were compared with usual care in patients admitted to hospital with COVID-19 in the UK. The trial is underway at 176 hospitals in the UK. Eligible and consenting patients were randomly allocated to either usual standard of care alone or usual standard of care plus azithromycin 500 mg once per day by mouth or intravenously for 10 days or until discharge (or allocation to one of the other RECOVERY treatment groups). Patients were assigned via web-based simple (unstratified) randomisation with allocation concealment and were twice as likely to be randomly assigned to usual care than to any of the active treatment groups. Participants and local study staff were not masked to the allocated treatment, but all others involved in the trial were masked to the outcome data during the trial. The primary outcome was 28-day all-cause mortality, assessed in the intention-to-treat population. The trial is registered with ISRCTN, 50189673, and ClinicalTrials.gov, NCT04381936. Findings: Between April 7 and Nov 27, 2020, of 16 442 patients enrolled in the RECOVERY trial, 9433 (57%) were eligible and 7763 were included in the assessment of azithromycin. The mean age of these study participants was 65·3 years (SD 15·7) and approximately a third were women (2944 [38%] of 7763). 2582 patients were randomly allocated to receive azithromycin and 5181 patients were randomly allocated to usual care alone. Overall, 561 (22%) patients allocated to azithromycin and 1162 (22%) patients allocated to usual care died within 28 days (rate ratio 0·97, 95% CI 0·87-1·07; p=0·50). No significant difference was seen in duration of hospital stay (median 10 days [IQR 5 to >28] vs 11 days [5 to >28]) or the proportion of patients discharged from hospital alive within 28 days (rate ratio 1·04, 95% CI 0·98-1·10; p=0·19). Among those not on invasive mechanical ventilation at baseline, no significant difference was seen in the proportion meeting the composite endpoint of invasive mechanical ventilation or death (risk ratio 0·95, 95% CI 0·87-1·03; p=0·24). Interpretation: In patients admitted to hospital with COVID-19, azithromycin did not improve survival or other prespecified clinical outcomes. Azithromycin use in patients admitted to hospital with COVID-19 should be restricted to patients in whom there is a clear antimicrobial indication. Publisher PDF
Published: 2021
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18. Adaptive Robust Local Online Density Estimation for Streaming Data

Author: Andrea Edwards, Zhide Fang, Zhong Chen, Kun Zhang, Victor S. Sheng, Jiabin Zhao, and Wei Fan
Subjects: Concept drift, Data stream mining, Computer science, Kernel density estimation, 02 engineering and technology, Density estimation, Ensemble learning, Article, Noise, Artificial Intelligence, Robustness (computer science), 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Computer Vision and Pattern Recognition, Algorithm, Software, Statistical hypothesis testing
Abstract: Accurate online density estimation is crucial to numerous applications that are prevalent with streaming data. Existing online approaches for density estimation somewhat lack prompt adaptability and robustness when facing concept-drifting and noisy streaming data, resulting in delayed or even deteriorated approximations. To alleviate this issue, in this work, we first propose an adaptive local online kernel density estimator (ALoKDE) for real-time density estimation on data streams. ALoKDE consists of two tightly integrated strategies: (1) a statistical test for concept drift detection and (2) an adaptive weighted local online density estimation when a drift does occur. Specifically, using a weighted form, ALoKDE seeks to provide an unbiased estimation by factoring in the statistical hallmarks of the latest learned distribution and any potential distributional changes that could be introduced by each incoming instance. A robust variant of ALoKDE, i.e., R-ALoKDE, is further developed to effectively handle data streams with varied types/levels of noise. Moreover, we analyze the asymptotic properties of ALoKDE and R-ALoKDE, and also derive their theoretical error bounds regarding bias, variance, MSE and MISE. Extensive comparative studies on various artificial and real-world (noisy) streaming data demonstrate the efficacies of ALoKDE and R-ALoKDE in online density estimation and real-time classification (with noise).
Published: 2021

19. Identification of Single Nucleotide Polymorphism in Red Clover (Trifolium pratense L.) Using Targeted Genomic Amplicon Sequencing and RNA-seq

Author: Randy D. Dinkins, Andrea Edwards, Heathcliffe Riday, Christina Riehle, and Wenli Li
Subjects: 0106 biological sciences, 0301 basic medicine, Genomics, Single-nucleotide polymorphism, RNA-Seq, Plant Science, Biology, lcsh:Plant culture, 01 natural sciences, Genome, self-incompatibility, DNA sequencing, 03 medical and health sciences, lcsh:SB1-1110, Original Research, Genetics, food and beverages, targeted amplicon sequencing, single-nucleotide polymorphism, biology.organism_classification, Medicago truncatula, Red Clover, 030104 developmental biology, red clover, Pentatricopeptide repeat, RNA-seq, 010606 plant biology & botany
Abstract: Red clover (Trifolium pratense L.) is a diploid, naturally cross-pollinated, cool-season species. As a perennial forage legume, red clover is mostly cultivated in temperate regions worldwide. Being a non-model crop species, genomic resources for red clover have been underdeveloped. Thus far, genomic analysis used in red clover has mainly relied on simple sequence repeat (SSR) markers. However, SSR markers are sparse in the genome and it is often difficult to unambiguously map them using short reads generated by next generation sequencing technology. Single nucleotide polymorphisms (SNPs) have been successfully applied in genomics assisted breeding in several agriculturally important species. Due to increasing importance of legumes in forage production, there is a clear need to develop SNP based markers for red clover that can be applied in breeding applications. In this study, we first developed an analytical pipeline that can confidently identify SNPs in a set of 72 different red clover genotypes using sequences generated by targeted amplicon sequencing. Then, with the same filtering stringency used in this pipeline, we used sequences from publicly available RNA-seq data to identify confident SNPs in different red clover varieties. Using this strategy, we have identified a total of 67,975 SNPs across red clover varieties. Among these, 28% (19,116) of them are missense mutations. Using Medicago truncatula as the reference, we annotated the regions affected by these missense mutations. We identified 2,909 protein coding regions with missense mutations. Pathway analysis of these coding regions indicated several biological processes impacted by these mutations. Specifically, three domains (homeobox domain, pentatricopeptide repeat containing plant-like, and regulator of Vps4 activity) were identified with five or more missense SNPs. These domain might also be a functional contributor in the molecular mechanisms of self-incompatibility in red clover. Future in-depth sequence diversity analysis of these three genes may yield valuable insights into the molecular mechanism involved in self-incompatibility in red clover.
Published: 2019
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20. Author Correction: Transcriptomics analysis of host liver and meta-transcriptome analysis of rumen epimural microbial community in young calves treated with artificial dosing of rumen content from adult donor cow

Author: Wenli Li, Andrea Edwards, Christina Riehle, Madison S. Cox, Sarah Raabis, Joseph H. Skarlupka, Andrew J. Steinberger, Jason Walling, Derek Bickhart, and Garret Suen
Subjects: Multidisciplinary, lcsh:R, ComputingMethodologies_DOCUMENTANDTEXTPROCESSING, lcsh:Medicine, lcsh:Q, lcsh:Science
Abstract: A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper.
Published: 2019
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21. Significant Prognostic Features and Patterns of Somatic TP53 Mutations in Human Cancers

Author: Andrea Edwards, Erik K. Flemington, Kun Zhang, and Wensheng Zhang
Subjects: 0301 basic medicine, Cancer Research, Microarray, Disease, Bioinformatics, 03 medical and health sciences, 0302 clinical medicine, Germline mutation, Breast cancer, medicine, Clinical significance, somatic mutation, TP53, Gene, neoplasms, Original Research, Cancer, Mutation Spectra, Proportional hazards model, business.industry, medicine.disease, clinical outcomes, 3. Good health, 030104 developmental biology, Oncology, 030220 oncology & carcinogenesis, prognosis, business
Abstract: TP53 is the most frequently altered gene in human cancers. Numerous retrospective studies have related its mutation and abnormal p53 protein expression to poor patient survival. Nonetheless, the clinical significance of TP53 ( p53) status has been a controversial issue. In this work, we aimed to characterize TP53 somatic mutations in tumor cells across multiple cancer types, primarily focusing on several less investigated features of the mutation spectra, and determine their prognostic implications. We performed an integrative study on the clinically annotated genomic data released by The Cancer Genome Atlas. Standard statistical methods, such as the Cox proportional hazards model and logistic regression, were used. This study resulted in several novel findings. They include the following: (1) similar to previously reported cases in breast cancer, the mutations in exons 1 to 4 of TP53 were more lethal than those in exons 5 to 9 for the patients with lung adenocarcinomas; (2) TP53 mutants tended to be negatively selected in mammalian evolution, but the evolutionary conservation had various clinical implications for different cancers; (3) conserved correlation patterns (ie, consistent co-occurrence or consistent mutual exclusivity) between TP53 mutations and the alterations in several other cancer genes (ie, PIK3CA, PTEN, KRAS, APC, CDKN2A, and ATM) were present in several cancers in which prognosis was associated with TP53 status and/or the mutational characteristics; (4) among TP53-mutated tumors, the total mutation burden in other driver genes was a predictive signature ( P < .05, false discovery rate
Published: 2017

22. Integrative Genomics and Transcriptomics Analysis Reveals Potential Mechanisms for Favorable Prognosis of Patients with HPV-Positive Head and Neck Carcinomas

Author: Kun Zhang, Wensheng Zhang, Zhide Fang, Erik K. Flemington, and Andrea Edwards
Subjects: 0301 basic medicine, DNA repair, Genomics, Biology, Bioinformatics, Article, 03 medical and health sciences, Carcinoma, medicine, Humans, Gene, Multidisciplinary, Gene Expression Profiling, Papillomavirus Infections, HPV infection, virus diseases, Prognosis, medicine.disease, female genital diseases and pregnancy complications, 3. Good health, Gene expression profiling, 030104 developmental biology, Head and Neck Neoplasms, Cancer cell, Carcinoma, Squamous Cell, Cancer research, DNA mismatch repair
Abstract: Patients with HPV-positive head neck squamous cell carcinomas (HNSCC) usually have a better prognosis than the HPV-negative cases while the underlying mechanism remains far from being well understood. We investigated this issue by an integrative analysis of clinically-annotated multi-omics HNSCC data released by the Cancer Genome Atlas. As confirmatory results, we found: (1) Co-occurrence of mutant TP53 and HPV infection was rare; (2) Regardless of HPV status, HNSCCs of wild-type TP53 implied a good survival chance for patients and had fewer genome-wide somatic mutations than those with a mutation burden on the gene. Our analysis further led to some novel observations. They included: (1) The genes involved in “DNA mismatch repair” pathway were up-regulated in HPV-positive tumors compared to normal tissue samples and HPV-negative cases, and thus constituted a strong predictive signature for the identification of HPV infection; (2) HPV infection could disrupt some regulatory miRNA-mRNA correlations operational in the HPV-negative tumors. In light of these results, we proposed a hypothesis for the favorable clinical outcomes of HPV-positive HNSCC patients. That is, the replication of HPV genome and/or its invasion into the genomes of cancer cells may enhance DNA repair mechanisms, which in turn limit the accumulation of lethal somatic mutations.
Published: 2016
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23. Increasing URM Undergraduate Student Success through Assessment-Driven Interventions: A Multiyear Study Using Freshman-Level General Biology as a Model System

Author: Ian R. Davenport, Andrea Edwards, Peter Barrett, Mary C. Carmichael, Candace St. Clair, Anup K. Kundu, Mohamed Awad, Shubha Kale Ireland, and Harris E. McFerrin
Subjects: 0301 basic medicine, Male, Risk, Educational measurement, Models, Educational, Evidence-based practice, Population, Psychological intervention, Biology, General Biochemistry, Genetics and Molecular Biology, Article, Education, Formative assessment, 03 medical and health sciences, Mathematics education, Data Mining, Humans, education, Students, Curriculum, At-risk students, Minority Groups, education.field_of_study, 05 social sciences, 050301 education, Curriculum-based measurement, 030104 developmental biology, Regression Analysis, Female, Educational Measurement, 0503 education
Abstract: Xavier University of Louisiana leads the nation in awarding BS degrees in the biological sciences to African-American students. In this multiyear study with ~5500 participants, data-driven interventions were adopted to improve student academic performance in a freshman-level general biology class., Xavier University of Louisiana leads the nation in awarding BS degrees in the biological sciences to African-American students. In this multiyear study with ∼5500 participants, data-driven interventions were adopted to improve student academic performance in a freshman-level general biology course. The three hour-long exams were common and administered concurrently to all students. New exam questions were developed using Bloom’s taxonomy, and exam results were analyzed statistically with validated assessment tools. All but the comprehensive final exam were returned to students for self-evaluation and remediation. Among other approaches, course rigor was monitored by using an identical set of 60 questions on the final exam across 10 semesters. Analysis of the identical sets of 60 final exam questions revealed that overall averages increased from 72.9% (2010) to 83.5% (2015). Regression analysis demonstrated a statistically significant correlation between high-risk students and their averages on the 60 questions. Additional analysis demonstrated statistically significant improvements for at least one letter grade from midterm to final and a 20% increase in the course pass rates over time, also for the high-risk population. These results support the hypothesis that our data-driven interventions and assessment techniques are successful in improving student retention, particularly for our academically at-risk students.
Published: 2016

24. Classifying Imbalanced Data Streams via Dynamic Feature Group Weighting with Importance Sampling

Author: Kun Zhang, Jing Gao, Ke Wu, Wei Fan, and Andrea Edwards
Subjects: Data stream, Computer science, Data stream mining, business.industry, Machine learning, computer.software_genre, Upper and lower bounds, Article, Weighting, ComputingMethodologies_PATTERNRECOGNITION, Discriminative model, Artificial intelligence, Data mining, Parallel running, business, computer, Classifier (UML), Importance sampling
Abstract: Data stream classification and imbalanced data learning are two important areas of data mining research. Each has been well studied to date with many interesting algorithms developed. However, only a few approaches reported in literature address the intersection of these two fields due to their complex interplay. In this work, we proposed an importance sampling driven, dynamic feature group weighting framework (DFGW-IS) for classifying data streams of imbalanced distribution. Two components are tightly incorporated into the proposed approach to address the intrinsic characteristics of concept-drifting, imbalanced streaming data. Specifically, the ever-evolving concepts are tackled by a weighted ensemble trained on a set of feature groups with each sub-classifier (i.e. a single classifier or an ensemble) weighed by its discriminative power and stable level. The un-even class distribution, on the other hand, is typically battled by the sub-classifier built in a specific feature group with the underlying distribution rebalanced by the importance sampling technique. We derived the theoretical upper bound for the generalization error of the proposed algorithm. We also studied the empirical performance of our method on a set of benchmark synthetic and real world data, and significant improvement has been achieved over the competing algorithms in terms of standard evaluation metrics and parallel running time. Algorithm implementations and datasets are available upon request.
Published: 2014
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25. miRNA-mRNA Correlation-Network Modules in Human Prostate Cancer and the Differences between Primary and Metastatic Tumor Subtypes

Author: Kun Zhang, Wei Fan, Wensheng Zhang, Erik K. Flemington, and Andrea Edwards
Subjects: Male, Transcription, Genetic, Gene regulatory network, lcsh:Medicine, Gene Expression, Bioinformatics, Prostate cancer, 0302 clinical medicine, RNA interference, Transforming Growth Factor beta, Cluster Analysis, Gene Regulatory Networks, Neoplasm Metastasis, lcsh:Science, Tissue homeostasis, 0303 health sciences, Multidisciplinary, Systems Biology, Prostate Cancer, Chromoplexy, Gene Expression Regulation, Neoplastic, Oncology, 030220 oncology & carcinogenesis, Medicine, Epigenetics, Algorithms, Research Article, Signal Transduction, Computational biology, Biology, Molecular Genetics, 03 medical and health sciences, medicine, Genetics, Cancer Genetics, Humans, Gene Regulation, RNA, Messenger, KEGG, Gene Networks, 030304 developmental biology, Regulatory Networks, Microarray analysis techniques, Gene Expression Profiling, lcsh:R, Cancer, Computational Biology, Cancers and Neoplasms, Prostatic Neoplasms, medicine.disease, Signaling Networks, Gene expression profiling, Genitourinary Tract Tumors, MicroRNAs, lcsh:Q, Gene Function
Abstract: Recent studies have shown the contribution of miRNAs to cancer pathogenesis. Prostate cancer is the most commonly diagnosed cancer in men. Unlike other major types of cancer, no single gene has been identified as being mutated in the majority of prostate tumors. This implies that the expression profiling of genes, including the non-coding miRNAs, may substantially vary across individual cases of this cancer. The within-class variability makes it possible to reconstruct or infer disease-specific miRNA-mRNA correlation and regulatory modular networks using high-dimensional microarray data of prostate tumor samples. Furthermore, since miRNAs and tumor suppressor genes are usually tissue specific, miRNA-mRNA modules could potentially differ between primary prostate cancer (PPC) and metastatic prostate cancer (MPC). We herein performed an in silico analysis to explore the miRNA-mRNA correlation network modules in the two tumor subtypes. Our analysis identified 5 miRNA-mRNA module pairs (MPs) for PPC and MPC, respectively. Each MP includes one positive-connection (correlation) module and one negative-connection (correlation) module. The number of miRNAs or mRNAs (genes) in each module varies from 2 to 8 or from 6 to 622. The modules discovered for PPC are more informative than those for MPC in terms of the implicated biological insights. In particular, one negative-connection module in PPC fits well with the popularly recognized miRNA-mediated post-transcriptional regulation theory. That is, the 3'UTR sequences of the involved mRNAs (∼620) are enriched with the target site motifs of the 7 modular miRNAs, has-miR-106b, -191, -19b, -92a, -92b, -93, and -141. About 330 GO terms and KEGG pathways, including TGF-beta signaling pathway that maintains tissue homeostasis and plays a crucial role in the suppression of the proliferation of cancer cells, are over-represented (adj.p
Published: 2012

26. miRNA-mediated relationships between Cis-SNP genotypes and transcript intensities in lymphocyte cell lines

Author: Prescott L. Deininger, Andrea Edwards, Kun Zhang, Wensheng Zhang, Erik K. Flemington, and Dongxiao Zhu
Subjects: Untranslated region, Linkage disequilibrium, Heredity, Genotype, In silico, lcsh:Medicine, Single-nucleotide polymorphism, Biology, Polymorphism, Single Nucleotide, Linkage Disequilibrium, Cell Line, Molecular Genetics, 03 medical and health sciences, 0302 clinical medicine, Gene expression, Genetics, Humans, Genetic Predisposition to Disease, Lymphocytes, RNA, Messenger, lcsh:Science, Gene, 3' Untranslated Regions, 030304 developmental biology, Regulation of gene expression, 0303 health sciences, Evolutionary Biology, Multidisciplinary, Binding Sites, Population Biology, Three prime untranslated region, lcsh:R, Computational Biology, MicroRNAs, Gene Expression Regulation, lcsh:Q, 030217 neurology & neurosurgery, Population Genetics, Research Article
Abstract: In metazoans, miRNAs regulate gene expression primarily through binding to target sites in the 3′ UTRs (untranslated regions) of messenger RNAs (mRNAs). Cis-acting variants within, or close to, a gene are crucial in explaining the variability of gene expression measures. Single nucleotide polymorphisms (SNPs) in the 3′ UTRs of genes can affect the base-pairing between miRNAs and mRNAs, and hence disrupt existing target sites (in the reference sequence) or create novel target sites, suggesting a possible mechanism for cis regulation of gene expression. Moreover, because the alleles of different SNPs within a DNA sequence of limited length tend to be in strong linkage disequilibrium (LD), we hypothesize the variants of miRNA target sites caused by SNPs potentially function as bridges linking the documented cis-SNP markers to the expression of the associated genes. A large-scale analysis was herein performed to test this hypothesis. By systematically integrating multiple latest information sources, we found 21 significant gene-level SNP-involved miRNA-mediated post-transcriptional regulation modules (SNP-MPRMs) in the form of SNP-miRNA-mRNA triplets in lymphocyte cell lines for the CEU and YRI populations. Among the cognate genes, six including ALG8, DGKE, GNA12, KLF11, LRPAP1, and MMAB are related to multiple genetic diseases such as depressive disorder and Type-II diabetes. Furthermore, we found that ∼35% of the documented transcript intensity-related cis-SNPs (∼950) in a recent publication are identical to, or in significant linkage disequilibrium (LD) (p
Published: 2012

27. Inferring the expression variability of human transposable element-derived exons by linear model analysis of deep RNA sequencing data

Author: Kun Zhang, Zhide Fang, Prescott L. Deininger, Wei Fan, Andrea Edwards, and Wensheng Zhang
Subjects: Untranslated region, Alu element, Gene Expression, Biology, 03 medical and health sciences, Exon, 0302 clinical medicine, Genetic model, Genetics, Humans, RNA, Messenger, 030304 developmental biology, Expressed Sequence Tags, 0303 health sciences, Expressed sequence tag, Models, Genetic, Genome, Human, Sequence Analysis, RNA, High-Throughput Nucleotide Sequencing, Exons, 030220 oncology & carcinogenesis, DNA Transposable Elements, Linear Models, Human genome, Tandem exon duplication, DNA microarray, Biotechnology, Research Article
Abstract: Background: The exonization of transposable elements (TEs) has proven to be a significant mechanism for the creation of novel exons. Existing knowledge of the retention patterns of TE exons in mRNAs were mainly established by the analysis of Expressed Sequence Tag (EST) data and microarray data. Results: This study seeks to validate and extend previous studies on the expression of TE exons by an integrative statistical analysis of high throughput RNA sequencing data. We collected 26 RNA-seq datasets spanning multiple tissues and cancer types. The exon-level digital expressions (indicating retention rates in mRNAs) were quantified by a double normalized measure, called the rescaled RPKM (Reads Per Kilobase of exon model per Million mapped reads). We analyzed the distribution profiles and the variability (across samples and between tissue/disease groups) of TE exon expressions, and compared them with those of other constitutive or cassette exons. We inferred the effects of four genomic factors, including the location, length, cognate TE family and TE nucleotide proportion (RTE, see Methods section) of a TE exon, on the exons’ expression level and expression variability. We also investigated the biological implications of an assembly of highly-expressed TE exons. Conclusion: Our analysis confirmed prior studies from the following four aspects. First, with relatively high expression variability, most TE exons in mRNAs, especially those without exact counterparts in the UCSC RefSeq (Reference Sequence) gene tables, demonstrate low but still detectable expression levels in most tissue samples. Second, the TE exons in coding DNA sequences (CDSs) are less highly expressed than those in 3′ (5′) untranslated regions (UTRs). Third, the exons derived from chronologically ancient repeat elements, such as MIRs, tend to be highly expressed in comparison with those derived from younger TEs. Fourth, the previously observed negative relationship between the lengths of exons and the inclusion levels in transcripts is also true for exonized TEs. Furthermore, our study resulted in several novel findings. They include: (1) for the TE exons with non-zero expression and as shown in most of the studied biological samples, a high TE nucleotide proportion leads to their lower retention rates in mRNAs; (2) the considered genomic features (i.e. a continuous variable such as the exon length or a category indicator such as 3′UTR) influence the expression level and the expression variability (CV) of TE exons in an inverse manner; (3) not only the exons derived from Alu elements but also the exons from the TEs of other families were preferentially established in zinc finger (ZNF) genes.
Published: 2013

28. Alu distribution and mutation types of cancer genes

Author: Andrea Edwards, Prescott L. Deininger, Wei Fan, Kun Zhang, and Wensheng Zhang
Subjects: endocrine system, lcsh:QH426-470, lcsh:Biotechnology, DNA Mutational Analysis, Alu element, Genes, Recessive, Biology, medicine.disease_cause, Genome, 03 medical and health sciences, Exon, 0302 clinical medicine, Alu Elements, lcsh:TP248.13-248.65, hemic and lymphatic diseases, Neoplasms, Genetics, medicine, Cluster Analysis, Humans, Gene, 030304 developmental biology, 0303 health sciences, Models, Statistical, Genome, Human, Intron, Exons, Introns, lcsh:Genetics, 030220 oncology & carcinogenesis, Mutation, Regression Analysis, Human genome, DNA microarray, Carcinogenesis, Biotechnology, Research Article, Genes, Neoplasm
Abstract: Background Alu elements are the most abundant retrotransposable elements comprising ~11% of the human genome. Many studies have highlighted the role that Alu elements have in genetic instability and how their contribution to the assortment of mutagenic events can lead to cancer. As of yet, little has been done to quantitatively assess the association between Alu distribution and genes that are causally implicated in oncogenesis. Results We have investigated the effect of various Alu densities on the mutation type based classifications of cancer genes. In order to establish the direct relationship between Alus and the cancer genes of interest, genome wide Alu-related densities were measured using genes rather than the sliding windows of fixed length as the units. Several novel genomic features, such as the density of the adjacent Alu pairs and the number of Alu-Exon-Alu triplets, were developed in order to extend the investigation via the multivariate statistical analysis toward more advanced biological insight. In addition, we characterized the genome-wide intron Alu distribution with a mixture model that distinguished genes containing Alu elements from those with no Alus, and evaluated the gene-level effect of the 5'-TTAAAA motif associated with Alu insertion sites using a two-step regression analysis method. Conclusions The study resulted in several novel findings worthy of further investigation. They include: (1) Recessive cancer genes (tumor suppressor genes) are enriched with Alu elements (p < 0.01) compared to dominant cancer genes (oncogenes) and the entire set of genes in the human genome; (2) Alu-related genomic features can be used to cluster cancer genes into biological meaningful groups; (3) The retention of exon Alus has been restricted in the human genome development, and an upper limit to the chromosome-level exon Alu densities is suggested by the distribution profile; (4) For the genes with at least one intron Alu repeat in individual chromosomes, the intron Alu densities can be well fitted by a Gamma distribution; (5) The effect of the 5'-TTAAAA motif on Alu densities varies across different chromosomes.
Published: 2011

29. The effect of the new GMS contract on GP appointment provision in Wales: postal questionnaire survey of practice managers

Author: Richard D Neal and Andrea Edwards
Subjects: Medicine(all), medicine.medical_specialty, Biochemistry, Genetics and Molecular Biology(all), Nurse practitioners, business.industry, lcsh:R, Short Report, lcsh:Medicine, General Medicine, General medical services, Minor (academic), Best interests, General Biochemistry, Genetics and Molecular Biology, Postal questionnaire, Incentive, lcsh:Biology (General), Nursing, Phone, Family medicine, Medicine, lcsh:Science (General), Telephone triage, business, lcsh:QH301-705.5, lcsh:Q1-390
Abstract: Background Access to health professionals is a key UK NHS priority, and meeting access targets is rewarded through the new General Medical Services (GMS) contract in the UK. We sought to determine the current state of appointment provision in Wales and any changes resulting from the need to meet indicators in the new GMS contract. We undertook a postal questionnaire study of practice managers in all general practices in Wales. Findings Valid responses were received from 396/505 (78.4%) practice managers. 361 (93.1%) practices reported that they had achieved the target for 2004/05. 104 (26%) practices reported that they were 100% open access/advanced access. The most frequent changes reported in response to the new GMS contract were offering more open or advanced access slots (237, 60%), more GP phone consultations (167, 42%), introducing a telephone triage system (100, 25%), introducing a minor illness clinic (76, 19%), and employing or training a nurse practitioner (59, 15%). 83% practice managers believed that patients were able to get an appointment at the time they need it either 'all of the time' or 'most of the time', and 70% that patients were able to get an appointment with the GP of choice either 'all of the time' or 'most of the time'. Conclusion This survey has demonstrated the current extent of appointment provision in Wales, and how changes have been driven by incentives. Whether these changes are in the best interests of either patients or doctors, or both, remains to be seen.
Published: 2008

30. svdPPCS: an effective singular valuedecomposition-based method for conserved anddivergent co-expression gene moduleidentification.

Author: Wensheng Zhang, Andrea Edwards, Wei Fan, Dongxiao Zhu, and Kun Zhang
Subjects: *GENE expression, *GENETIC regulation, *REGRESSION analysis, *TISSUE banks, *HISTOLOGY, *BIOTECHNOLOGY
Abstract: Background: Comparative analysis of gene expression profiling of multiple biological categories, such as different species of organisms or different kinds of tissue, promises to enhance the fundamental understanding of the universality as well as the specialization of mechanisms and related biological themes. Grouping genes with a similar expression pattern or exhibiting co-expression together is a starting point in understanding and analyzing gene expression data. In recent literature, gene module level analysis is advocated in order to understand biological network design and system behaviors in disease and life processes; however, practical difficulties often lie in the implementation of existing methods. Results: Using the singular value decomposition (SVD) technique, we developed a new computational tool, named svdPPCS (SVD-based Pattern Pairing and Chart Splitting), to identify conserved and divergent co-expression modules of two sets of microarray experiments. In the proposed methods, gene modules are identified by splitting the two-way chart coordinated with a pair of left singular vectors factorized from the gene expression matrices of the two biological categories. Importantly, the cutoffs are determined by a data-driven algorithm using the well-defined statistic, SVD-p. The implementation was illustrated on two time series microarray data sets generated from the samples of accessory gland (ACG) and malpighian tubule (MT) tissues of the line W118 of M. drosophila. Two conserved modules and six divergent modules, each of which has a unique characteristic profile across tissue kinds and aging processes, were identified. The number of genes contained in these models ranged from five to a few hundred. Three to over a hundred GO terms were over-represented in individual modules with FDR < 0.1. One divergent module suggested the tissue-specific relationship between the expressions of mitochondrion-related genes and the aging process. This finding, together with others, may be of biological significance. The validity of the proposed SVD-based method was further verified by a simulation study, as well as the comparisons with regression analysis and cubic spline regression analysis plus PAM based clustering. Conclusions: svdPPCS is a novel computational tool for the comparative analysis of transcriptional profiling. It especially fits the comparison of time series data of related organisms or different tissues of the same organism under equivalent or similar experimental conditions. The general scheme can be directly extended to the comparisons of multiple data sets. It also can be applied to the integration of data sets from different platforms and of different sources. [ABSTRACT FROM AUTHOR]
Published: 2010
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31. In vitro and in vivo Effects of Combination of Trastuzumab (Herceptin) and Tamoxifen in Breast Cancer.

Author: Chun-Xia Wang, Debbie C. Koay, Andrea Edwards, Zhao Lu, Gil Mor, Idris T. Ocal, and Michael P. DiGiovanna
Abstract: Summary Extensive interactions between estrogen receptor α (ERα) and HER2 signaling pathways have been described. Using BT-474 human breast cancer cells, we have previously shown that the combination of tamoxifen (TAM) and Herceptin results in strong synergistic growth inhibition, enhancement of G0–G1 cell cycle accumulation, inhibition of HER2 activity and a cytostatic effect without cell death. To further examine the underlying mechanism of synergy, we investigated the effect of this drug combination on ERα function and growth factor downstream signaling. TAM caused a small increase in ERα levels while Herceptin had no effect, and both drugs caused an increase in the level of Ser118-phosphorylated ERα. However, both TAM and Herceptin individually inhibited ERα transcriptional activity, although the combination did not have a greater effect than either single agent. Herceptin inhibited MAPK and Akt activity, while TAM had no effect on these either as a single agent or when added to Herceptin. Using a BALB/c athymic BT-474 in vivo xenograft model, the drug combination (Herceptin 0.3 mg/kg i.p. twice weekly, TAM 1.0 mg/mouse i.p. three times per week) showed a greater inhibition of tumor growth compared to either single agent. Tumor extracts and fixed sections were examined at the end of the treatment period for treatment-specific alterations: we noted a paradoxical proliferation-inducing effect of TAM that was reversed by the addition of Herceptin. Our results indicate that combined targeting of both peptide growth factor receptors and ERα represents a promising breast cancer treatment strategy. [ABSTRACT FROM AUTHOR]
Published: 2005
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31 results on '"Andrea Edwards"'

1. Quitting smoking after a cancer diagnosis is associated with high-risk neutrophil-to-lymphocyte ratio among tobacco use-related cancer survivors

2. Influence of CYP2D6 metabolizer status on ondansetron efficacy in pediatric patients undergoing hematopoietic stem cell transplantation: A case series

3. Cot-side imaging of functional connectivity in the developing brain during sleep using wearable high-density diffuse optical tomography

4. Trajectory of post-traumatic stress and depression among children and adolescents following single-incident trauma

5. Insufficient Lycopene Intake Is Associated With High Risk of Prostate Cancer: A Cross-Sectional Study From the National Health and Nutrition Examination Survey (2003–2010)

6. Seeking the exclusive binding region of phenylalkylamine derivatives on human T‐type calcium channels via homology modeling and molecular dynamics simulation approach

7. Functional imaging of the developing brain with wearable high-density diffuse optical tomography: A new benchmark for infant neuroimaging outside the scanner environment

8. Inferring Personalized and Race-Specific Causal Effects of Genomic Aberrations on Gleason Scores: A Deep Latent Variable Model

9. Identification of Single Nucleotide Polymorphism in Red Clover (Trifolium pratense L.) Using Targeted Genomic Amplicon Sequencing and RNA-seq

10. Significant Prognostic Features and Patterns of Somatic Mutations in Human Cancers

11. Mapping cortical haemodynamics during neonatal seizures using diffuse optical tomography: A case study

12. Somatic mutations favorable to patient survival are predominant in ovarian carcinomas.

13. Inferring polymorphism-induced regulatory gene networks active in human lymphocyte cell lines by weighted linear mixed model analysis of multiple RNA-Seq datasets.

14. The sequence structures of human microRNA molecules and their implications.

15. miRNA-mRNA correlation-network modules in human prostate cancer and the differences between primary and metastatic tumor subtypes.

16. miRNA-mediated relationships between Cis-SNP genotypes and transcript intensities in lymphocyte cell lines.

17. Azithromycin in patients admitted to hospital with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial

18. Adaptive Robust Local Online Density Estimation for Streaming Data

19. Identification of Single Nucleotide Polymorphism in Red Clover (Trifolium pratense L.) Using Targeted Genomic Amplicon Sequencing and RNA-seq

20. Author Correction: Transcriptomics analysis of host liver and meta-transcriptome analysis of rumen epimural microbial community in young calves treated with artificial dosing of rumen content from adult donor cow

21. Significant Prognostic Features and Patterns of Somatic TP53 Mutations in Human Cancers

22. Integrative Genomics and Transcriptomics Analysis Reveals Potential Mechanisms for Favorable Prognosis of Patients with HPV-Positive Head and Neck Carcinomas

23. Increasing URM Undergraduate Student Success through Assessment-Driven Interventions: A Multiyear Study Using Freshman-Level General Biology as a Model System

24. Classifying Imbalanced Data Streams via Dynamic Feature Group Weighting with Importance Sampling

25. miRNA-mRNA Correlation-Network Modules in Human Prostate Cancer and the Differences between Primary and Metastatic Tumor Subtypes

26. miRNA-mediated relationships between Cis-SNP genotypes and transcript intensities in lymphocyte cell lines

27. Inferring the expression variability of human transposable element-derived exons by linear model analysis of deep RNA sequencing data

28. Alu distribution and mutation types of cancer genes

29. The effect of the new GMS contract on GP appointment provision in Wales: postal questionnaire survey of practice managers

30. svdPPCS: an effective singular valuedecomposition-based method for conserved anddivergent co-expression gene moduleidentification.

31. In vitro and in vivo Effects of Combination of Trastuzumab (Herceptin) and Tamoxifen in Breast Cancer.

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31 results on '"Andrea Edwards"'

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