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151. Scientific Abstract to Full Paper: Publication Rate over a 3-Year Period in a Malaysian Clinical Research Conference

Author: Nicholas Yee Liang Hing, Xin Ci Wong, Pei Xuan Kuan, Mohan Dass Pathmanathan, Mohd Aizuddin Abdul Rahman, and Kalaiarasu M. Peariasamy
Subjects: abstracts, manuscripts, collaboration, conversion rate, impact factor, publication rate, Communication. Mass media, P87-96, Information resources (General), ZA3040-5185
Abstract: Background: The publication rates of abstracts after they were presented at the National Conference for Clinical Research (NCCR), a scientific conference held in Malaysia, was determined to gauge the scientific value of the conference, whilst providing comparative information with other scientific conferences. Methods: All the abstracts that were presented at the NCCR from 2014 to 2016 were analysed. Keywords from the abstract title, along with the first, second, and last author’s name, were searched via PubMed, Google Scholar, and Scopus to determine publication status. Results: A total of 320 abstracts were analysed. Of those, 57 abstracts (17.8%) were published. Almost 70% of published abstracts appeared in open access journals that charge article processing fees. Early publications (≤18 months from the conference date) had higher median journal impact factors compared to later publications. Approximately 42% of the published abstracts had collaborations with the Institute for Clinical Research (ICR) or Clinical Research Centres (CRCs). An increasing number of authors in an abstract and having the first author from a research centre, reduced and increased the odds of publication, respectively. Conclusions: The NCCR publication rate is lower compared to the reported average in other scientific conferences abroad. More encouragement and support to publish should be provided to the presenting authors. Clinicians should also be encouraged to collaborate with research centres such as those from the ICR or CRCs to boost publication likelihoods.
Published: 2022
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152. Modelling the effect of gap junctions on tissue-level cardiac electrophysiology

Author: Bruce, Doug, Pathmanathan, Pras, and Whiteley, Jonathan P.
Subjects: Computer Science - Computational Engineering, Finance, and Science, Physics - Biological Physics, Quantitative Biology - Cell Behavior, Quantitative Biology - Tissues and Organs
Abstract: When modelling tissue-level cardiac electrophysiology, continuum approximations to the discrete cell-level equations are used to maintain computational tractability. One of the most commonly used models is represented by the bidomain equations, the derivation of which relies on a homogenisation technique to construct a suitable approximation to the discrete model. This derivation does not explicitly account for the presence of gap junctions connecting one cell to another. It has been seen experimentally [Rohr, Cardiovasc. Res. 2004] that these gap junctions have a marked effect on the propagation of the action potential, specifically as the upstroke of the wave passes through the gap junction. In this paper we explicitly include gap junctions in a both a 2D discrete model of cardiac electrophysiology, and the corresponding continuum model, on a simplified cell geometry. Using these models we compare the results of simulations using both continuum and discrete systems. We see that the form of the action potential as it passes through gap junctions cannot be replicated using a continuum model, and that the underlying propagation speed of the action potential ceases to match up between models when gap junctions are introduced. In addition, the results of the discrete simulations match the characteristics of those shown in Rohr 2004. From this, we suggest that a hybrid model -- a discrete system following the upstroke of the action potential, and a continuum system elsewhere -- may give a more accurate description of cardiac electrophysiology., Comment: In Proceedings HSB 2012, arXiv:1208.3151
Published: 2012
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153. Credibility Assessment of a Subject-Specific Mathematical Model of Blood Volume Kinetics for Prediction of Physiological Response to Hemorrhagic Shock and Fluid Resuscitation

Author: Bahram Parvinian, Ramin Bighamian, Christopher George Scully, Jin-Oh Hahn, and Pras Pathmanathan
Subjects: subject-specific, model credibility assessment, workflow, model validation, fluid resuscitation, mathematical model, Physiology, QP1-981
Abstract: Subject-specific mathematical models for prediction of physiological parameters such as blood volume, cardiac output, and blood pressure in response to hemorrhage have been developed. In silico studies using these models may provide an effective tool to generate pre-clinical safety evidence for medical devices and help reduce the size and scope of animal studies that are performed prior to initiation of human trials. To achieve such a goal, the credibility of the mathematical model must be established for the purpose of pre-clinical in silico testing. In this work, the credibility of a subject-specific mathematical model of blood volume kinetics intended to predict blood volume response to hemorrhage and fluid resuscitation during fluid therapy was evaluated. A workflow was used in which: (i) the foundational properties of the mathematical model such as structural identifiability were evaluated; (ii) practical identifiability was evaluated both pre- and post-calibration, with the pre-calibration results used to determine an optimal splitting of experimental data into calibration and validation datasets; (iii) uncertainty in model parameters and the experimental uncertainty were quantified for each subject; and (iv) the uncertainty was propagated through the blood volume kinetics model and its predictive capability was evaluated via validation tests. The mathematical model was found to be structurally identifiable. Pre-calibration identifiability analysis led to splitting the 180 min of time series data per subject into 50 and 130 min calibration and validation windows, respectively. The average root mean squared error of the mathematical model was 12.6% using the calibration window of (0 min, 50 min). Practical identifiability was established post-calibration after fixing one of the parameters to a nominal value. In the validation tests, 82 and 75% of the subject-specific mathematical models were able to correctly predict blood volume response when predictive capability was evaluated at 180 min and at the time when amount of infused fluid equals fluid loss.
Published: 2021
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154. Stent Embolization Mimicking Acute Myocardial Infarction

Author: Syed Arqum Huda MD, Sara Akram Kahlown MD, Anojan Pathmanathan MD, Muhammad Saad Farooqi FACC, and Mark Charlamb MD
Subjects: Medicine (General), R5-920, Pathology, RB1-214
Abstract: Venous thromboembolism is associated with significant morbidity and mortality if left untreated. Anticoagulation is the cornerstone of treatment. Venous stents are a relatively newer entity that are increasingly being used to treat venous stenosis/occlusion. It is a safe procedure, but complications include vein rupture, arterial puncture, retroperitoneal bleeding, and in-stent thrombosis. Stent migration is a rare but potentially fatal complication. We present a case of venous stent embolization to the heart that presented as a non-ST segment elevation myocardial infarction.
Published: 2021
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155. A Full-Stack Application for Detecting Seizures and Reducing Data During Continuous Electroencephalogram Monitoring

Author: John M. Bernabei, BSE, Olaoluwa Owoputi, BSE, Shyon D. Small, BS, Nathaniel T. Nyema, BS, Elom Dumenyo, BS, Joongwon Kim, Steven N. Baldassano, MD, PhD, Christopher Painter, MS, Erin C. Conrad, MD, Taneeta M. Ganguly, MD, Ramani Balu, MD, PhD, Kathryn A. Davis, MD, Joshua M. Levine, MD, Jay Pathmanathan, MD, PhD, and Brian Litt, MD
Subjects: Medical emergencies. Critical care. Intensive care. First aid, RC86-88.9
Abstract: BACKGROUND:. Continuous electroencephalogram monitoring is associated with lower mortality in critically ill patients; however, it is underused due to the resource-intensive nature of manually interpreting prolonged streams of continuous electroencephalogram data. Here, we present a novel real-time, machine learning–based alerting and monitoring system for epilepsy and seizures that dramatically reduces the amount of manual electroencephalogram review. METHODS:. We developed a custom data reduction algorithm using a random forest and deployed it within an online cloud-based platform, which streams data and communicates interactively with caregivers via a web interface to display algorithm results. We developed real-time, machine learning–based alerting and monitoring system for epilepsy and seizures on continuous electroencephalogram recordings from 77 patients undergoing routine scalp ICU electroencephalogram monitoring and tested it on an additional 20 patients. RESULTS. We achieved a mean seizure sensitivity of 84% in cross-validation and 85% in testing, as well as a mean specificity of 83% in cross-validation and 86% in testing, corresponding to a high level of data reduction. This study validates a platform for machine learning–assisted continuous electroencephalogram analysis and represents a meaningful step toward improving utility and decreasing cost of continuous electroencephalogram monitoring. We also make our high-quality annotated dataset of 97 ICU continuous electroencephalogram recordings public for others to validate and improve upon our methods.
Published: 2021
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156. Genome-wide association study identifies 25 known breast cancer susceptibility loci as risk factors for triple-negative breast cancer.

Author: Purrington, Kristen, Slager, Susan, Eccles, Diana, Yannoukakos, Drakoulis, Fasching, Peter, Miron, Penelope, Carpenter, Jane, Chang-Claude, Jenny, Martin, Nicholas, Montgomery, Grant, Kristensen, Vessela, Goodfellow, Paul, Tapper, William, Rafiq, Sajjad, Gerty, Susan, Durcan, Lorraine, Konstantopoulou, Irene, Fostira, Florentia, Vratimos, Athanassios, Apostolou, Paraskevi, Konstanta, Irene, Kotoula, Vassiliki, Lakis, Sotiris, Dimopoulos, Meletios, Skarlos, Dimosthenis, Pectasides, Dimitrios, Fountzilas, George, Beckmann, Matthias, Hein, Alexander, Ruebner, Matthias, Ekici, Arif, Hartmann, Arndt, Schulz-Wendtland, Ruediger, Renner, Stefan, Janni, Wolfgang, Rack, Brigitte, Scholz, Christoph, Neugebauer, Julia, Andergassen, Ulrich, Lux, Michael, Haeberle, Lothar, Clarke, Christine, Pathmanathan, Nirmala, Rudolph, Anja, Flesch-Janys, Dieter, Nickels, Stefan, Olson, Janet, Ingle, James, Olswold, Curtis, Slettedahl, Seth, Eckel-Passow, Jeanette, Anderson, S, Visscher, Daniel, Cafourek, Victoria, Sicotte, Hugues, Prodduturi, Naresh, Weiderpass, Elisabete, Bernstein, Leslie, Ivanovich, Jennifer, Giles, Graham, Baglietto, Laura, Southey, Melissa, Kosma, Veli-Matti, Fischer, Hans-Peter, Reed, Malcom, Cross, Simon, Deming-Halverson, Sandra, Shrubsole, Martha, Cai, Qiuyin, Shu, Xiao-Ou, Daly, Mary, Weaver, Joellen, Ross, Eric, Klemp, Jennifer, Sharma, Priyanka, Torres, Diana, Rüdiger, Thomas, Wölfing, Heidrun, Ulmer, Hans-Ulrich, Försti, Asta, Khoury, Thaer, Kumar, Shicha, Pilarski, Robert, Shapiro, Charles, Greco, Dario, Heikkilä, Päivi, Aittomäki, Kristiina, Blomqvist, Carl, Irwanto, Astrid, Liu, Jianjun, Pankratz, Vernon, Wang, Xianshu, Severi, Gianluca, Mannermaa, Arto, Easton, Douglas, Hall, Per, Brauch, Hiltrud, Cox, Angela, Zheng, Wei, and Godwin, Andrew
Subjects: Adult, Aged, Aged, 80 and over, Case-Control Studies, Chromosomes, Human, Pair 19, Estrogen Receptor alpha, Female, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Middle Aged, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Triple Negative Breast Neoplasms, Young Adult
Abstract: Triple-negative (TN) breast cancer is an aggressive subtype of breast cancer associated with a unique set of epidemiologic and genetic risk factors. We conducted a two-stage genome-wide association study of TN breast cancer (stage 1: 1529 TN cases, 3399 controls; stage 2: 2148 cases, 1309 controls) to identify loci that influence TN breast cancer risk. Variants in the 19p13.1 and PTHLH loci showed genome-wide significant associations (P < 5 × 10(-) (8)) in stage 1 and 2 combined. Results also suggested a substantial enrichment of significantly associated variants among the single nucleotide polymorphisms (SNPs) analyzed in stage 2. Variants from 25 of 74 known breast cancer susceptibility loci were also associated with risk of TN breast cancer (P < 0.05). Associations with TN breast cancer were confirmed for 10 loci (LGR6, MDM4, CASP8, 2q35, 2p24.1, TERT-rs10069690, ESR1, TOX3, 19p13.1, RALY), and we identified associations with TN breast cancer for 15 additional breast cancer loci (P < 0.05: PEX14, 2q24.1, 2q31.1, ADAM29, EBF1, TCF7L2, 11q13.1, 11q24.3, 12p13.1, PTHLH, NTN4, 12q24, BRCA2, RAD51L1-rs2588809, MKL1). Further, two SNPs independent of previously reported signals in ESR1 [rs12525163 odds ratio (OR) = 1.15, P = 4.9 × 10(-) (4)] and 19p13.1 (rs1864112 OR = 0.84, P = 1.8 × 10(-) (9)) were associated with TN breast cancer. A polygenic risk score (PRS) for TN breast cancer based on known breast cancer risk variants showed a 4-fold difference in risk between the highest and lowest PRS quintiles (OR = 4.03, 95% confidence interval 3.46-4.70, P = 4.8 × 10(-) (69)). This translates to an absolute risk for TN breast cancer ranging from 0.8% to 3.4%, suggesting that genetic variation may be used for TN breast cancer risk prediction.
Published: 2014

157. The Tendon Injury Response is Influenced by Decorin and Biglycan

Author: Dunkman, Andrew A, Buckley, Mark R, Mienaltowski, Michael J, Adams, Sheila M, Thomas, Stephen J, Satchell, Lauren, Kumar, Akash, Pathmanathan, Lydia, Beason, David P, Iozzo, Renato V, Birk, David E, and Soslowsky, Louis J
Subjects: Physical Injury - Accidents and Adverse Effects, Aetiology, 2.1 Biological and endogenous factors, Musculoskeletal, Animals, Biglycan, Collagen, Decorin, Disease Models, Animal, Female, Gene Expression Regulation, Mice, Mice, Knockout, Patellar Ligament, Tendon Injuries, Tendon, Injury, Proteoglycan, Extracellular matrix, SLRP, Healing
Abstract: Defining the constituent regulatory molecules in tendon is critical to understanding the process of tendon repair and instructive to the development of novel treatment modalities. The purpose of this study is to define the structural, expressional, and mechanical changes in the tendon injury response, and elucidate the roles of two class I small leucine-rich proteoglycans (SLRPs). We utilized biglycan-null, decorin-null and wild type mice with an established patellar tendon injury model. Mechanical testing demonstrated functional changes associated with injury and the incomplete recapitulation of mechanical properties after 6 weeks. In addition, SLRP deficiency influenced the mechanical properties with a marked lack of improvement between 3 and 6 weeks in decorin-null tendons. Morphological analyses of the injury response and role of SLRPs demonstrated alterations in cell density and shape as well as collagen alignment and fibril structure resulting from injury. SLRP gene expression was studied using RT-qPCR with alterations in expression associated with the injured tendons. Our results show that in the absence of biglycan initial healing may be impaired while in the absence of decorin later healing is clearly diminished. This suggests that biglycan and decorin may have sequential roles in the tendon response to injury.
Published: 2014

158. Machine learning for major food crops breeding: Applications, challenges, and ways forward.

Author: N. Govaichelvan, Kumanan, Pathmanathan, Dharini, Zainal‐Abidin, Rabiatul‐Adawiah, and Abu, Arpah
Abstract: Increasing the production of the three major food crops (MFCs), maize (Zea mays), rice (Oryza sativa), and wheat (Triticum aestivum), is essential to fulfilling the food demand for the growing human population. Increasing food production may require the integration of machine learning (ML) into plant breeding programs. However, developing ML tools to improve the production of MFCs is a daunting task due to the lack of quality data and the computation resources needed to process this information. Hence, this review discusses the recent applications of ML for improving MFCs production, including plant phenotyping, yield forecasting, and candidate gene prediction. Based on the challenges reported in recent ML experiments for MFCs, this review prescribes solutions to produce scalable ML models. This review provides valuable insights for future studies and promotes collective efforts among researchers implementing ML to enhance MFCs productivity. Core Ideas: Implementation of scalable machine learning models for major food crop breeding is in the exploratory phase.Integrative approaches enhance the use of machine learning for crop breeding.Scalability of machine learning depends on data quality, modeling techniques, and computational resources.Coordinated efforts are required in developing interoperable datasets and scalable machine learning tools. [ABSTRACT FROM AUTHOR]
Published: 2024
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159. Capture of complete ciliate chromosomes in single sequencing reads reveals widespread chromosome isoforms

Author: Kelsi A. Lindblad, Jananan S. Pathmanathan, Sandrine Moreira, John R. Bracht, Robert P. Sebra, Elizabeth R. Hutton, and Laura F. Landweber
Subjects: Ciliate, Oxytricha, PacBio, SMRT sequencing, Genome assembly, Alternative fragmentation, Biotechnology, TP248.13-248.65, Genetics, QH426-470
Abstract: Abstract Background Whole-genome shotgun sequencing, which stitches together millions of short sequencing reads into a single genome, ushered in the era of modern genomics and led to a rapid expansion of the number of genome sequences available. Nevertheless, assembly of short reads remains difficult, resulting in fragmented genome sequences. Ultimately, only a sequencing technology capable of capturing complete chromosomes in a single run could resolve all ambiguities. Even “third generation” sequencing technologies produce reads far shorter than most eukaryotic chromosomes. However, the ciliate Oxytricha trifallax has a somatic genome with thousands of chromosomes averaging only 3.2 kbp, making it an ideal candidate for exploring the benefits of sequencing whole chromosomes without assembly. Results We used single-molecule real-time sequencing to capture thousands of complete chromosomes in single reads and to update the published Oxytricha trifallax JRB310 genome assembly. In this version, over 50% of the completed chromosomes with two telomeres derive from single reads. The improved assembly includes over 12,000 new chromosome isoforms, and demonstrates that somatic chromosomes derive from variable rearrangements between somatic segments encoded up to 191,000 base pairs away. However, while long reads reduce the need for assembly, a hybrid approach that supplements long-read sequencing with short reads for error correction produced the most complete and accurate assembly, overall. Conclusions This assembly provides the first example of complete eukaryotic chromosomes captured by single sequencing reads and demonstrates that traditional approaches to genome assembly can mask considerable structural variation.
Published: 2019
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160. Measuring the Success of Women Led 'Dry Zone Home Gardening' Program with Organic Amendments in Sri Lanka

Author: T M Kularathne, Pathmanathan Sivashankar, and W G.C Wekumbura
Subjects: Sri Lanka, Home gardening, Organic amendments, Women, Agriculture (General), S1-972, Business, HF5001-6182
Abstract: This paper attempts to measure the level of success of the dry zone home gardening (HG) program introduced by the government of Sri Lanka among the women. The women used the organic amendments for their HG activities. This study assessed the success of the program and the constraints faced in engaging in home gardening using organic amendments, and solutions to overcome were identified. Using an interviewer administrated structured questionnaire and purposive sampling technique, data were collected from 80 beneficiaries in Thirappane DS division. The results revealed that the beneficiaries were mainly married, unemployed, with no primary education and in more than 50 years’ age category. There was significant relationship between two socio-economic factors, farming factors, reasons to engage in home gardening with organic amendments, and outcomes of the program. Majority of women perceived that all the purposes of the program were successfully achieved. But 91.25 % of women have no idea on farming with organic amendments at large scale. Most influencing reason was the resulting low productivity with organic amendments. To address these solutions, the government should intervene to build the large scale farming with organic amendments in Anuradhapura district.
Published: 2019
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161. Forecasting the Volatility of Cryptocurrencies in the Presence of COVID-19 with the State Space Model and Kalman Filter

Author: Shafiqah Azman, Dharini Pathmanathan, and Aerambamoorthy Thavaneswaran
Subjects: cryptocurrency, volatility, state space model, Kalman filter, Mathematics, QA1-939
Abstract: During the COVID-19 pandemic, cryptocurrency prices showed abnormal volatility that attracted the participation of many investors. Studying the behaviour of volatility for the prices of cryptocurrency is an interesting problem to be investigated. This research implements the state space model framework for volatility incorporating the Kalman filter. This method directly forecasts the conditional volatility of five cryptocurrency prices (Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), Litecoin (LTC) and Bitcoin Cash (BCH)) for 10,000 consecutive hours, i.e., approximately 417 days during the COVID-19 pandemic from 26 February 2020, 00:00 h until 18 April 2021, 00:00 h. The performance of this model is compared to the GARCH (1,1) model and the neural network autoregressive (NNAR) based on root mean square error (RMSE), mean absolute error (MAE) and the volatility plot. The autocorrelation function plot, histogram and the residuals plot are used to examine the model adequacy. Among the three models, the state space model gives the best fit. The state space model gives the narrowest confidence interval of volatility and value-at-risk forecasts among the three models.
Published: 2022
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162. Rheometry for Concrete 3D Printing: A Review and an Experimental Comparison

Author: Roshan Jayathilakage, Pathmanathan Rajeev, and Jay Sanjayan
Subjects: 3D concrete printing, rheology, rheometry, extrusion, yield stress, viscosity, Building construction, TH1-9745
Abstract: The rapid advancement of 3D concrete printing (3DCP) and the development of relevant cementitious material compositions can be seen in the last few decades. The commonly used 3DCP method is to build the structure layer by layer after extruding the material through a nozzle. Initially, the pumping and extrusion of the material should be done with considerable fluidity and workability. The extruded layers should retain their shape immediately after extruding and depositing. While constructing the structure in a layerwise manner, the bottom layers should have enough early age strength to support the layers at the top. Therefore, at different processes in 3DCP, the rheological requirement is contradictory. As the rheology of the material is the deterministic factor which decides the fluidity or workability of the mix, proper rheological characterization should be completed accurately. In some instances, due to the higher stiffness, and higher time and rate-dependent material behavior (thixotropic behavior) compared to the conventional concrete, standard rheology measurement techniques have many limitations when used for 3DCP material. Therefore, non-conventional and novel techniques can be implemented with suitable material models to characterize the rheology of 3DCP material. In this study, a comprehensive review was conducted on conventional and non-conventional methods used for characterizing the rheological parameters for 3DCP material. The previously conducted studies were highlighted with the targeted 3DCP processes in the study (if applicable), and rheological parameters achieved from the test (i.e., yield stress, viscosity, and thixotropy). In addition, some experimental studies were conducted to compare several selected testing methods. The rheological parameters achieved from different test methods were compared to identify the similarities, dissimilarities, pros, and cons between the test methods. Furthermore, the extrudability and buildability studies were conducted for the mixes to demonstrate the usage of the mixes in 3DCP applications and to correlate the achieved rheological parameters with these processes.
Published: 2022
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163. Residual stress-strain relationship for the biochar-based mortar after exposure to elevated temperature

Author: Satheeskumar Navaratnam, Hendrik Wijaya, Pathmanathan Rajeev, Priyan Mendis, and Kate Nguyen
Subjects: Biochar, Compressive strength, Compressive stress-strain relationship, Elevated temperature, Materials of engineering and construction. Mechanics of materials, TA401-492
Abstract: The effects of elevated temperature on the compressive strength and stress-strain behaviour of biochar-based mortar were investigated in this research. A total of 39 cylindrical (100 × 200 mm) specimens were prepared with four mortar mixtures by replacing 0%, 5%, 10 % and 20 % of cement weight with biochar. The specimens were cured for 28 days and exposed to elevated temperatures of 200 °C, 450 °C and 700 °C before testing. Results show that increasing replacement rate of biochar reduces the compressive strength at room temperature as well as residual strength after being exposed to elevated temperature. The 5% weight of cement replaced by biochar performed better than other specimen at elevated temperature as it retains about 88 %, 76 % and 38 % of compressive strength, respectively after being exposed to 200 °C, 450 °C and 700 °C. The study also established the relationship among biochar mix ratio, elevated temperature, elastic modulus and peak compressive strength. Finally, a new stress-strain model was developed and validated to predict the axial behaviour of biochar-based mortar with varying biochar mix ratio and elevated temperature. This model can be used to predict the post fire mechanical behaviour of biochar-based mortar.
Published: 2021
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164. Genetic variation in high light responses of Theobroma cacao L. accessions

Author: Vernessa R. Lewis, Aidan D. Farrell, Pathmanathan Umaharan, and Adrian M. Lennon
Subjects: Ascorbate peroxidase, Photoinhibition, Photosynthesis, Reactive oxygen species, Superoxide dismutase, Science (General), Q1-390, Social sciences (General), H1-99
Abstract: Cacao (Theobroma cacao L.) is a shade-tolerant tree species, but in recent years it has increasingly been cultivated under full sun conditions in an orchard system where photoinhibition is likely. Here we investigate the extent of photoinhibition in 17 cacao accessions from a range of genetic groups, growing under high light conditions. The ability of the photosynthetic systems to respond to high light was assessed using chlorophyll fluorescence parameters (diurnal Fv/Fm and instantaneous light response curves), and differences in photosynthetic pigment content were compared using biochemical assays. Damage due to photoinhibition was assessed using electrolyte leakage, lipid peroxidation, and reactive oxygen species scavenging systems were compared using biochemical assays (for APX, CAT and SOD). There was significant variation between the 17 accessions for photosynthetic parameters, although in all cases the light saturation points were well below the midday light levels. Light acclimation of photosynthetic pigments was evident and variation in the total chlorophyll to total carotenoid ratio was significantly correlated with electrolyte leakage. Significant genetic variation was observed across the 17 accessions in the activities of CAT, APX and SOD. Across all accessions, photoprotection appeared to be restricted by the ability of leaves to generate SOD. Significant negative correlations were observed between SOD activity and both APX activity and electrolyte leakage, while significant positive correlations were observed between electrolyte leakage and both APX and CAT activity. Accessions with higher light saturation points, as well as high carotenoid and high SOD concentrations were able to tolerate the moderately high light, however, none of the accessions were clearly superior to the commonly grown Amelonado accession. The results imply that screening for SOD activity, total carotenoid content and light saturation point can aid in selection of genotypes with better tolerance to high light.
Published: 2021
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165. Proteomic Analysis on Anti-Proliferative and Apoptosis Effects of Curcumin Analog, 1,5-bis(4-Hydroxy-3-Methyoxyphenyl)-1,4-Pentadiene-3-One-Treated Human Glioblastoma and Neuroblastoma Cells

Author: Yee Qian Lee, Pathmanathan Rajadurai, Faridah Abas, Iekhsan Othman, and Rakesh Naidu
Subjects: glioblastoma, neuroblastoma, diarylpentanoids, cytotoxicity, anti-proliferation, apoptosis, Biology (General), QH301-705.5
Abstract: Curcumin analogs with excellent biological properties have been synthesized to address and overcome the poor pharmacokinetic profiles of curcumin. This study aims to investigate the cytotoxicity, anti-proliferative, and apoptosis-inducing ability of curcumin analog, MS13 on human glioblastoma U-87 MG, and neuroblastoma SH-SY5Y cells, and to examine the global proteome changes in these cells following treatment. Our current findings showed that MS13 induced potent cytotoxicity and anti-proliferative effects on both cells. Increased caspase-3 activity and decreased bcl-2 concentration upon treatment indicate that MS13 induces apoptosis in these cells in a dose- and time-dependent manner. The label-free shotgun proteomic analysis has defined the protein profiles in both glioblastoma and neuroblastoma cells, whereby a total of nine common DEPs, inclusive of glyceraldehyde 3-phosphate dehydrogenase (GAPDH), alpha-enolase (ENO1), heat shock protein HSP 90-alpha (HSP90AA1), Heat shock protein HSP 90-beta (HSP90AB1), Eukaryotic translation initiation factor 5A-1 (EFI5A), heterogenous nuclear ribonucleoprotein K (HNRNPK), tubulin beta chain (TUBB), histone H2AX (H2AFX), and Protein SET were identified. Pathway analysis further elucidated that MS13 may induce its anti-tumor effects in both cells via the common enriched pathways, “Glycolysis” and “Post-translational protein modification.” Conclusively, MS13 demonstrates an anti-cancer effect that may indicate its potential use in the management of brain malignancies.
Published: 2021
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166. Environmental challenges induced by extensive use of face masks during COVID-19: A review and potential solutions

Author: Kajanan Selvaranjan, Satheeskumar Navaratnam, Pathmanathan Rajeev, and Nishanthan Ravintherakumaran
Subjects: Micro plastic pollution, Sustainable, Upcycling, Face mask waste, Environmental sciences, GE1-350
Abstract: The ongoing COVID-19 disease significantly affects not only human health, it also affects the wealth of country’ economy and everyday routine of human life. To control the spread of the virus, face mask is used as primary personal protective equipment (PPE). Thus, the production and usage of face masks significantly increase as the COVID-19 pandemic still escalating. Further, most of these masks contain plastics or other derivatives of plastics. Therefore, this extensive usage of face masks generates million tons of plastic wastes to the environments in a short span of time. This study aims to investigate the environmental impact induced by face mask wastes and sustainable solution to reduce this waste. An online survey was carried out to identify the types of face mask and number of masks used per week by an individual from 1033 people. Based on this survey and available literature, this study quantifies the amount of plastics waste generated by face masks. However, this survey was limited with certain ages, country and durations (July–August 2020). Thus, the prediction of plastic waste generation, only provide fundamental knowledge about the mask wastes. Results revealed that there is a huge plastic waste remained in land and marine environment in the form of mask waste, which will contribute to micro-plastic pollution. Therefore, this paper also highlights the sustainable approach to the mask production by integrating the use of natural plant fiber in the woven face mask technology to reduce the plastic waste induced by masks. Further, upcycling the mask waste and producing construction materials also discussed.
Published: 2021
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167. Transcribed germline-limited coding sequences in Oxytricha trifallax

Author: Richard V Miller, Rafik Neme, Derek M Clay, Jananan S Pathmanathan, Michael W Lu, V Talya Yerlici, Jaspreet S Khurana, and Laura F Landweber
Subjects: Genetics, QH426-470
Abstract: AbstractThe germline-soma divide is a fundamental distinction in developmental biology, and different genes are expressed in germline and somatic cells throughout metazoan life cycles. Ciliates, a group of microbial eukaryotes, exhibit germline-somatic nuclear dimorphism within a single cell with two different genomes. The ciliate Oxytricha trifallaxO. trifallax
Published: 2021
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168. Transmission dynamics, clinical characteristics and sero-surveillance in the COVID-19 outbreak in a population dense area of Colombo, Sri Lanka April- May 2020

Author: Chandima Jeewandara, Dinuka Guruge, Deshni Jayathilaka, Panambara Arachchige Deshan Madhusanka, Pradeep Darshana Pushpakumara, Shyrar Tanussiya Ramu, Inoka Sepali Aberathna, Danasekara Rallage Saubhagya Rasikangani Danasekara, Thilagaraj Pathmanathan, Buddhini Gunatilaka, Sauni Malavige, Yowan Dias, Ruwan Wijayamuni, Graham S. Ogg, and Gathsaurie Neelika Malavige
Subjects: Medicine, Science
Abstract: Background The transmission dynamics of SARS-CoV-2 varies depending on social distancing measures, circulating SARS-CoV-2 variants, host factors and other environmental factors. We sought to investigate the clinical and epidemiological characteristics of a SARS-CoV-2 outbreak that occurred in a highly dense population area in Colombo, Sri Lanka from April to May 2020. Methodology/principal findings We carried out RT-qPCR for SARS-CoV2, assessed the SARS-CoV-2 specific total and neutralizing antibodies (Nabs) in a densely packed, underserved settlement (n = 2722) after identification of the index case on 15th April 2020. 89/2722 individuals were detected as infected by RT-qPCR with a secondary attack rate among close contacts being 0.077 (95% CI 0.063–0.095). Another 30 asymptomatic individuals were found to have had COVID-19 based on the presence of SARS-CoV-2 specific antibodies. However, only 61.5% of those who were initially seropositive for SARS-CoV-2 had detectable total antibodies at 120 to 160 days, while only 40.6% had detectable Nabs. 74/89 (83.1%) of RT-qPCR positive individuals were completely asymptomatic and all 15 (16.9%) who experienced symptoms were classified as having a mild illness. 18 (20.2%) were between the ages of 61 to 80. 11/89 (12.4%) had diabetes, 8/89 (9%) had cardiovascular disease and 4 (4.5%) had asthma. Of the two viruses that were sequenced and were of the B.1 and B.4 lineages with one carrying the D614G mutation. Discussion/conclusion Almost all infected individuals developed mild or asymptomatic illness despite the presence of comorbid illnesses. Since the majority of those who were in this underserved settlement were not infected despite circulation of the D614G variant, it would be important to further study environmental and host factors that lead to disease severity and transmission.
Published: 2021

169. Comparative transcriptomic analysis reveals key components controlling spathe color in Anthurium andraeanum (Hort.)

Author: Jaime A. Osorio-Guarín, David Gopaulchan, Corey Quanckenbush, Adrian M. Lennon, Pathmanathan Umaharan, and Omar E. Cornejo
Subjects: Medicine, Science
Abstract: Anthurium andraeanum (Hort.) is an important ornamental in the tropical cut-flower industry. However, there is currently insufficient information to establish a clear connection between the genetic model(s) proposed and the putative genes involved in the differentiation between colors. In this study, 18 cDNA libraries related to the spathe color and developmental stages of A. andraeanum were characterized by transcriptome sequencing (RNA-seq). For the de novo transcriptome, a total of 114,334,082 primary sequence reads were obtained from the Illumina sequencer and were assembled into 151,652 unigenes. Approximately 58,476 transcripts were generated and used for comparative transcriptome analysis between three cultivars that differ in spathe color (‘Sasha’ (white), ‘Honduras’ (red), and ‘Rapido’ (purple)). A large number of differentially expressed genes (8,324), potentially involved in multiple biological and metabolic pathways, were identified, including genes in the flavonoid and anthocyanin biosynthetic pathways. Our results showed that the chalcone isomerase (CHI) gene presented the strongest evidence for an association with differences in color and the highest correlation with other key genes (flavanone 3-hydroxylase (F3H), flavonoid 3’5’ hydroxylase (F3’5’H)/ flavonoid 3’-hydroxylase (F3’H), and leucoanthocyanidin dioxygenase (LDOX)) in the anthocyanin pathway. We also identified a differentially expressed cytochrome P450 gene in the late developmental stage of the purple spathe that appeared to determine the difference between the red- and purple-colored spathes. Furthermore, transcription factors related to putative MYB-domain protein that may control anthocyanin pathway were identified through a weighted gene co-expression network analysis (WGCNA). The results provided basic sequence information for future research on spathe color, which have important implications for this ornamental breeding strategies.
Published: 2021

170. HIV testing and ART initiation among partners, family members, and high-risk associates of index clients participating in the CommLink linkage case management program, Eswatini, 2016-2018.

Author: Daniel Williams, Duncan MacKellar, Makhosazana Dlamini, Johnita Byrd, Lenhle Dube, Phumzile Mndzebele, Sikhathele Mazibuko, Trong Ao, Ishani Pathmanathan, Alysha Beyer, and Caroline Ryan
Subjects: Medicine, Science
Abstract: To help diagnose and initiate antiretroviral therapy (ART) for ≥95% of all persons living with HIV (PLHIV), the World Health Organization (WHO) recommends offering HIV testing to biological children, and sexual and needle-sharing partners of all PLHIV (index-client testing, ICT). Many index clients, however, do not identify or have contactable partners, and often substantially fewer than 95% of HIV-positive partners initiate ART soon after index testing. To help improve early HIV diagnosis and ART initiation in Eswatini (formerly Swaziland), we implemented a community-based HIV testing and peer-delivered, linkage case management program (CommLink) that provided ICT as part of a comprehensive package of WHO recommended linkage services. CommLink was implemented June 2015 -March 2017 (Phase I), and April 2017 -September 2018 (Phase II). In addition to biological children and partners, HIV testing was offered to adult family members (Phases I and II) and high-risk associates including friends and acquaintances (Phase II) of CommLink index clients. Compared with Phase I, in Phase II proportionally more CommLink clients disclosed their HIV-infection status to a partner or family member [94% (562/598) vs. 75% (486/652)], and had ≥1 partners, family members, or high-risk associates (contacts) tested through CommLink [41% (245/598) vs. 18% (117/652)]. Of 537 contacts tested, 253 (47%) were HIV-positive and not currently in HIV care, including 17% (17/100) of family members aged
Published: 2021
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171. Urinary leukotrienes and histamine in patients with varying severity of acute dengue.

Author: Tehani Silva, Chandima Jeewandara, Laksiri Gomes, Chathurika Gangani, Sameera D Mahapatuna, Thilagaraj Pathmanathan, Ananda Wijewickrama, Graham S Ogg, and Gathsaurie Neelika Malavige
Subjects: Medicine, Science
Abstract: BackgroundVascular leak is a hallmark of severe dengue, and high leukotriene levels have been observed in dengue mouse models, suggesting a role in disease pathogenesis. We sought to explore their role in acute dengue, by assessing levels of urinary LTE4 and urinary histamine in patients with varying severity of acute dengue.MethodsUrinary LTE4, histamine and creatinine were measured by a quantitative ELISA, in healthy individuals (n = 19), patients with dengue fever (DF = 72) and dengue haemorrhagic fever DHF (n = 48). The kinetics of LTE4 and histamine and diurnal variations were assessed in a subset of patients.ResultsUrinary LTE4 levels were significantly higher (p = 0.004) in patients who proceed to develop DHF when compared to patients with DF during early illness (≤ 4 days) and during the critical phase (p = 0.02), which continued to rise in patients who developed DHF during the course of illness. However, LTE4 is unlikely to be a good biomarker as ROCs gave an AUC value of 0.67 (95% CI 0.57 and 0.76), which was nevertheless significant (p = 0.002). Urinary LTE4 levels did not associate with the degree of viraemia, infecting virus serotype and was not different in those with primary vs secondary dengue. Urinary histamine levels were significantly high in patients with acute dengue although no difference was observed between patients with DF and DHF and again did not associate with the viraemia. Interestingly, LTE4, histamine and the viral loads showed a marked diurnal variation in both patients with DF and DHF.ConclusionsOur data suggest that LTE4 could play a role in disease pathogenesis and since there are safe and effective cysteinyl leukotriene receptor blockers, it would be important to assess their efficacy in reducing dengue disease severity.
Published: 2021
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172. Expression and functional relevance of long non-coding RNAs in acute myeloid leukemia stem cells

Author: Bill, Marius, Papaioannou, Dimitrios, Karunasiri, Malith, Kohlschmidt, Jessica, Pepe, Felice, Walker, Christopher J., Walker, Allison E., Brannan, Zachary, Pathmanathan, Aparna, Zhang, Xiaoli, Mrózek, Krzysztof, LaRocco, Allison, Volinia, Stefano, Bloomfield, Clara D., Garzon, Ramiro, and Dorrance, Adrienne M.
Published: 2019
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173. Suitable intensity measure for probabilistic seismic risk assessment of non-ductile Australian reinforced concrete buildings

Author: Amirsardari, Anita, Rajeev, Pathmanathan, Lumantarna, Elisa, and Goldsworthy, Helen M.
Published: 2019
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174. Morphological and genetic diversity of cacao (Theobroma cacao L.) in Uganda

Author: Gopaulchan, David, Motilal, Lambert A., Bekele, Frances L., Clause, Séverine, Ariko, James O., Ejang, Harriet P., and Umaharan, Pathmanathan
Published: 2019
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175. Clinical characteristics and risk factors for severe COVID-19 infections in Malaysia: A nationwide observational study

Author: Benedict Lim Heng Sim, MRCP, Suresh Kumar Chidambaram, MRCP, Xin Ci Wong, MDataSc, Mohan Dass Pathmanathan, MD, Kalaiarasu M. Peariasamy, FDSRCS, Chee Peng Hor, MSc, Hiu Jian Chua, MRCP, and Pik Pin Goh, FRCS
Subjects: Public aspects of medicine, RA1-1270
Abstract: Summary: Background: COVID-19 emerged as a major public health outbreak in late 2019. Malaysia reported its first imported case on 25th January 2020, and adopted a policy of extensive contact tracing and hospitalising of all cases. We describe the clinical characteristics of COVID-19 cases nationwide and determine the risk factors associated with disease severity. Method: Clinical records of all RT-PCR confirmed COVID-19 cases aged ≥12 years admitted to 18 designated hospitals in Malaysia between 1st February and 30th May 2020 with complete outcomes were retrieved. Epidemiological history, co-morbidities, clinical features, investigations, management and complications were captured using REDCap database. Variables were compared between mild and severe diseases. Univariate and multivariate regression were used to identify determinants for disease severity. Findings: The sample comprised of 5889 cases (median age 34 years, male 71.7%). Majority were mild (92%), and 3.3% required intensive care, with 80% admitted within the first five days. Older age (≥51 years), underlying chronic kidney disease and chronic pulmonary disease, fever, cough, diarrhoea, breathlessness, tachypnoea, abnormal chest radiographs and high serum CRP (≥5 mg/dL) on admission were significant determinants for severity (p
Published: 2020
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176. Data-Driven Uncertainty Quantification for Cardiac Electrophysiological Models: Impact of Physiological Variability on Action Potential and Spiral Wave Dynamics

Author: Pras Pathmanathan, Suran K. Galappaththige, Jonathan M. Cordeiro, Abouzar Kaboudian, Flavio H. Fenton, and Richard A. Gray
Subjects: uncertainty quantification, sensitivity analysis, variability, electrophysiology, correlation, Physiology, QP1-981
Abstract: Computational modeling of cardiac electrophysiology (EP) has recently transitioned from a scientific research tool to clinical applications. To ensure reliability of clinical or regulatory decisions made using cardiac EP models, it is vital to evaluate the uncertainty in model predictions. Model predictions are uncertain because there is typically substantial uncertainty in model input parameters, due to measurement error or natural variability. While there has been much recent uncertainty quantification (UQ) research for cardiac EP models, all previous work has been limited by either: (i) considering uncertainty in only a subset of the full set of parameters; and/or (ii) assigning arbitrary variation to parameters (e.g., ±10 or 50% around mean value) rather than basing the parameter uncertainty on experimental data. In our recent work we overcame the first limitation by performing UQ and sensitivity analysis using a novel canine action potential model, allowing all parameters to be uncertain, but with arbitrary variation. Here, we address the second limitation by extending our previous work to use data-driven estimates of parameter uncertainty. Overall, we estimated uncertainty due to population variability in all parameters in five currents active during repolarization: inward potassium rectifier, transient outward potassium, L-type calcium, rapidly and slowly activating delayed potassium rectifier; 25 parameters in total (all model parameters except fast sodium current parameters). A variety of methods was used to estimate the variability in these parameters. We then propagated the uncertainties through the model to determine their impact on predictions of action potential shape, action potential duration (APD) prolongation due to drug block, and spiral wave dynamics. Parameter uncertainty had a significant effect on model predictions, especially L-type calcium current parameters. Correlation between physiological parameters was determined to play a role in physiological realism of action potentials. Surprisingly, even model outputs that were relative differences, specifically drug-induced APD prolongation, were heavily impacted by the underlying uncertainty. This is the first data-driven end-to-end UQ analysis in cardiac EP accounting for uncertainty in the vast majority of parameters, including first in tissue, and demonstrates how future UQ could be used to ensure model-based decisions are robust to all underlying parameter uncertainties.
Published: 2020
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177. Mantle cell lymphoma of palatine tonsil: A case report

Author: Jayalakshmi Pailoor, Venupriya Ramasamy, Yousoof Saira Bahnu, Cheng-Eng Koay, Kian-Meng Chang, and Pathmanathan Rajadurai
Subjects: Cyclin D, Extra nodal lymphoma, Fluorescent-in-situ hybridization, Mantle cell lymphoma, Palatine tonsillar lymphoma, Diseases of the respiratory system, RC705-779
Published: 2020
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178. A Case of Successful Treatment with Unilateral Oophorectomy in a Patient with Resistant Polycystic Ovary Syndrome

Author: S. Pathmanathan, I. Ranathunga, and N. P. Somasundaram
Subjects: Diseases of the endocrine glands. Clinical endocrinology, RC648-665
Abstract: Background. Polycystic ovary syndrome (PCOS) is a common endocrine disorder with heterogeneous etiology. Typical features consist of oligo/anovulation, polycystic ovaries, and features of hyperandrogenism. Pathogenesis is multifactorial, and positive family history may have a predisposition for disease development. The syndrome is associated with multiple metabolic and nonmetabolic entities. As the disease is involved with multiple adverse outcomes, the successful treatment is pivotal. Among the more advanced options, the unilateral oophorectomy is considered as a last resort to alleviate the symptoms. Case Presentation. A 29-year-old female presented to us with oligomenorrhea, severe hirsutism, androgenic pattern hair loss, acne, increased skin pigmentation, and secondary subfertility. On examination, she was obese with a body mass index (BMI) of 29.6 kg/m2. She had evidence of acanthosis nigricans, androgenic pattern balding, acne, dorsal, supraclavicular fat deposition, and moderate-severe hirsutism. Investigations confirmed excess right ovarian testosterone secretion which led to the ultimate management with right oophorectomy with successful alleviation of clinical features. Conclusions. The multifaceted medical treatment comprises the first-line therapy in PCOS. Surgery is considered as a second-line option in resistant PCOS following failure of initial therapeutic options. We report a case of resistant polycystic ovary syndrome with secondary subfertility and moderate-to-severe hirsutism who was successfully treated with unilateral oophorectomy with favorable results.
Published: 2020
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179. QuickCount®: a novel automated software for rapid cell detection and quantification

Author: Kai Hung Tiong, Jit Kang Chang, Dharini Pathmanathan, Muhammad Zaki Hidayatullah Fadlullah, Pei San Yee, Chee Sun Liew, Zainal Ariff Abdul Rahman, Kheng Ling Beh, and Sok Ching Cheong
Subjects: accurate, automation, cell count, cell quantification, scalable, time-efficient, Biology (General), QH301-705.5
Abstract: We describe a novel automated cell detection and counting software, QuickCount® (QC), designed for rapid quantification of cells. The Bland–Altman plot and intraclass correlation coefficient (ICC) analyses demonstrated strong agreement between cell counts from QC to manual counts (mean and SD: -3.3 ± 4.5; ICC = 0.95). QC has higher recall in comparison to ImageJauto, CellProfiler and CellC and the precision of QC, ImageJauto, CellProfiler and CellC are high and comparable. QC can precisely delineate and count single cells from images of different cell densities with precision and recall above 0.9. QC is unique as it is equipped with real-time preview while optimizing the parameters for accurate cell count and needs minimum hands-on time where hundreds of images can be analyzed automatically in a matter of milliseconds. In conclusion, QC offers a rapid, accurate and versatile solution for large-scale cell quantification and addresses the challenges often faced in cell biology research.
Published: 2018
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180. Prevalence of colonic polyps among patients with Acromegaly

Author: S. Pathmanathan, Noel P. Somasundaram, and N. M. M. Nawarathne
Subjects: acromegaly, colonic polyps, screening colonoscopy, sri lanka, Diseases of the endocrine glands. Clinical endocrinology, RC648-665
Abstract: Background: Patients with acromegaly are reported to have a higher prevalence of colorectal adenoma, which is a pre-malignant condition. It has been recommended that patients with acromegaly should undergo colonoscopic surveillance to detect these lesions early. Aims and Objectives: Our objective was to evaluate the prevalence of colonic polyps in patients with acromegaly. Materials: This was a descriptive cross-sectional study conducted in the Endocrinology and Pituitary clinics at National Hospital of Sri Lanka between January 2012 and June 2013. From a total of 72 patients with acromegaly, 33 patients (13 males and 20 females), who underwent colonoscopy were enrolled for analysis. Age group of this sample was 29 to 71 years. None of these patients had previous or family history of colonic neoplasm or colonic surgery. Results: The mean age of the patients at diagnosis was 40.12 (SD±11.9) years and average lag time between symptomatology and diagnosis of acromegaly in these patients were 3.5 (SD ± 2.8) years. The basal mean plasma Growth hormone was 57.91 mU/L (SD±53.74) and on imaging 28 (84.84%) had tumour of grade II or above. Colonoscopic examination was complete up to the cecum in 23 patients (69.69%), to the splenic flexure in eight patients (24.24%) and to hepatic flexure in the remaining two patients (6.06%). Five patients (15%) with acromegaly had polyps in their colonoscopy in which two of them had tubular adenoma with low grade dysplasia. The group of acromegalic patients with and without polyps did not differ significantly in age 39.8±13.3 years vs 39.71 ±11.34 years, (p= 0.326), in duration of disease 2.8 ±2.0 years vs 3.6±3.0 years, (p=0.544) or in circulating basal GH levels (77.13 ±64.91mU/L vs 42.61±42.70 mU/L, p= 0.134). Diabetes did not influence the prevalence of colonic polyps in acromegaly (Chi-Squared test; p=0.478). Having polyps was statistically significantly higher among male sex than female sex (p Conclusion: Despite lower prevalence of colonic polyps (15%) compared to studies from the west, we still found premalignant lesions in 2 patients (6.06%). Therefore, we suggest that acromegalic patients should undergo screening colonoscopy.
Published: 2018
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181. Clinico-pathological features of oropharyngeal squamous cell carcinomas in Malaysia with reference to HPV infection

Author: Lee Fah Yap, Sook Ling Lai, Anthony Rhodes, Hans Prakash Sathasivam, Maizaton Atmadini Abdullah, Kin-Choo Pua, Pathmanathan Rajadurai, Phaik-Leng Cheah, Selvam Thavaraj, Max Robinson, and Ian C. Paterson
Subjects: Oropharyngeal, Squamous cell carcinoma, p16, Human papillomavirus, Malaysia, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282, Infectious and parasitic diseases, RC109-216
Abstract: Abstract Background The incidence of oropharyngeal squamous cell carcinoma (OPSCC) has been rising in Western countries and this has been attributed to human papillomavirus (HPV) infection. p16 expression is a marker for HPV infection and p16 positive OPSCC is now recognized as a separate disease entity. There are only limited data available regarding HPV-related OPSCC in Asian countries and no data from Malaysia. Methods We identified 60 Malaysian patients with OPSCC over a 12-year period (2004–2015) from four different hospitals in two major cities, Kuala Lumpur and Penang. The detection of HPV was carried out using p16 immunohistochemistry and high risk HPV DNA in situ hybridisation. Results Overall, 15 (25%) tumours were p16 positive by immunohistochemistry, 10 of which were also positive for high risk HPV DNA by in situ hybridisation. By comparison, a matched cohort of UK patients had a p16 positive rate of 49%. However, between 2009 and 2015, where cases were available from all four hospitals, 13 of 37 (35%) cases were p16 positive. In our Malaysian cohort, 53% of patients were of Chinese ethnicity and 80% of the p16 positive cases were found in these patients; no Indian patients had p16 positive disease, despite representing 35% of the total cohort. Conclusion The proportion of OPSCCs associated with HPV in Malaysia appears to be lower than in European and American cohorts and could possibly be more prevalent amongst Malaysians of Chinese ethnicity. Further, our data suggests that the burden of HPV-related OPSCC could be increasing in Malaysia. Larger cross-sectional studies of Malaysian patients are required to determine the public health implications of these preliminary findings.
Published: 2018
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182. Non-typeable Haemophilus Influenzae detection in the lower airways of patients with lung cancer and chronic obstructive pulmonary disease

Author: Krishna B. Sriram, Amanda J. Cox, Pathmanathan Sivakumaran, Maninder Singh, Annabelle M. Watts, Nicholas P. West, and Allan W. Cripps
Subjects: Non-typeable Haemophilus Influenzae, Lung Cancer, COPD, Diseases of the respiratory system, RC705-779
Abstract: Abstract Background Chronic airway inflammation and hypersensitivity to bacterial infection may contribute to lung cancer pathogenesis. Previous studies have demonstrated that nontypeable Haemophilus influenzae (NTHi) is the most common colonizing bacteria in the lower airways of patients with COPD. The objective of this study was to determine the presence of NTHi and immunoglobulin concentrations in patients with lung cancer, COPD and controls. Methods Serum and bronchial wash samples were collected from patients undergoing diagnostic bronchoscopy. Total IgE, IgG and specific NTHi IgG were measured by enzyme linked immunosorbent assay. Bronchial wash samples were examined for the presence of NTHi via PCR. Results Out of the 60 patients: 20 had confirmed Lung Cancer, 27 had COPD only and 13 were used as Controls. NTHi was detected in the lower airways of all three groups (Lung Cancer 20%; COPD 22% and Controls 15%). Total IgE was highest in Lung Cancer subjects followed by COPD and control subjects (mean ± SD: 870 ± 944, 381 ± 442, 159 ± 115). Likewise total IgG was higher in Lung cancer (Mean ± SD: 6.99 ± 1.8) patients compared to COPD (Mean ± SD: 5.43 ± 2). Conclusions The lack of difference in NTHi and specific antibodies between the three groups makes it less likely that NTHi has an important pathogenetic role in subjects with Lung Cancer. However the detection of higher IgE antibody in Lung Cancer subjects identifies a possible mechanism for carcinogenesis in these subjects and warrants further study.
Published: 2018
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183. Formation of chimeric genes with essential functions at the origin of eukaryotes

Author: Raphaël Méheust, Debashish Bhattacharya, Jananan S. Pathmanathan, James O. McInerney, Philippe Lopez, and Eric Bapteste
Subjects: Eukaryogenesis, Evolutionary transition, Chimeric genes, Evolutionary genomics, Endosymbiosis, Biology (General), QH301-705.5
Abstract: Abstract Background Eukaryotes evolved from the symbiotic association of at least two prokaryotic partners, and a good deal is known about the timings, mechanisms, and dynamics of these evolutionary steps. Recently, it was shown that a new class of nuclear genes, symbiogenetic genes (S-genes), was formed concomitant with endosymbiosis and the subsequent evolution of eukaryotic photosynthetic lineages. Understanding their origins and contributions to eukaryogenesis would provide insights into the ways in which cellular complexity has evolved. Results Here, we show that chimeric nuclear genes (S-genes), built from prokaryotic domains, are critical for explaining the leap forward in cellular complexity achieved during eukaryogenesis. A total of 282 S-gene families contributed solutions to many of the challenges faced by early eukaryotes, including enhancing the informational machinery, processing spliceosomal introns, tackling genotoxicity within the cell, and ensuring functional protein interactions in a larger, more compartmentalized cell. For hundreds of S-genes, we confirmed the origins of their components (bacterial, archaeal, or generally prokaryotic) by maximum likelihood phylogenies. Remarkably, Bacteria contributed nine-fold more S-genes than Archaea, including a two-fold greater contribution to informational functions. Therefore, there is an additional, large bacterial contribution to the evolution of eukaryotes, implying that fundamental eukaryotic properties do not strictly follow the traditional informational/operational divide for archaeal/bacterial contributions to eukaryogenesis. Conclusion This study demonstrates the extent and process through which prokaryotic fragments from bacterial and archaeal genes inherited during eukaryogenesis underly the creation of novel chimeric genes with important functions.
Published: 2018
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184. Adding 6 months of androgen deprivation therapy to postoperative radiotherapy for prostate cancer: a comparison of short-course versus no androgen deprivation therapy in the RADICALS-HD randomised controlled trial

Author: Parker, Chris C, Clarke, Noel W, Cook, Adrian D, Kynaston, Howard, Catton, Charles N, Cross, William R, Petersen, Peter M, Persad, Rajendra A, Saad, Fred, Bower, Lorna C, Logue, John, Payne, Heather, Forcat, Silvia, Goldstein, Cindy, Murphy, Claire, Anderson, Juliette, Barkati, Maroie, Bottomley, David M, Branagan, Jennifer, Choudhury, Ananya, Chung, Peter W M, Cogley, Lyn, Goh, Chee L, Hoskin, Peter, Khoo, Vincent, Malone, Shawn C, Masters, Lindsey, Morris, Stephen L, Nabid, Abdenour, Ong, Aldrich D, Raman, Rakesh, Tarver, Kathryn L, Tree, Alison C, Worlding, Jane, Wylie, James P, Zarkar, Anjali M, Parulekar, Wendy R, Parmar, Mahesh K B, Sydes, Matthew R, Amos, Claire, Beaney, Katherine, Bellenger, Katharine, Brawley, Chris, Chung, Christina, Cook, Adrian, Embleton, Andrew, Forcat, Silvia, Goldstein, Cindy, Grabey, Jenna, Hague, Dominic, Herasimtschuk, Anna, Islam, Zaheer, Jovic, Gordana, Maniatis, Christos, Masters, Lindsey, Mohamed, Fatima, Morgan, Rachel, Murphy, Claire, Noor, Dipa, Parmar, Max, Patterson, Paul, Pickering, Holly, Pugh, Cheryl, Rauchenberger, Mary, Ribeiro, Helena, Roach, Carol, Seray-Wurie, Fatimah, Shaji, Preetha, Sims, Hannah, Spittle, Ben, Sydes, Matthew, Tappenden, Nancy, Tsang, Lilian, Uscinska, Barbara, Brown, Teddy, Casey, Sue, Davidson, Cathy, Dellar, Conor, Fletcher, Mandy, Meyer, Ralph, Murphy, Karen, Parulekar, Wendy, Richardson, Harriet, Richardson, Karen, Whelan, Kate, Bissett, Donald, Bonner-Shand, Shelagh, Grant, Judith, Lynch, Rory, MacDonald, Graham, McWilliam, Marie, Moir, Rachel, Rodwell, Sue, Shearer, Kirsty, Smith, Margaret, Binns, Louise, Briggs, Maxine, Brown, Simon, Casanova, Nathalie, Clark, Katy, Hartley, Jasmine, Henry, Ann, Henson, Helen, Hill, Pipa, Jeyasanger, Ganesan, McAlister, Judy, Parkinson, Sharron, Shaw, Alison, Alcock, Christopher, Brady, Joanne, Camilleri, Philip, Carpenter, Janice, Hogbin, Emma, Hyde, Katherine, Kavanagh, Roisin, Ngumo, Alice, Padilla-Harris, Cheryl, Panakis, Niki, Pwint, Thinn, Sabharwal, Ami, Stammers, Tracey, Stone, Helena, Varley, Gail, Weaver, Andrew, Wynn, Hazel, Allan, Elaine, Ansari, Jawaher, Cannon, Philip, Carruthers, Ross, Coubrough, Claudia, Cowan, Chloe, Glen, Hilary, Graham, Janet, Hardie, Maryon, Love, Clare, Macleod, Nicholas, Mahmood, Rana, McClements, Jane, McGlynn, Brian, McKernan, Margaret, Mcleod, Nick, Othman, Aqilah, Ross, Kristy, Roxburgh, Patricia, Templeton, Maureen, Tufail, Aisha, Turley, Claudia, Wilson, Mark, Ali, Zulfiqer, Andrews, Kelly, Burns, Rebecca, Butterworth, Caryl, Coventry, Thomas, Evans, Angela, Jones, Gareth, Lewis, Sion, Oommen, Nikhil, Owen, Susan, Saxton, Wendy, Tapping, Sister Hayley, Thomas, Dianne, Thomas, Rachel, Walker, Beth, Williams, Rachel, Collard, Susan, Ford, Victoria, Goodhind, Frances, Holbrook, Becky, Kershaw, Elizabeth, Ley, Samantha, Martinez, Maria, McCormack, Anne, Sheehan, Denise, Toy, Elizabeth, Van-Koutrik, Lynne, Varughese, Mohini, Aitken, Katherine, Barlow, David, Beattie, Louise, Bownie-Mukumbu, Godfrey, Bryan, Rachel, Corner, Victoria, Edwards, Abigail, Fazekasne Fulep, Adrienn, Guerrero-Urbano, Teresa, Gwilt, Julie, Happle, Liz, Jackman, Dee, Krysta, Beata, Paisey, Sangeeta, Shaffer, Richard, Smith, Jackie, Westley, Rosalyne, Yusof, Hilawati, Appleby, Rowan, Avis, Joanne, Beresford, Mark, Brydon-Hill, Ruth, Cox, Christine, Curtis, Samantha, Elwell, Christine, Frim, Olivera, Hamilton, Beatrice, Hudson, Zoe, Jenner, Abigail, Mantle, Mark, Mason, Jessica, Masson, Susan, McFarlane, Jonathan, Newman, Hugh, Pocock, Abigail, Robertson, Bryony, Tomlinson, Eve, Wassall, Rebecca, Williams (Née Allen), Sister Tania, Brown, Grace, Clarke, Peter, Cole, Aiden, Cousins, Jane, Gilchrist, Gail, Hagan, Chris, Hanna, Emma, Harney, Jackie, Hetherington, Stacey, Houghton, Fionnuala, Hynds, Sharon, Jain, Suneil, Johnston, Ruth, Keane, Patrick, Lyons, Ciara, McAleese, Jonathan, McAllister, Jo, McKenna, Karen, Mitchell, Darren, Murray, Stacey, O'Sullivan, Joe, Rajan, Nambi, Shum, Poh Lin, Stewart, David, Stranex, Stephen, Totten, Grace, Chandrashekara, Madhura, Drew, Ellen, Drew, Mary (Ellen), Fletcher, Julie, Gamble, Tina, Lokes, Rachel, Schumacher, Ann, Shetty, Kavitha, Sohanpal, Sundip, Whitehouse, James, Zarkar, Anjali, Ford, Daniel, Glaholm, John, Lenton, Daniel, O'Beney, Rachael, Sohanpal, Sundip, Taylor, Helen, Thomas, Helen, Whitehouse, James, Chetiyawardana, Samantha, Doherty, A, El-Modir, Ahmed, Ford, Daniel, Glaholm, John, Gunn, Melanie, James, Nicholas, Kedge, Liz, Lokes, Rachel, Mannan, Aliyah, Marange, Josephine, Morrison, Andrew, Okubanjo, Maryanne, Olaniyi-Oduntan, Darlletta, Peterkin, Zhane, Preece, George, Qureshi, Zayn, Ross, Liliam, Samra, Tarandip, Southgate, Elizabeth, Stephens, Rebekah, Stevenson, Robert, Watson-Jones, Phillip, Zarkar, Anjali, Abdulwahid, Danya, Ainsworth, Sarah, Ashworth, Sue, Beard, Karen, Charnley, Natalie, Desai, Parth, Fielding, Bethany, Flaherty, Jan, Forrest, Diane, Frankland, Helen, Haidar, Imran, Hargreaves, Jeanette, Henry, Joanne, Hugill, Angela, Jewers, Karen, Keith, Sarah, Kilroy, Stephen, King, Jennifer, Parikh, Omi, Riley, Karen, Ryan-Smith, Janet, Thomas, Jacqueline, Thompson, Catherine, Tickle, Vivienne, Waring, Lynsey, Wise, Marcus, Abdulwahid, Danya, Ainsworth, Sarah, Ashworth, Sue, Beard, Karen, Dawson, Louise, Desai, Parth, Ditchfield, Julie, Fielding, Bethany, Flaherty, Jan, Forrest, Diane, Frankland, Helen, Haidar, Imran, Hargreaves, Jeanette, Henry, Joanne, Hugill, Angela, Jewers, Karen, Keith, Sarah, Kilroy, Stephen, King, Jennifer, Parikh, Omi, Riley, Karen, Ryan-Smith, Janet, Thompson, Catherine, Tickle, Vivienne, Waring, Lynsey, Wise, Marcus, Al-Samarraie, Amir, Ali, Zulfiqer, Bolger, Annette, Davies, Llinos, Flavin, Aileen, Heron, Jane, Hughes, Faye, Jackson, Kim, Lewis, Joanne, Manley, Rachel, Oommen, Nikhil, Rutter, Charley-Anne, Smith, Nick, Srinivasan, Vaiku, Stockport, Jane, Tapping, Sister Hayley, Thomas, Heather, Toussi, H, Wood, Cathryn, Archer, Simon, Fletcher, Joanne, Ginnelly, Helen, Kirkby, Amy, Knight (n. Sherburn), Victoria, Lawrence nee Palmer, Tara, Mashegede, Beverley, Metcalf, Karen, Panades, Miguel, Sreenivasan, Thiagarajan, Szymiczek, Kinga, Thomas, Isobel, Young, Anita, Broadley, Eve, Brock, Sue, Davies, Joseph, Jones, Stephanie, McCully, Ruby, Naraine, Nicky, Preece, Kate, Purandare, Laura, Rabbi, Taslima, Savage, Sarah, Vamplew, Luke, Bamford, Linda, Benton, Richard, Cotterill, Lauren, Henry, Ann, Inman, Hayley, Karsera, Louise, Kay, Anne Marie, Kenyon, Lucille, Lazarte, Jannika, McNulty, Dawn, Moore, Eleanor, Nallathambi, Chandran, Naylor, Deirdre, Owen, Lisa, Parikh, Shefali, Pendrill, Amy, Price, Joanne, Robertshaw, Helen, Rodda, Sree, Sewell, Jane, Teo, Mark, Waldron, Eleanor, Bloomfield, David, Blythe, Tracy, Brown, Joanne, Carr, Philippa, Chambers, Carla, Dlodlo, Jessica, Frattaroli, Paul, Gilbert, Duncan, Hart, Andrew, Hodges, Samantha, Hooper, Simon, Hunter, Catherine, Lactao, Ranee, Leitao, Madalena, McKinna, Fiona, Nikapota, Ashok, Peterson, Jane, Robinson, Angus, Tremlett, Jean, Webb, Andrew, Wilkins, Marie, Young, Bobbie, Bahl, Amit, Baxter, Matt, Beresford, Mark, Bowers, Dawn, Challipalli, Amarnath, Coe, Marc, Cowley, Sue, Gillatt, David, Griffiths, Dorothy, Henson (nee Savidge), Verity, Hilman, Serena, Hollister, Robert, Kisanga, Mary, Lee, Vivienne, Lowe, Amelia, Macefield, Rhiannon, Masson, Susan, Pegler, Ruth, Robertson, Peter, Saldanha, Helen, Simmonds, Mary, Sims, Laura, Wheeler, Lucie, Wilson, Paula, Woods, Martin, Yarrow, Sue, Zelley, Sarah, Abd Alazeez, Mohamed, Alderton, Kate, Bahl, Amit, Beresford, Mark, Brain, Carol, Challipalli, Amarnath, Chilcott, Helen, Clarke, Samantha, Cobos-Arrivabene, Marta, Corderoy, Helen, Cousins, Rebecca, Garner, Victoria, Gillatt, David, Herbert, Chris, Johnson, Lyndsey, Jones, Kathryn, Kemp, Gail, Kirkby, Sarah, Kirkpatrick, Suriya, Koupparis, Anthony, Manning, Courtney, Oxley, Jon, Perry, Emily, Persad, Raj, Phipps, Charlotte, Richmond-Russell, Kerry, Rowe, Edward, Shelton, Luke, Shiridzinomwa, Constance, Smith, Carolyn, Treasure, Ann, Wilson, Paula, Wyatt, Clare, Beesley, Katherine, Benson, Richard, Couch, Ellie, Foxwell, Sue, Goss, Vanessa, Harnwell, Juliette, Hughes-Davies, Luke, Joslin, Vicky, Laubscher, Angelique, Mazhar, Danish, Patterson, Helen, Raj, Sanjay, Ramanujachar, Ramya, Rimmer, Yvonne, Robinson, Nicola, Russell, Simon, Stone, Matthew, Thomas, Robert, Treeby, Jo, Vickery, Kathryn, Woodward, Cathryn, Ahmed, Ilyas, Appleby, Bonny, Brulinski, Patryk, Buckley, Julie, Catt, Natalie, Coppins, Helen, Crawford, Sister Denise, Davies, Julie-Ann, Drakeley, Susan, Edwards, Albert, Evans, Clary, Galante, Joao, Gladwell, Louise, Gough, Jessica, Hargreaves, Carolyn, Hegarty, Gemma, Kehoe, Laura, Larkins, Rachel, Lipsham, Margaret, Little, Jessica, Malde, Rohit, Middleton, Sharon, Mikropoulos, Christos, Mirza, Arafat, Mithal, Natasha, Nathan, Kannon, Raman, Rakesh, Rendall, Alice, Robinson, Karen, Ryan, Rachel, Sim, Van, Swann, Michelle, Tasker, Lorraine, Taylor, Katy, Thomas, Carys, Trigonis, Ioannis, Walker, Karen, Williams, Joanne, Williamson, Elizabeth, Wood, Marian, Wood, Pauline, Zurakovsky, Hilary, Bois (nee Harris), Elizabeth, Chan, Hin Fan, Chikopela, Loveness, Clark, Helen, Clements, Colette, Holliday, Samantha, Hughes, Owen, Jones, Clare, Kynaston, Howard, Mason, Malcolm, Narahari, Krishna, Pearse, Kevin, Staffurth, John, Austin, Mandy, Barnett, Christian, Camburn, Sister Tracey, Dawson, Elizabeth, Gibson, Sian, Hamid, Abdel, Kancherla, Kiran, Leone, Priscilla, Perkins, Lauren, Ramsay, Valerie, Singizi, Bryan, Allen, Julie, Anderson, Susan, Andrews (nee Moore), Eleanor, Bakawala, Rehana, Beazer, Sarah, Binks, Colin, Bowen, Jo, Broomfield, Barbara, Cook, Audrey, Crossley, Lin, Ford, Chris, Forkes, Janet, Hall, Julia, Healey-Mariano, Jennifer, Jenkins, Peter, Johnson, Nigel, Jonnada, Sai, Owen, Roger, Pratten, Elaine, Sayers, Rachel, Shimell, Chris, Skelton, Amy, Tan, Matthew, Trigg-Hogarth, Kate, Wronski, Sue, Aldous, Mary, Archer, Denise, Eccleson, Helen, Gallimore, Elizabeth, Green, Sue, Ibrahim, Azman, Jones, Carys, Prince, Jude, Chrystal, Melanie, Din, Omar, Firth, Alexandra, Ford, Nicky, Kirkbride, Peter, Martindale, John, Redfearn, Alison, Smedley (nee McCabe), Janine, Smith, Lucy, Sorice, Vittoria, Stevenson, Lesley, Toms, Julie, Wood, Sister Kim, Barke, Nyssa, Basu, Devy, Dewar, Lorna, Driscoll, Celine, Hunting, Liz, Kumar, Muthar, Leone, Priscilla, Mabelin, Louies, Sizer, Bruce, Thorogood, Lucy, Askew, Sarah, Beech, Darren, Broom, Jane, Duley, Emma, Firth, Elizabeth, Gould, Aaron, Hocking, Christopher, Johns, Louise, Kent, Emma, Madine, John, Partridge, Kerena, Penhaligon, Corrine, Pentecost, Catherine, Pollard, Kay, Poultney, Melissa, Pynsent, William, Skewes, Johanna, Steele, Anita, Thomson, Alastair H, Topps, Alice, Townley, Luke, Wheatley, Duncan, Brown, Maggie, Chan, Andrew, Connor, Marie, Elwell, Sue, Geraghty, Kerry, Griffiths, Theresa, Hayward, Lesley, Humber, Caroline, Khan, Yakhub, Lake, Sister Judith, Laverick, Rosaleen, Mansell, Gemma, Mislang, Albert, Ngwenya, Su, Panesar, Pritpal, Sanders, Kay, Simmons, Elaine, Sinfield, Melanie, Stanley, Laura, Steventon, Emily, Stockdale, Andrew, Sunny, Manju, Thind, Manreet, Tranter, Fiona, Tsalic, Medy, Worlding, Jane, Adamson, Vanessa, Bennion, Carole, Butler-Barnes, Jenny, Hough, Adele, Hough, Chris, Hughes, Joanne, Irwin, P, Javle, P, Logue, John, Mansfield, Carolyn, Smith, Rachel, Sumner, Lesley, Tomlinson, Annabel, Tran, Anna, Walker, Caroline, Wylie, James, Chakraborti, Prabir, Das, Prantik, Ennis, Dawn, Marriott, Sue, Muthukumar, Dakshinamoorthy, Pattu, Pugazhenthi, Ward, Colin, Alzouebi, Mymoona, Burlace, Barbara, Ferguson, Catherine, Firth, Alexandra, Hooton, Georgia, Jeffcutt, Nicole, Martindale, John, Neal, Amy, Pezaro, Carmel, Sivoglo, Virgil, Smedley (nee McCabe), Janine, Smith, Lucy, Taylor, Jennifer, Wilkinson, Nicola, Wood, Sister Kim, Afzal, Naveed, Anderson, Beverley, Andrews, Stephen, Bakker, Piet, Breakspear, Sally, Cornaby, Andrew, Crellin, Perric, Gibbins, Andrew, Gibbins, Jackie, Glen, Tracy, Goodsell, Josie, Horton, Sarah, Jones, Stephanie, Love, Sally, Masters, Benjamin, Rees, Andrew, Sharpe, Simon, Taylor, Kate, Wignall, Suzy, Harrow, Sister Kath, Jones, Lesley, Kanyi, Karen, Keng-Koh, Pek, McGarry, Karen, Ramachandra, Prakash, Smith, Lucy, Watts, Angela, Baumber, Shelley, Gilbert, Duncan, Howard, Joanna, Jones-Skipper, Kay, Manetta, Caroline, McCrisken, Lauren, McKinna, Fiona, Taylor, Jo-Anne, Williams, Amanda, Clark, Alison, Clarke, Maria, Egan, Lisa, Fiddes, Kathleen, Forman, Susan, Gardiner, Fiona, Jeffrey, David, Mariappan, Param, Mayne, Barbara, McLaren, Duncan, Rogers, Brian, Woods, Catherine, Young, Douglas, Anderson, John, Baines, Kizzy, Crundwell, M, Davey, Elizabeth, Ford, Victoria, Lawless, Theresa, Matkins, Ellen, McGinley, Shannon, McGrath, John, Piper, Jane, Roantree, Alison, Seath, Ingrid, Sheehan, Denise, Srinivasan, Rajaguru, Stott, Mark, Timmings, Katie, Webb, Claire, Al-hasso, Abdulla, Allan, Elaine, Barwell, Lorraine, Cowan, Chloe, Dodds, David, Finnegan, Niall, Griffen, Ailsa, Jones, Rob, Lawless, Claire, MacMillan, Antonia, Mahmood, Rana, Russell, Martin, Sadozye, Azmat, Shirley, Sian, Sidek, Norma, Wallace, Jan, Akhtar, Naeem, Al-hasso, Abdulla, Ansari, Jawaher, Bell, Karen, Humpreys, Louise, McWilliam, Donna, Moody, Emma, O'Neil, Rebecca, Peck, Suzannah, Turley, Claudia, Auladin, Raidah, Bird, Stephanie, Bissa, Caterina, Dabbs, Marianne, Di Maria, Fabio, Goh, Chee, Guerrero-Urbano, Teresa, Harding, Joshua, Jamieson, Ceri, Jones, Jules, Khaksar, Sara, Laing, Robert, Matthews, Laura, Medisetti, Aruna, Molony-Oates, Barbara, Money-Kyrle, Julian, Nobes, Jenny, Rabsztyn, Weronika, Shaffer, Richard, Sidi, Frances, Smale, Daisy May, Stone, Sarah, Tindall, Annie, Woods, Jane, Wu, Min, Daniel, Amelia, Holmes, Evelyn, Kellaway, Joanne, Lewis, Amanda, Light, Teresa, Melcher, Lucinda, Robinson, Lily, Staines, Nikki, Anderson, Susan, Carini, Serrafina, Cook, Audrey, Cooper, Sophie, Evans, Melanie, Follows, Josh, Forkes, Janet, Grant, Warren, Hedges, Andy, Hughes, Claire, Jones (Birch), Janine, King, Lisa, Lees, Laura, Mercer, Lily, Myles, Sophie, Roberts, Zara, Williamson, Nicola, Abbassi, Sara, Alonzi, Roberto, Anyamene, Nicola, Bici, Julia, Collins, Lucy, Dickson, Jeanette, Garrido-Perez, Sandra, Hoskin, Peter, Hughes, Robert, Jenkins, Suzanne, Kowalewska, Paulina, Milner, Jessica, Mitchell, Lesley, Ostler, Peter, Rashid, Aamna, Stapleton, Stephanie, Bashir, Faheem, Bone, Linzi, Clark, Diane, Dixit, Sanjay, Horne, Rhian, Jones, Dawn, Palmer, Sarah, Plowman, Kristian, Rawlings, Julie, Simms, Matthew, Addison, Anglise, Arnott, Seonaid, Borgaonkar, Sudhir, Brown, Sandra, Campbell, Audrey, Colligan, Steve, Cormack, Margaret, Doughty, Victoria, Kelly, Kay, Lister, Debbie, Macdonald, Alison, Macgregor, Carol, Mackay, Rachel, Madeleine, Jude, McNally, Morag, McPhail, Neil, Neville, Sean, Nicholls, Alison, Papakostidi, Aristoula, Simpson, Georgina, Sinclair, Glenda, Skene, Anna, Telfer, Sheena, Urquhart, Zoe, Whillis, David, Etheridge, Charlotte, Ford, Amanda, Ridley, Paul, Scrase, Christopher, Upson, Susan, Venkitaraman, Ramachandran, Capaldi, Lisa, Churn, Mark, Higgins, Linda, Knott, Helen, Stringer (pr. Davis), Sally, Taylor, Jacob, Tranter, Helen, Wollaston, Julie, Brogan (nee Roddie), Stephanie, Erskine, Susan, Hamill, Maureen, Prentice, Lynn, Russell, Martin, Sidek, Norma, Symon, Lesley, Teahan, Seamus, Todd, Anne, Tweedle, James, Young, Sally, Austin (nee Glover), Gemma, Boon, Ian, Bottomley, David, Breckin, Edmund, Clarke, Jude, Coyle, Catherine, Davies, Emily, Goulding, James, Henry, Ann, Kiltie, Anne, Loughrey, Carmel, Moore, Eleanor, Murad, Fatima, Pratt, Claire, Shuttleworth, Pam, Smith, Beccy, Smith, Gill, Branson (née Dineen), Amy, Christmas, Sallyanne, Cooke, Jill, Griffiths, Leyshon, Kent, Christopher, King, Amy, Kockelbergh, Roger, Potterton, Janet, Speed, Lesley, Vasanthan, Subramanian, Walker, Julia, Archer, Simon, Baria, Karin, Bateman, Samantha, Coombs, Sarah, Cunningham, Amy, Dahar, Nazeer, Daruwala, Pallon, Francis, Olesya, Ginnelly, Helen, Hall, Jane, Hoare, Kathryn, Key, Claire, Lockwood, Carol, Mark, Ian, Newton, Rachel, Okubanjo, Maryanne, Panades, Miguel, Sloan, Andrew, Sreenivasan, Thiagarajan, Bermingham, Nicola, Dean, Lynsey, Malik, Zafar, Pilkington, Pauline, Robson, Peter, Rogers, Heather, Rogers, Thomas, Treherne, Katy, Bourke, Amie, Chikkamuniyappa, Bindu, Dalton-Short, Ross, Edwards, Steve, Falconer, Alison, Ford, Amy, Harinarayanan, Sathish, Hassan, Nebah, Hornzee, Gillian, Magwaro, Sophia, McInerney, Sue, Pratap, Bhavesh, Rezkallah, Sarah, Sadare (Olaleye), Ibiyemi, Stancliffe, May, Stewart, Simon, Thoi, Anne-Maree, Westrop, Anna, Ahamadali, Sheefa, Arsanious, Nasr, Balogun, Shaki, Batish, Cheryl, Campbell, Yvonne, Crowley, Claire, Dunne, Emma, Ferreira, Miguel, Haldeos, Anne, Hijab, Adham, Huddart, Robert, John, Babbin, Michaelidou, Andriana, Nawrozzadeh, Mohammed, Pach, Jackie, Patel, Priyanka, Payne, Ann, Perry, Matthew, Sadare (Olaleye), Ibiyemi, Serra, Maria, Springall, Christine, Thompson, Jane, Thomson, Sister Jane, Tree, Alison, Aya, Viviana, Beaney, Ronald, Bower, Lorna, Conway, Lisa-Jane, Donaghey, Sally, Gordon, Hussain, Grau, Isabel, Guerrero-Urbano, Teresa, Harris, Sarah, Hogan, Vesna, Hore, Brendan, Hughes, Simon, Jones, Katie, Kuenzig, Greg, Morris, Stephen, Nathan, Kannon, Nunney, Dawn, Rashid, Jessica, Reynolds, Philip, Rooprai, Bali, Chikkamuniyappa, Bindu, Dalton-Short, Ross, Edwards, Steve, Hassan, Nebah, Magwaro, Sophia, Mangar, Stephen, Rezkallah, Sarah, Stewart, Simon, Thoi, Anne-Maree, Westrop, Anna, Azirar, Hanna, Caumont, Tom, Gupta, Nishi, Hill, Judy, Karp, Stephen, Kullane, Sagal, Macavoy, Tina, Melcher, Lucinda, Neave, Farhad, Newby, Jackie, Van Someren, Chloe, Anderson, Naomi, Annan, Lynda, Cooke, Juniebel, Douch, Emma, Fawcitt, Sara, Gore, Alexandra, Grieve, Sylvia, Jarvis, Claire, McCadden, Angela, Needleman, Sarah, Pigott, Katherine, Powell, Hannah, Ramtohul, Kaliyanee, Rosenfelder, Nicola, Vilarino-Varela, Maria, Ali, Jennyfa, Ali, Maryam, Banda, Laillah-Crystal, Bott, Trevor, Brown, Emmanuel, Bryant, Hanna, Carlino, Giulia, Grant, Cordelia, Hogan, Holly, Khoo, Vincent, Mojidra, Chintan, Montebello, Joseph, Morrison, Jennifer, Olabanji, Matthew, Pillai, Sijy, Siu, Bernard, Stafferton, Ruth, Storrs, Sarah, Townsend-Thorn, Debra, Tree, Alison, Van As, Nicholas, Vijayakumar, Vijitha, Welsh, Liam, Dearnaley, David, Gao, Annie, Horwich, Alan, Huddart, Robert, Lewington, Val, Marshall, John, Murphy, Louise, Parker, Chris, Venkitaraman, Ramachandran, Beaton, Kirsty, Bech-Nielsen, Nanette, Bolton, Olivia, Chetiyawardana, Samantha, Conibear, John, Dekaj, Fatjon, Hillman, Paul, Jayachandran, Resmi, Kaliski, Alexandre, Kiff, Janet, Nicholson, Alastair, Nixon, Jude, Oladimeji, Janet, Riches, Oscar, Smith, Dan, Tipples, Karen, Wells, Paula, Anderson, Chris, Daly, Deirdre, Dover, Serena, Gilmartin, Claire, Golden, Sophie, Gregg, Jane, Issa, Rami, Khoo, Vincent, Oni, Titilayo, Patel, Chandni, Perry, Matthew, Quarrell, Mark, Rolfe, Debbie, Tighe, Helen, Toledo, Jesusa, Tuazon, Juel, Varro, Robert, Bohra, Vikram, Caballes, Mark, Custins, Laura, Hornzee, Gillian, Ivie, Stephanie, Joshi, Manisha, Liau, Joy, Magwaro, Sophia, Naim, Farhan, Patel, Anup, Pathmanathan, Byiravey, Russell, Emily, Stewart, Simon, Thoi, Anne-Maree, Tippins, Anna, Westrop, Anna, Agdiran, Didem, Aggarwal, Ajay, Amoaten, Thomas, Aslam, Natasha, Bonsu, Nicole, Butt, Zainab, Danaswamy, Patricia, Davda, Reena, Gillesen, Annelies, Goel, Roshni, Hall, Julia, Khong, Rachel, Lowi, Suzy, Merrick, Richard, Mitra, Anita, Payne, Heather, Teh, Jonathan, Whinn, Samantha, Woolf, David, Zilkha, Helene, Bech-Nielsen, Nanette, Bolton, Olivia, Danaswamy, Patricia, Dawson-Athey, Linda, Foster, Emma, Gillian, Andrew, Green, James, Hillman, Paul, Hines, John, Holden, Simon, Jayachandran, Resmi, Kazingizi, Memory, O'Neill, John, Olaoni, Thompson, Peters, John, Philp, Timothy, Wells, Paula, Woolf, David, Zaidi, Sadaf, Adeyoju, Adebanji, Adinkra, Victoria, Brough, Richard, Brown, Stephen CW, Collins, Gerald, Hardstaff, Lisa, Hill, Pippa, Kujawa, Magda, McCurrie, Marilyn, Townley, Barbara, Woodhouse, Iain, Zafar, Waheed, Beesley, Sharon, Breen, Vivienne, Brulinski, Patryk, Burrage, Su, Calvert, Clare, Davison, Alison, Dias, Heather, Fossey, Gavin, Hotine, Jodie, Jones, Ifan, Jope, Carmel, Lines, Yvonne, Martins, Sarah, Mercier, Barbara, Mikropoulos, Christos, Murray, Jane, Neshiri, Innocent, Pamphlett, Ian, Phillips, Ann, Rai, Pavnish, Rezkallah, Sarah, Roberts, Verity, Taylor, Henry, Taylor, Katy, Thomas, Carys, Tribe, Lisa, Wood, Pauline, Bottomley, Ian, Buchan, Jonathan, Carpio, Viviana, Choudhury, Ananya, Conroy, Ruth, Cowan, Richard, Davison, Sue, Duncan, Ian, Elliott, Tony, Fair, Kim, Fallaize, Adrian, Flanagan, Laura, Green, Sarah, Harris, Catherine, Hart, Amber, Jones, Cathryn, Livsey, Jacqueline, Logue, John, McCaul, Damian, Nazari, Roonak, Newbery, Carol, O'Connor, Kate, O'Dwyer, Sister Jackie, Oliver, Joanne, Onoge, Ekugbe, Petsa, Maria, Redshaw, Catherine, Smith, Sarah-Ellen, Spoor, Willemijn, Thomas, Sister Viv, White, Holly, Worsley, Lucy, Wylie, James, Bailey, Linda, Bennett, Molly, Corless, Rebecca, Duffy, Annie, Fellows, Kathryn, Gipson, Anna, Haydock, Helen, Knox, WF, Liptrott, Sarah, McCarth, Fiona, McCartin, Fiona, Mistry, Heena, Mthethwa, Thobekile, Partington, Lillian, Piper, Lindsay, Ramani, Vijay, Sahin, Sarah, Sangar, Vijay, Slevin, Kathryn, Tuck, Jonathan, Wylie, James, Atkinson, Lorraine, Barnbrooke, Sarah, Barnes, Alison, Brownless, Caroline, Chadwick, David, Hardman, John, Harland, Keith, Long, Carol, McAuliffe, Sarah, McBride, Julia, McClurey, Sister Patricia, Mohan, Rita, Naylor, Lynne, Peedell, Clive, Robson, Paula, Shakespeare, Devadasan, Thompson, Emma, Van der Voet, Hans, Wilson, David, Barron, Chris, Frew, John, Lambert, Katy, McMenemin, Rhona, Pedley, Ian, Riseborough, Danielle, Roberts, Trevor, Stockley, Emma, Stoddart, Jonathan, Tate, Janine, Walker, Jayashree, Workman, Bridget, Barnett, Debra, Carter, Adam, Cox, Adam, Davey, Alison, Davies, Kirstin, Gilby, Lisa, Hodge, Simon, Kearney, S, Lavender, Helene, Marty, Janet, Nash, Maxine, Rahman, Syhed, Richards, Jayne, Simpson, Julie, Wall, Elaine, Webb, Paula, Wild, Karen, Williams, Rachel, Wilson, Jim, Branagan, Jenny, Camilleri, Philip, Dell, Gillian, Elwell, Christine, Foo, Maxine, Goyena, Ruby, Jones, Andrea, McBain, Hazel, McGrath, Katherine, O'Callaghan, Jane, Ramanathan, Dorai, Tee, Elizabeth, Thominson, Bronwen, Tighe (nee Bussey), Rachel, Archer, Denise, Beety, Jane, Birch, Richard, Bloomfield, Mark, Callam, Sara, Cooper, Adele, Darby, Helen, Healey, Gail, Heasley, Karen, Kapur, Guarav, London, Clare, Nobes, Jenny, Oosterom, Katrien, Ostrowski, Joe, Shobrook, Bridget, Turner, Sarah, Wade, Rob, Walker, Suzanne, Walton, Sharon, Websdale, Cheryl, White, Joanna, Alder, Leanne, Chivers, Rachael, Gooch, Carol, Howard, Lucy, Hutchinson, Camille, Jevons, Cody, Kumar, Daniel, Marley, Hazel, Mills, Jamie, Purves, Hanna, Sayers, Ian, Sundar, Santhanam, Szolin-Jones, Jacob, Walker, Georgina, Wood, Catherine, Choudhury, Ananya, Conroy, Ruth, Cook, Wendy, Gill, Lisa, Johnson, Joanne, Johnstone, Dawn, Jones, Richard, Livsey, Jacqueline, Mistry, Leena, Scarratt, Lyndsay, Aiku, Abimbola, Ajzensztejn, Daniel, Al-Chamali, Hiba, Andrade, Gerard, Barber, Dave, Bekulart, Sylwia, Bhatnagar, Gagan, Boutflower, Jane, Brewster, Simon, Butterfield, Anne, Camilleri, Philip, Carroll, Weronika, Cole, David J, Coutts, Tim, Doyle, Kerrie, Durrant, Lisa, Green, Trish, Hyde, Katherine, Jampana, Raj, Jastrzebska, Patrycja, Kiltie, Anne, Kyffin, Phillippa, Langridge, Nicole, Lawrey, Sarah, Markus, Sarah, Marston, Kerrie, Mooney, Matthew, Mukkath, Sandra, Murphy, Ann, Owens, Rob, Panakis, Niki, Rabsztyn, Weronika, Ruane, Sarah, Sabbagh, Ahmad, Sabharwal, Ami, Samkange, Grace, Sankighatta, Naveen, Skwarski, Michael, Stuart, Robert, Wellman, Sandie, Wilson, Jo, Cavanagh, Kerrie, Hollingdale, Abigail, Allen, Susan, Barter, Elisa, Benson, Richard, Gregory, Debbie, Ingle, Charlotte, Lawrence, Steph, Rooney, Paula-Joanne, Bishop, Emma, Brennan, Maria, Cogley, Lyn, Hammonds, John Christopher, McInerney, Paul, McLarty, Esther, Natale, Salvatore, Pascoe, Sarah, Purcell, Keith, Sells, Henry, Tyner, Sharah, yates, Victoria, Beamish, Neal, Blaney, Hilary, Brock, Sue, Clapp, Felicity, Clarke, Elizabeth, Coffin, Teresa, Crellin, Perric, Davies, Joe, Downs, Nichola, Gillespie, Sally, Goodsell, Josie, Harinarayanan, Sathish, Heckford, Louise, Iskender, Amanda, Orford, Sara, Pressdee, Sandy, Rix, Sophie, Wheelwright, Roger, Woodward, Elizabeth, Wright, Seonaid, Blight, Kathy, Dobson, Tracey, Hale, Jennifer, Harris-Burland, Angie, Khoury, Ghassan, Madhava, Kudingila, Meadows, Lorna, Nagar, Yoodhvir, Roca, Mila, Stacey, Wendy, Stephenson, Anna, Uherek, Maja, Virgo, Azarel, Watkinson, Catrin, Birtle, Alison, Chauhan, Zainab, Cornthwaite, Stephanie, Curtis, Sandra, Ellard, Rose, Fish, Nathan, Hefferon, Billy, Parikh, Omi, Searle, Claire, Spickett, Helen, Walmsley, Catherine, Wise, Marcus, Atkinson, Jane, Brown, Richard B, Cartwright, Debbie, Coomber, Kristy, Hunt, Allison, Kowalczyk-Williams, Wioletta, Lewis, Christina, Parr, Stephen, Purdon, Helen, Rogers, Paul, Vowell, Emma, Capaldi, Lisa, Featherstone, Wendy, Hamilton, Joanna, Harrison, Alison, Hindle, Margaret, Hodson, Hayley, Isaew, Andrea, Knapp, Jeanette, Moss, Sarah, Gibbs, Stephanie, Gujral, Sandeep, Jannapureddy, Revanth, Kelkar, Anand, Martinou, Maria, Mills-Baldock, Tina, Nicholson, Alastair, O'Brien, Neale, Subramaniam, Ramachandran, Tahir, Saad, Vandal, Mohammad Tanvir, Vu, Thi, Allen, Rachael, Betts, Chris, Bowmer, Amanda, Clarke, Noel, Cordwell, Amanda, Cowan, Richard, Dharmaprasad, Soney, Duncan (nee Keatley), Claire, Elliott, Tony, Farnworth, Christine, Farrell, Helen, George, Siny, Goulden, Kay, Harter, Leah, Kaushal, Malan, Kirk, Sister Sarah, Lau, Maurice, Lydon, Anne Marie, O'Flynn, Kieran, Platt, Danielle, Redshaw, Catherine, Richardson, Karen, Shackley, David, Stevenson, Garry, Taylor, Sister Melanie, Thompson, Chloe-Anne, Tran, Anna, Vinod, Cellins, Wadsworth, Oliver, Youd, Sister Jill, Attlee, Julie, Bhatnagar, Adityanarayan, Bunn, Sarah, Campbell, Allister, El-Saghir, Mohammed, Fennelly, Ruth, Guy, Peter, Gwilt, Julie, Mattocks, Lehentha, Mcintosh, Gregor, Murphy, Brenda, Reed, Catherine, Strong-Sheldrake, Sophia, Tedd, Gemma, Dent, Kathleen, Dixit, Sanjay, Hood, Marion, Martin, Karen, Pearson, Sandra, Potoczna, Dorota, Spencer, Sue, Towse, Jo, Whotton, Nikita, Bowen, Katie, Clark, Su, Clarke, Rachael, Ferguson, Catherine, Firth, Alexandra, Gibbins, Kate, Kirkbride, Peter, Martin, Jackie, Martindale, John, Smedley (nee McCabe), Janine, Smith, Suzanne, Spalton, Catherine, Tay, Jessica, Wood, Sister Kim, Adams, Marion, Bates, Emma, Chennattukungu, Indukala, Clancy, Joanna, Cross, Vanessa, Hughes, Hayley, Jose, Sanal, Kurian-Downer, Sunita, Michael, Elena, Moore, Sister Helen, Nicholas, Karen, Pope, Suzanne, Potts, Sally, Prashant, Ravi, Singh, Harpreet, Smith, Sandra, Srihari, Narayanan, Weaver, Emma, Abbas, Ali, Barnes, Nicky, Brown, Richard B, Dallas, Nicola, Jackson, Sister Ann, Kader, Nicole, Karim, Omer, Laniado, M, Litchfield, Jayne, Mahmood, Sana, O'Donnell, Helen, Robinson, Victoria, Sears, Alison, Sharif, Shahid, Sierra Pala, Ana, Sinclair, Julie-Ann, Smith, Sister Catherine, Ashcroft, Jan, Boardman, John, Bowes-Cavanagh, Jeanette, Brown, Rebecca, Chedham, Alison, Darch, Flora, Farrar, Michelle, Foot, Jayne, Goldsworthy, Simon, Graham, John, Gray, Emma, Green, Sara, Jelski, Joseph, Kalejaiye, Odunayo, Keni, Manjusha, Locke, Angela, Mahoney, Sue, McMeekin, Faith, Periasamy, Manivannan, Russell, Tamlyn, Ruszala, Elizabeth, Segaran, Surayne, Tighe, Mary, Varughese, Mohini, Wallbutton, Rebecca, Webster, Christine, Whitcher, Alison, Wiggins, Sarah, Wrigley, Martha, Zietz, Maria, Abab, Julie, Cumming, Kirsty, Galloway, Lucy, Ganabady, Malavika, Gwilt, Julie, Haskell, Annelise, Heath, Catherine, James, Naomi, Kennedy, Julie, McFarlane, Victoria, Morales-Azofra, Fabiola, Morton, Susan, Mundy, Carina, Noble, Mark, Paschalis, Alec, Patrick, Julie, Reader, Leanne, Stephenson, Anna, Taylor, Lisa, Wakefield, Shauna, Ahmed, Imtiaz, Bowman, Sue, Chan, Olivia, Cyriac, Abby, Davies, Tracey, Maitland, Katrina, Prejbisz, Jan, Reece, Sheila, Shires, Heather, Tsang, David, Anscombe, Eleanor, Brough, Richard, Brough, WA, Clitheroe, Pat, Cocks, Tracie, Collins, Gerald, Connolly nee McKenna, Sarah, Corcoran, Sam, Coughlan, Tricia, Gilmour, Christina, Goodwin, Emma, Graham, Susan, Hatimy, Umi, Haydock, Helen, Hodgkinson, Sheila, Kilmartin, John, Logue, John, Orrell, Lucy, Rotherham, Carol, Smallwood, Sarah, Taylor, Jill, Wiss, Elliott, Adab, Fawzi, Bhana, Rajanee, Dhinakaran, Kathirvelu, Evans, Marion, Myatt, Alison, Parkinson, Katrina, Peake, Angela, Sellars, Elizabeth, Smith, Rowena, Storer, Julie, Thomasson, Georgia, Vengalil, Salil, Walton, Jenny, Ward, Angela, Boam, Samantha, Burgoyne, Jamie-Rae, Chadwick, Eliot, Gill, Shafiq, Haigh, Steve, Hollis, Keri, Lim, Jun, McCabe, Alastair, Munson, Lyndsey, Nash, Dominic, Saunders, Daniel, Smith, Susan, Sundar, Santhanam, Wade, Lynne, Walker, Georgina, Evans, Elizabeth, Franklin, Alex, Harris (n. Marchant), Emily, Johnstone, Maria, Jones, Lewis, Lemon, Nicola, Phan, Mau-Don, Pudney, Delia, Spence, Rachael, Stretch, Alison, Tait, Ellen, Williams, Bethan, Belcher, Rebecca, Cole, David J, De Silva-Minor, Shiroma, Dodge, Sister Jan, Grayland, Sarah, Jones, Ania, Khan, Omar, Owen, Tim, Palmer, Debbie, Parajes, Cerila, Pegler, Suzannah, Poole, Abbie, Sargent, Sister Tracey, Starling, Ellen, Taylor, Becky, Winter, Sister Helen, Zinyemba, Vivian, Allison, Michele, Blundell, Martyn, Brookman, Catherine, Buckley, Jon, Chamberlain, Shelley, Cuffe, Donna, Davies, Stacey, Fairfax, Claire, Greedus, Helen, Griffin, Hannah, Koehler, Ingrid, Lydon, Anna, Michels, Jorg, Pym, Julia, Roberts, Fiona, Srinivasan, Rajaguru, Thornton, Lorraine, Vandecandalaere, Elaine, Watts, Erica, Welsh, Linda, Anderson, Jim, Anderson, Juliette, Ball, Julie, Benton, Louise, Bowers, Richard, Burlace, Barbara, Casanova, Nathalie, Chahal, Rohit, Coyle, Catherine, Dickinson, Peter, Fosker, Chris, Heeley, Janine, Henry, Ann, Kanaga-Sundaram, Subramanian, Littler, Stephen, Rodda, Sree, Slack, Jonathan, Sundaram, Subramanian Kanaga, Taylor, Beverley, Weston, Philip, Chan, Andrew, Chettle, Judith, Gamble, Tina, Griffiths, Theresa, Hartwell, Lyn, Johnson-Rollings, Ashley, Jones, Julia, Millage, Helen, O'Neill, Eilish, Simmons, Elaine, Stockdale, Andrew, Walsh, Donna, Webb, Kerrie, Williams, Jo, Anderson, John, Dymond, Harvey, Eleftheriadis, Symeon, Hilman, Serena, Lloyd-Jones, Hugh, Wells, Tom, Bennett, Sarah, Dunn, Sharon, Fildes, Diane, Hassall, Alison, Herbert, Gaynor, Hughes, Kathryn, Innes, Helen, Kelly, Alison, Littler, John, Lomax, Laurie, Miller, Nikki, Nutman, Chris, Stott, Matthew, Syndikus, Isabel, Tolan, Shaun, Weston, Alison, Baker, Ivanna, Carter, Vanda, Chatfield, Jenny, Churn, Mark, Cooke, Peter, Grant, Anna, Homer, David, Keng-Koh, Pek, Kirk, Christine, Lomas, Claire, McCormick, Marian, Mohseeni, Arizoo, Pawaroo, Renita, Radford, Liz, Rogers, Jason, Samanci, Ali, Sayers, Ian, Sharman, Emma, Spencer, Hazel, Spruce, Debbie, Worthington, Hannah, Bak, Dagmara, Bowen, Jo, Capaldi, Lisa, Cleary, Kristy, Davies, Sue, Forkes, Janet, Gauntlett, Monica, Gupta, Kamalnayan, Healey-Mariano, Jennifer, Hodson, Hayley, Holdsworth, Amanda, Ledger, Kate, Morrow, Hugh, Perry, Heather, Rimell, Patricia, Rosoman, Alison, Taylor, Jacob, Tranter, Helen, Tyler, Jayne, White, Ann, Bloomfield, David, Crowe (nee Hughes), Dawn, Flynn-Batham, Marian, Folkes, Linda, Funnell, Sarah, Gilbert, Jeanette, Gomez-Marcos, Raquel, Gonzalez, Celia, Hoskins, Chloe, Moore, Sally, Nikapota, Ashok, Plataniotis, George, Rippin, Sarah, Smith, Matthew, Tsawayo, Tan, Turner, Nikki, Wakelen, Paula, Brady, Joanne, Camilleri, Philip, Carpenter, Janice, Cserbane, Anita, Fernandez, Claire, Hogbin, Emma, Hyde, Katherine, Joshi, Manisha, Kavanagh, Roisin, Kurup, Rahul, Manyangadze, Sarah, McLain-Smith, Susan, Ngumo, Alice, Panakis, Niki, Protheroe, Andrew, Pwint, Thinn, Sabharwal, Ami, Trewsmith, Neil, Wynn, Hazel, Allison, Joanna, Barron, Claire, Beaumont, Erica, Beer, Nigel, Beesley, Kate, Donaldson, David, Duckett, Tracey, Fox, Shirley, Gray, Emma, Kotze, Michelle, Kumar, Shiyam, Macrory, Joanne, Perry, Jess, Pippard, Lucy, Porter, Tim, Rennie, Kerry, Sparrow, Geoffrey, Williams-Yesson, Barbara, Balderson, Jane, Bottomley, David, Bowling, Lorna, Brittain, Paul, Brookes, Claire, Campbell, Hilary, Campos, Monica, Conway, Rachel, Davies, Flor, Donnison, Michelle, Elliott, Mark, Evans, Richard, Fallah, Fereshteh, Fearnley, Mark, Giddings, Emma, Gollapothu, Srilakshmi, Harvey, Sally, Holbrook, Andy, Howard, Lucy, Ingham, Jo, Jakeman, Christine, Joseph, Joji, Mole, Lisa, Musunuru, Hima Bindu, Pashley, Sarah, Saleem, Shamila, Stower, Michael, Strider, Paula, Taylor, Carol, Thackray, Louis, Todd, Joanna, Wightman, John, Wilson, Russ, Youngs, Nora, Byrne, Mary Marg, Deenihan, Emma, Durkan, Garret, Jennings, Marian, Mahoney, Catriona, Martin, Joseph, McInerney, Veronica, Raftery, Liz, Rogers, Eamonn, 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Patricia, MarshGray, Kimberley, Morton, Gerard, Murphy, Cara, Pascoe, Caitlin, Pokhrel, Sujata, Stevens, Christiaan, Yoon, Frederick, Yu, Jason, Dubey, Arbind, Sivananthan, Gokulan, Bahary, JeanPaul, Barkati, Maroie, Beauchemin, MarieClaude, Benth, Silvine, Carbonaro, Adriana, Delouya, Guila, ElSokhn, Nidale, Frazzi, Alexandra, Lambert, Carole, Lavertu, Sophie, Menard, Cynthia, Pan, Siew Siew, Rousseau, Pierre, Saad, Fred, Taussky, Daniel, Trudel, Diane, Amanie, John Oliver, Batz, Margaret, Borynec, Carol, Danielson, Brita, Garcia, Leni Santiago, Gramlich, Colin, Lang, Monika, Larson, Beverly, Mallett, Debbie, McCrudden, Sylvia, Murtha, Albert, Parker, Shelley, Parliament, Matthew B, Patel, Samir, Pearcey, Robert, Pervez, Nadeem, Rose, Jim, Shewchuk, Caroline, Smith, Carlie, Usmani, Nawaid, Williams, Candra, Yee, Don, AlRashdan, Abdulla, Avery, Alison, Beaton, Heather, Bond, Brittany, BrucePayne, Kim, Burbridge, Susan, Bursey, Kara, Carvery, Lane, Dill, Kendra, Dugas, Stevie, Harding, 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A, Johnson, Craig, Kenny, Shannon, Lock, Michael I, Loughlin, Kate, Mardell, Stephen, Mayer, Heather, Rodrigues, George B, Sax, Lori, Sebborn, Kes, Sexton, Tracy, Venkatesan, Varagur M, tenHaaf, Darlene, Chatelain, AnneMarie, Fortin, Jacqueline, Houle, Stefanie, Khaouam, Nader, Lachance, Nicole, Laliberte, Benoit, Lemaire, Celine, Nguyen, David H A, Sylvestre, Alma, Vavassis, Peter, Yassa, Michael, Adnaan, Jamila, Briand, Wayne, Casey, Linda, Chipman, Penny, Corwin, Charlotte, Duclos, Marie, Faria, Sergio Luiz, Ghoul, Mokhtar, Joncas, MarieClaude, Koulouris, Zoe, Larocque, Stephanie, Lee, Brenda, Olha, Carolynna, Robert, MarieFrance, Sharma, Rajesh, Shenouda, George, Souhami, Luis, Albrecht, Virginia, Froese, Jane, Froude, Nancy, Howitt, Pamela, Iqbal, Fawaad, Kift, Shannon, Koll, Wayne, Li, Audrey, MacGregor, Barbara, Manganaro, Aileen, McKee, Dianne, Mendoza, Edeliza, Mui, Jimmy, Myrehaug, Sten, Singson, Angelina, Slessor, Janet, Somasiri, Ashane, Stevens, Nicole, Berghout, Koralee, Bourque, JeanMarc, Carkner, Amy, E, Choan, Eapen, Libni, Eze, Nancy, Fish, Matt, Haddad, Alain, Hicks, Kimberly, Iordanidi, Elina, Kanji, Femina, Kendal, Wayne S, MacMullin, Lisa, MacRae, Robert M, Malone, Shawn, Morgan, Scott, Nguyen, Julie, OBrien, Anna, Page, Nancy, Perry, Gad A, Reeves, MarieClaude, Roach, Joanne, Sanchez, Maria, Spearman, Mary, Spencer, Patti, Tikoo, Archana, Turriff, Lisa, Wright, Natalie, Bedoya, Luis Diaz de, Cossette, Danielle, Cusan, Leonello, Desrosiers, Isabelle, Dufour, Nathalie, Foster, William, Gomez, JoseLuis, Hathout, Lara, Lajoie, MarieAndree, Martin, AndreGuy, Pouliot, Sophie, Vigneault, Eric, Bouchard, Myriam, Bujold, Rachel, Carmel, Michel, Cote, Genevieve, Dion, Helene, Ebacher, Annie, Gauthier, Isabelle C, Ladouceur, Cynthia, Lamoureux, Alexandra, Larouche, Julie, Marquis, Tania, Nabid, Abdenour, Pearson, Diane, Sabbagh, Robert, TetreaultLaflamme, Audrey, Baker, Erin, Blackwood, Geoffrey, Galway, Denise, Greenland, Jonathan, Hannaford, Zeta, Hoskins, Ashley, House, Gail, Kamran, Asim, Manning, Kimberley, Nelder, Matthew, Noel, Lorilee, Norman, Alia, ODea, Jamie, Pinnell, Erin, Pochini, Craig, Putt, Dawne, Rahman, Arifur, Rickert, Lauren, Sathya, Jinka R, Teasdale, Breanne, Thoms, John, Whiteway, Chrystal, Whittle, Stacy, Cheung, Patrick CF, Chu, William, Chung, Hans T, Commisso, Angela, Danjoux, Cyril, Escueta, Vivette, Klotz, Laurence, Leung, Michael, Liu, Stanley, Loblaw, Douglas Andrew, Malek, Leila, Morton, Gerard, Nam, Robert K, Szumacher, Ewa, Tseng, Eric Chia Lin, Vesprini, Danny, Abed, Jessy, Ang, Daniel, Bayley, Andrew J, Berlin, Alejandro, Bristow, Robert, Catton, Charles, Chan, Heidi, Chui, Amadeus, Chung, Peter, Degendorfer, Sarah, Gospodarowicz, Mary, Gregorcic, Emily, Gumapac, Nicole, Helou, Joelle, Hsien, John, Kong, Vickie, Lalani, Nafisha, Lao, Bernadeth, Lawson, Daeria, Menard, Cynthia, Merante, Pat, Milosevic, Michael, Mirmooji, Ramesh, Muraj, Zaynab, Peres, Sarin, Petroff, Avi, Petrovska, Aleksandra, Pineda, Sandra, Quintos, Judy, Raman, Srinivas, Ramotar, Sarah, Ruiz, Maria Jose Gaitan, Sabate, Kathryn, Shukla, Arundhati, Tharahan, Nanthini, Thevarajah, Vaishaali, Tiessen, Kyoko, Topalovich, Aleksandra, Torres, Cynthia, Tse, Karen, Tsuji, Debbie, Velec, Erin, Wallace, Kris, Warde, Padraig, Bisson, MarieEve, Dahmane, Rafika, Gagne, Vanessa, Harvey, Julie, Lebrun, Benoit, Marineau, Line, Montour, Julie, Samson, Julie, Sicard, MarieEve, Thibeault, Andreanne, Vincent, Francois, Vincent, LindaSuzanne, Blood, Paul A, Canavan, Joycelin, Clements, Mia AnnaMaria, Glick, Daniel, Holloway, Caroline, Holwerda, Eleanor, Joe, Howard, Lacey, Cathy, Lam, Jacqueline, Leong, Nelson, Lim, Jan, Lomas, Alia, Pai, Howard H, Priestley, Emilie, Shahid, Negin, Vallieres, Isabelle, Duncan, Graeme, Gleave, Martin E, Goldenberg, S Larry, Karunakaran, Subashini, Keyes, Mira, KimSing, Charmaine, McKenzie, Michael, Morris, William James, Nakashima, Lynne, Peacock, Michael, Pickles, Thomas A, Rodda, Sree, Sibug, Sheena, So, Alan I, Tyldesley, Scott, Zhang, Tina Wanting, Ahmed, Shahida, Ammeter, Barb, Anderson, Leanne, Ann, Taehee, Bakija, Zeljka, Bashir, Bashir, Benjaminson, Patricia, Bergen, Leah, Cadiz, Lovely, Chahine, Jacqueline, Cherepak, Kathy, Chornopyski, Tracy, Chowdhury, Amitava D, Davies, Heather, Dawe, David, Drachenberg, Darrel E, DuMontier, Joelle, Dubey, Arbind, Eska, Tiffani, Francisco, Cristina, Gamez, Raquel, Garrett, Gina, Giovannini, Julia, Graham, Jeffrey, Guarino, Robyn, Hunter, William, Johnston, Laurel, Kitkowski, Caitlin, Klapp, Kathi, Koul, Rashmi, Kuzyk, Valerie, Last, Debbie, Li, KuanLin, Long, Heather, Majumdar, Arpita, Marek, Kathleen, McNish, Jennifer, Mills, Laurie, Morales, Maria, Nashed, Maged, Nichol, Rhonda, Ong, Aldrich Dixon, Ouelette, Ashley, Patel, DeepKumar, Pawl, Marilyn, Peak, Lisa, Priebe, Shauna, Quon, Harvey, Rizzuto, Stacey, Rudnicki, Rayleen, Santos, Priscilla, Sharma, Atul, Sivananthan, Gokulan, Squires, Mandy, Trakalo, Kathy, Turner, Christy, Vaughn, Jamie, Walker, Lori, Watson, Melanie R, Wei, Joyce, Weir, JoAnn, Woloshyn, Rose, Wood, Allison, Zhang, Hanbo, and Zwarych, Darlene
Abstract: Previous evidence indicates that adjuvant, short-course androgen deprivation therapy (ADT) improves metastasis-free survival when given with primary radiotherapy for intermediate-risk and high-risk localised prostate cancer. However, the value of ADT with postoperative radiotherapy after radical prostatectomy is unclear.
Published: 2024
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185. Duration of androgen deprivation therapy with postoperative radiotherapy for prostate cancer: a comparison of long-course versus short-course androgen deprivation therapy in the RADICALS-HD randomised trial

Author: Parker, Chris C, Kynaston, Howard, Cook, Adrian D, Clarke, Noel W, Catton, Charles N, Cross, William R, Petersen, Peter M, Persad, Rajendra A, Pugh, Cheryl A, Saad, Fred, Logue, John, Payne, Heather, Bower, Lorna C, Brawley, Chris, Rauchenberger, Mary, Barkati, Maroie, Bottomley, David M, Brasso, Klaus, Chung, Hans T, Chung, Peter W M, Conroy, Ruth, Falconer, Alison, Ford, Vicky, Goh, Chee L, Heath, Catherine M, James, Nicholas D, Kim-Sing, Charmaine, Kodavatiganti, Ravi, Malone, Shawn C, Morris, Stephen L, Nabid, Abdenour, Ong, Aldrich D, Raman, Rakesh, Rodda, Sree, Wells, Paula, Worlding, Jane, Parulekar, Wendy R, Parmar, Mahesh K B, Sydes, Matthew R, Amos, Claire, Beaney, Katherine, Bellenger, Katharine, Brawley, Chris, Chung, Christina, Cook, Adrian, Embleton, Andrew, Forcat, Silvia, Goldstein, Cindy, Grabey, Jenna, Hague, Dominic, Herasimtschuk, Anna, Islam, Zaheer, Jovic, Gordana, Maniatis, Christos, Masters, Lindsey, Mohamed, Fatima, Morgan, Rachel, Murphy, Claire, Noor, Dipa, Parmar, Max, Patterson, Paul, Pickering, Holly, Pugh, Cheryl, Rauchenberger, Mary, Ribeiro, Helena, Roach, Carol, Seray-Wurie, Fatimah, Shaji, Preetha, Sims, Hannah, Spittle, Ben, Sydes, Matthew, Tappenden, Nancy, Tsang, Lilian, Uscinska, Barbara, Brown, Teddy, Casey, Sue, Davidson, Cathy, Dellar, Conor, Fletcher, Mandy, Meyer, Ralph, Murphy, Karen, Parulekar, Wendy, Richardson, Harriet, Richardson, Karen, Whelan, Kate, Bissett, Donald, Bonner-Shand, Shelagh, Grant, Judith, Lynch, Rory, MacDonald, Graham, McWilliam, Marie, Moir, Rachel, Rodwell, Sue, Shearer, Kirsty, Smith, Margaret, Binns, Louise, Briggs, Maxine, Brown, Simon, Casanova, Nathalie, Clark, Katy, Hartley, Jasmine, Henry, Ann, Henson, Helen, Hill, Pipa, Jeyasanger, Ganesan, McAlister, Judy, Parkinson, Sharron, Shaw, Alison, Alcock, Christopher, Brady, Joanne, Camilleri, Philip, Carpenter, Janice, Hogbin, Emma, Hyde, Katherine, Kavanagh, Roisin, Ngumo, Alice, Padilla-Harris, Cheryl, Panakis, Niki, Pwint, Thinn, Sabharwal, Ami, Stammers, Tracey, Stone, Helena, Varley, Gail, Weaver, Andrew, Wynn, Hazel, Allan, Elaine, Ansari, Jawaher, Cannon, Philip, Carruthers, Ross, Coubrough, Claudia, Cowan, Chloe, Glen, Hilary, Graham, Janet, Hardie, Maryon, Love, Clare, Macleod, Nicholas, Mahmood, Rana, McClements, Jane, McGlynn, Brian, McKernan, Margaret, Mcleod, Nick, Othman, Aqilah, Ross, Kristy, Roxburgh, Patricia, Templeton, Maureen, Tufail, Aisha, Turley, Claudia, Wilson, Mark, Ali, Zulfiqer, Andrews, Kelly, Burns, Rebecca, Butterworth, Caryl, Coventry, Thomas, Evans, Angela, Jones, Gareth, Lewis, Sion, Oommen, Nikhil, Owen, Susan, Saxton, Wendy, Tapping, Sister Hayley, Thomas, Dianne, Thomas, Rachel, Walker, Beth, Williams, Rachel, Collard, Susan, Ford, Victoria, Goodhind, Frances, Holbrook, Becky, Kershaw, Elizabeth, Ley, Samantha, Martinez, Maria, McCormack, Anne, Sheehan, Denise, Toy, Elizabeth, Van-Koutrik, Lynne, Varughese, Mohini, Aitken, Katherine, Barlow, David, Beattie, Louise, Bownie-Mukumbu, Godfrey, Bryan, Rachel, Corner, Victoria, Edwards, Abigail, Fazekasne Fulep, Adrienn, Guerrero-Urbano, Teresa, Gwilt, Julie, Happle, Liz, Jackman, Dee, Krysta, Beata, Paisey, Sangeeta, Shaffer, Richard, Smith, Jackie, Westley, Rosalyne, Yusof, Hilawati, Appleby, Rowan, Avis, Joanne, Beresford, Mark, Brydon-Hill, Ruth, Cox, Christine, Curtis, Samantha, Elwell, Christine, Frim, Olivera, Hamilton, Beatrice, Hudson, Zoe, Jenner, Abigail, Mantle, Mark, Mason, Jessica, Masson, Susan, McFarlane, Jonathan, Newman, Hugh, Pocock, Abigail, Robertson, Bryony, Tomlinson, Eve, Wassall, Rebecca, Williams (Née Allen), Sister Tania, Brown, Grace, Clarke, Peter, Cole, Aiden, Cousins, Jane, Gilchrist, Gail, Hagan, Chris, Hanna, Emma, Harney, Jackie, Hetherington, Stacey, Houghton, Fionnuala, Hynds, Sharon, Jain, Suneil, Johnston, Ruth, Keane, Patrick, Lyons, Ciara, McAleese, Jonathan, McAllister, Jo, McKenna, Karen, Mitchell, Darren, Murray, Stacey, O'Sullivan, Joe, Rajan, Nambi, Shum, Poh Lin, Stewart, David, Stranex, Stephen, Totten, Grace, Chandrashekara, Madhura, Drew, Ellen, Drew, Mary (Ellen), Fletcher, Julie, Gamble, Tina, Lokes, Rachel, Schumacher, Ann, Shetty, Kavitha, Sohanpal, Sundip, Whitehouse, James, Zarkar, Anjali, Ford, Daniel, Glaholm, John, Lenton, Daniel, O'Beney, Rachael, Sohanpal, Sundip, Taylor, Helen, Thomas, Helen, Whitehouse, James, Chetiyawardana, Samantha, Doherty, A, El-Modir, Ahmed, Ford, Daniel, Glaholm, John, Gunn, Melanie, James, Nicholas, Kedge, Liz, Lokes, Rachel, Mannan, Aliyah, Marange, Josephine, Morrison, Andrew, Okubanjo, Maryanne, Olaniyi-Oduntan, Darlletta, Peterkin, Zhane, Preece, George, Qureshi, Zayn, Ross, Liliam, Samra, Tarandip, Southgate, Elizabeth, Stephens, Rebekah, Stevenson, Robert, Watson-Jones, Phillip, Zarkar, Anjali, Abdulwahid, Danya, Ainsworth, Sarah, Ashworth, Sue, Beard, Karen, Charnley, Natalie, Desai, Parth, Fielding, Bethany, Flaherty, Jan, Forrest, Diane, Frankland, Helen, Haidar, Imran, Hargreaves, Jeanette, Henry, Joanne, Hugill, Angela, Jewers, Karen, Keith, Sarah, Kilroy, Stephen, King, Jennifer, Parikh, Omi, Riley, Karen, Ryan-Smith, Janet, Thomas, Jacqueline, Thompson, Catherine, Tickle, Vivienne, Waring, Lynsey, Wise, Marcus, Abdulwahid, Danya, Ainsworth, Sarah, Ashworth, Sue, Beard, Karen, Dawson, Louise, Desai, Parth, Ditchfield, Julie, Fielding, Bethany, Flaherty, Jan, Forrest, Diane, Frankland, Helen, Haidar, Imran, Hargreaves, Jeanette, Henry, Joanne, Hugill, Angela, Jewers, Karen, Keith, Sarah, Kilroy, Stephen, King, Jennifer, Parikh, Omi, Riley, Karen, Ryan-Smith, Janet, Thompson, Catherine, Tickle, Vivienne, Waring, Lynsey, Wise, Marcus, Al-Samarraie, Amir, Ali, Zulfiqer, Bolger, Annette, Davies, Llinos, Flavin, Aileen, Heron, Jane, Hughes, Faye, Jackson, Kim, Lewis, Joanne, Manley, Rachel, Oommen, Nikhil, Rutter, Charley-Anne, Smith, Nick, Srinivasan, Vaiku, Stockport, Jane, Tapping, Sister Hayley, Thomas, Heather, Toussi, H, Wood, Cathryn, Archer, Simon, Fletcher, Joanne, Ginnelly, Helen, Kirkby, Amy, Knight (n. Sherburn), Victoria, Lawrence nee Palmer, Tara, Mashegede, Beverley, Metcalf, Karen, Panades, Miguel, Sreenivasan, Thiagarajan, Szymiczek, Kinga, Thomas, Isobel, Young, Anita, Broadley, Eve, Brock, Sue, Davies, Joseph, Jones, Stephanie, McCully, Ruby, Naraine, Nicky, Preece, Kate, Purandare, Laura, Rabbi, Taslima, Savage, Sarah, Vamplew, Luke, Bamford, Linda, Benton, Richard, Cotterill, Lauren, Henry, Ann, Inman, Hayley, Karsera, Louise, Kay, Anne Marie, Kenyon, Lucille, Lazarte, Jannika, McNulty, Dawn, Moore, Eleanor, Nallathambi, Chandran, Naylor, Deirdre, Owen, Lisa, Parikh, Shefali, Pendrill, Amy, Price, Joanne, Robertshaw, Helen, Rodda, Sree, Sewell, Jane, Teo, Mark, Waldron, Eleanor, Bloomfield, David, Blythe, Tracy, Brown, Joanne, Carr, Philippa, Chambers, Carla, Dlodlo, Jessica, Frattaroli, Paul, Gilbert, Duncan, Hart, Andrew, Hodges, Samantha, Hooper, Simon, Hunter, Catherine, Lactao, Ranee, Leitao, Madalena, McKinna, Fiona, Nikapota, Ashok, Peterson, Jane, Robinson, Angus, Tremlett, Jean, Webb, Andrew, Wilkins, Marie, Young, Bobbie, Bahl, Amit, Baxter, Matt, Beresford, Mark, Bowers, Dawn, Challipalli, Amarnath, Coe, Marc, Cowley, Sue, Gillatt, David, Griffiths, Dorothy, Henson (nee Savidge), Verity, Hilman, Serena, Hollister, Robert, Kisanga, Mary, Lee, Vivienne, Lowe, Amelia, Macefield, Rhiannon, Masson, Susan, Pegler, Ruth, Robertson, Peter, Saldanha, Helen, Simmonds, Mary, Sims, Laura, Wheeler, Lucie, Wilson, Paula, Woods, Martin, Yarrow, Sue, Zelley, Sarah, Abd Alazeez, Mohamed, Alderton, Kate, Bahl, Amit, Beresford, Mark, Brain, Carol, Challipalli, Amarnath, Chilcott, Helen, Clarke, Samantha, Cobos-Arrivabene, Marta, Corderoy, Helen, Cousins, Rebecca, Garner, Victoria, Gillatt, David, Herbert, Chris, Johnson, Lyndsey, Jones, Kathryn, Kemp, Gail, Kirkby, Sarah, Kirkpatrick, Suriya, Koupparis, Anthony, Manning, Courtney, Oxley, Jon, Perry, Emily, Persad, Raj, Phipps, Charlotte, Richmond-Russell, Kerry, Rowe, Edward, Shelton, Luke, Shiridzinomwa, Constance, Smith, Carolyn, Treasure, Ann, Wilson, Paula, Wyatt, Clare, Beesley, Katherine, Benson, Richard, Couch, Ellie, Foxwell, Sue, Goss, Vanessa, Harnwell, Juliette, Hughes-Davies, Luke, Joslin, Vicky, Laubscher, Angelique, Mazhar, Danish, Patterson, Helen, Raj, Sanjay, Ramanujachar, Ramya, Rimmer, Yvonne, Robinson, Nicola, Russell, Simon, Stone, Matthew, Thomas, Robert, Treeby, Jo, Vickery, Kathryn, Woodward, Cathryn, Ahmed, Ilyas, Appleby, Bonny, Brulinski, Patryk, Buckley, Julie, Catt, Natalie, Coppins, Helen, Crawford, Sister Denise, Davies, Julie-Ann, Drakeley, Susan, Edwards, Albert, Evans, Clary, Galante, Joao, Gladwell, Louise, Gough, Jessica, Hargreaves, Carolyn, Hegarty, Gemma, Kehoe, Laura, Larkins, Rachel, Lipsham, Margaret, Little, Jessica, Malde, Rohit, Middleton, Sharon, Mikropoulos, Christos, Mirza, Arafat, Mithal, Natasha, Nathan, Kannon, Raman, Rakesh, Rendall, Alice, Robinson, Karen, Ryan, Rachel, Sim, Van, Swann, Michelle, Tasker, Lorraine, Taylor, Katy, Thomas, Carys, Trigonis, Ioannis, Walker, Karen, Williams, Joanne, Williamson, Elizabeth, Wood, Marian, Wood, Pauline, Zurakovsky, Hilary, Bois (nee Harris), Elizabeth, Chan, Hin Fan, Chikopela, Loveness, Clark, Helen, Clements, Colette, Holliday, Samantha, Hughes, Owen, Jones, Clare, Kynaston, Howard, Mason, Malcolm, Narahari, Krishna, Pearse, Kevin, Staffurth, John, Austin, Mandy, Barnett, Christian, Camburn, Sister Tracey, Dawson, Elizabeth, Gibson, Sian, Hamid, Abdel, Kancherla, Kiran, Leone, Priscilla, Perkins, Lauren, Ramsay, Valerie, Singizi, Bryan, Allen, Julie, Anderson, Susan, Andrews (nee Moore), Eleanor, Bakawala, Rehana, Beazer, Sarah, Binks, Colin, Bowen, Jo, Broomfield, Barbara, Cook, Audrey, Crossley, Lin, Ford, Chris, Forkes, Janet, Hall, Julia, Healey-Mariano, Jennifer, Jenkins, Peter, Johnson, Nigel, Jonnada, Sai, Owen, Roger, Pratten, Elaine, Sayers, Rachel, Shimell, Chris, Skelton, Amy, Tan, Matthew, Trigg-Hogarth, Kate, Wronski, Sue, Aldous, Mary, Archer, Denise, Eccleson, Helen, Gallimore, Elizabeth, Green, Sue, Ibrahim, Azman, Jones, Carys, Prince, Jude, Chrystal, Melanie, Din, Omar, Firth, Alexandra, Ford, Nicky, Kirkbride, Peter, Martindale, John, Redfearn, Alison, Smedley (nee McCabe), Janine, Smith, Lucy, Sorice, Vittoria, Stevenson, Lesley, Toms, Julie, Wood, Sister Kim, Barke, Nyssa, Basu, Devy, Dewar, Lorna, Driscoll, Celine, Hunting, Liz, Kumar, Muthar, Leone, Priscilla, Mabelin, Louies, Muthu Kumar, Dakshinamoorthy, Sizer, Bruce, Thorogood, Lucy, Askew, Sarah, Beech, Darren, Broom, Jane, Duley, Emma, Firth, Elizabeth, Gould, Aaron, Hocking, Christopher, Johns, Louise, Kent, Emma, Madine, John, Partridge, Kerena, Penhaligon, Corrine, Pentecost, Catherine, Pollard, Kay, Poultney, Melissa, Pynsent, William, Skewes, Johanna, Steele, Anita, Thomson, Alastair H, Topps, Alice, Townley, Luke, Wheatley, Duncan, Brown, Maggie, Chan, Andrew, Connor, Marie, Elwell, Sue, Geraghty, Kerry, Griffiths, Theresa, Hayward, Lesley, Humber, Caroline, Khan, Yakhub, Lake, Sister Judith, Laverick, Rosaleen, Mansell, Gemma, Mislang, Albert, Ngwenya, Su, Panesar, Pritpal, Sanders, Kay, Simmons, Elaine, Sinfield, Melanie, Stanley, Laura, Steventon, Emily, Stockdale, Andrew, Sunny, Manju, Thind, Manreet, Tranter, Fiona, Tsalic, Medy, Worlding, Jane, Adamson, Vanessa, Bennion, Carole, Butler-Barnes, Jenny, Hough, Adele, Hough, Chris, Hughes, Joanne, Irwin, P, Javle, P, Logue, John, Mansfield, Carolyn, Smith, Rachel, Sumner, Lesley, Tomlinson, Annabel, Tran, Anna, Walker, Caroline, Wylie, James, Chakraborti, Prabir, Das, Prantik, Ennis, Dawn, Marriott, Sue, Pattu, Pugazhenthi, Ward, Colin, Alzouebi, Mymoona, Burlace, Barbara, Ferguson, Catherine, Firth, Alexandra, Hooton, Georgia, Jeffcutt, Nicole, Martindale, John, Neal, Amy, Pezaro, Carmel, Sivoglo, Virgil, Smedley (nee McCabe), Janine, Smith, Lucy, Taylor, Jennifer, Wilkinson, Nicola, Wood, Sister Kim, Afzal, Naveed, Anderson, Beverley, Andrews, Stephen, Bakker, Piet, Breakspear, Sally, Cornaby, Andrew, Crellin, Perric, Gibbins, Andrew, Gibbins, Jackie, Glen, Tracy, Goodsell, Josie, Horton, Sarah, Jones, Stephanie, Love, Sally, Masters, Benjamin, Rees, Andrew, Sharpe, Simon, Taylor, Kate, Wignall, Suzy, Harrow, Sister Kath, Jones, Lesley, Kanyi, Karen, Keng-Koh, Pek, McGarry, Karen, Ramachandra, Prakash, Smith, Lucy, Watts, Angela, Baumber, Shelley, Gilbert, Duncan, Howard, Joanna, Jones-Skipper, Kay, Manetta, Caroline, McCrisken, Lauren, McKinna, Fiona, Taylor, Jo-Anne, Williams, Amanda, Clark, Alison, Clarke, Maria, Egan, Lisa, Fiddes, Kathleen, Forman, Susan, Gardiner, Fiona, Jeffrey, David, Mariappan, Param, Mayne, Barbara, McLaren, Duncan, Rogers, Brian, Woods, Catherine, Young, Douglas, Anderson, John, Baines, Kizzy, Crundwell, M, Davey, Elizabeth, Ford, Victoria, Lawless, Theresa, Matkins, Ellen, McGinley, Shannon, McGrath, John, Piper, Jane, Roantree, Alison, Seath, Ingrid, Sheehan, Denise, Srinivasan, Rajaguru, Stott, Mark, Timmings, Katie, Webb, Claire, Al-hasso, Abdulla, Allan, Elaine, Barwell, Lorraine, Cowan, Chloe, Dodds, David, Finnegan, Niall, Griffen, Ailsa, Jones, Rob, Lawless, Claire, MacMillan, Antonia, Mahmood, Rana, Russell, Martin, Sadozye, Azmat, Shirley, Sian, Sidek, Norma, Wallace, Jan, Akhtar, Naeem, Al-hasso, Abdulla, Ansari, Jawaher, Bell, Karen, Humpreys, Louise, McWilliam, Donna, Moody, Emma, O'Neil, Rebecca, Peck, Suzannah, Turley, Claudia, Auladin, Raidah, Bird, Stephanie, Bissa, Caterina, Dabbs, Marianne, Di Maria, Fabio, Goh, Chee, Guerrero-Urbano, Teresa, Harding, Joshua, Jamieson, Ceri, Jones, Jules, Khaksar, Sara, Laing, Robert, Matthews, Laura, Medisetti, Aruna, Molony-Oates, Barbara, Money-Kyrle, Julian, Nobes, Jenny, Rabsztyn, Weronika, Shaffer, Richard, Sidi, Frances, Smale, Daisy May, Stone, Sarah, Tindall, Annie, Woods, Jane, Wu, Min, Daniel, Amelia, Holmes, Evelyn, Kellaway, Joanne, Lewis, Amanda, Light, Teresa, Melcher, Lucinda, Robinson, Lily, Staines, Nikki, Anderson, Susan, Carini, Serrafina, Cook, Audrey, Cooper, Sophie, Evans, Melanie, Follows, Josh, Forkes, Janet, Grant, Warren, Hedges, Andy, Hughes, Claire, Jones (Birch), Janine, King, Lisa, Lees, Laura, Mercer, Lily, Myles, Sophie, Roberts, Zara, Williamson, Nicola, Abbassi, Sara, Alonzi, Roberto, Anyamene, Nicola, Bici, Julia, Collins, Lucy, Dickson, Jeanette, Garrido-Perez, Sandra, Hoskin, Peter, Hughes, Robert, Jenkins, Suzanne, Kowalewska, Paulina, Milner, Jessica, Mitchell, Lesley, Ostler, Peter, Rashid, Aamna, Stapleton, Stephanie, Bashir, Faheem, Bone, Linzi, Clark, Diane, Dixit, Sanjay, Horne, Rhian, Jones, Dawn, Palmer, Sarah, Plowman, Kristian, Rawlings, Julie, Simms, Matthew, Addison, Anglise, Arnott, Seonaid, Borgaonkar, Sudhir, Brown, Sandra, Campbell, Audrey, Colligan, Steve, Cormack, Margaret, Doughty, Victoria, Kelly, Kay, Lister, Debbie, Macdonald, Alison, Macgregor, Carol, Mackay, Rachel, Madeleine, Jude, McNally, Morag, McPhail, Neil, Neville, Sean, Nicholls, Alison, Papakostidi, Aristoula, Simpson, Georgina, Sinclair, Glenda, Skene, Anna, Telfer, Sheena, Urquhart, Zoe, Whillis, David, Etheridge, Charlotte, Ford, Amanda, Ridley, Paul, Scrase, Christopher, Upson, Susan, Venkitaraman, Ramachandran, Capaldi, Lisa, Churn, Mark, Higgins, Linda, Knott, Helen, Stringer (pr. Davis), Sally, Taylor, Jacob, Tranter, Helen, Wollaston, Julie, Brogan (nee Roddie), Stephanie, Erskine, Susan, Hamill, Maureen, Prentice, Lynn, Russell, Martin, Sidek, Norma, Symon, Lesley, Teahan, Seamus, Todd, Anne, Tweedle, James, Young, Sally, Austin (nee Glover), Gemma, Boon, Ian, Bottomley, David, Breckin, Edmund, Clarke, Jude, Coyle, Catherine, Davies, Emily, Goulding, James, Henry, Ann, Kiltie, Anne, Loughrey, Carmel, Moore, Eleanor, Murad, Fatima, Pratt, Claire, Shuttleworth, Pam, Smith, Beccy, Smith, Gill, Branson (née Dineen), Amy, Christmas, Sallyanne, Cooke, Jill, Griffiths, Leyshon, Kent, Christopher, King, Amy, Kockelbergh, Roger, Potterton, Janet, Speed, Lesley, Vasanthan, Subramanian, Walker, Julia, Archer, Simon, Baria, Karin, Bateman, Samantha, Coombs, Sarah, Cunningham, Amy, Dahar, Nazeer, Daruwala, Pallon, Francis, Olesya, Ginnelly, Helen, Hall, Jane, Hoare, Kathryn, Key, Claire, Lockwood, Carol, Mark, Ian, Newton, Rachel, Okubanjo, Maryanne, Panades, Miguel, Sloan, Andrew, Sreenivasan, Thiagarajan, Bermingham, Nicola, Dean, Lynsey, Malik, Zafar, Pilkington, Pauline, Robson, Peter, Rogers, Heather, Rogers, Thomas, Treherne, Katy, Bourke, Amie, Chikkamuniyappa, Bindu, Dalton-Short, Ross, Edwards, Steve, Falconer, Alison, Ford, Amy, Harinarayanan, Sathish, Hassan, Nebah, Hornzee, Gillian, Magwaro, Sophia, McInerney, Sue, Pratap, Bhavesh, Rezkallah, Sarah, Sadare (Olaleye), Ibiyemi, Stancliffe, May, Stewart, Simon, Thoi, Anne-Maree, Westrop, Anna, Ahamadali, Sheefa, Arsanious, Nasr, Balogun, Shaki, Batish, Cheryl, Campbell, Yvonne, Crowley, Claire, Dunne, Emma, Ferreira, Miguel, Haldeos, Anne, Hijab, Adham, Huddart, Robert, John, Babbin, Michaelidou, Andriana, Nawrozzadeh, Mohammed, Pach, Jackie, Patel, Priyanka, Payne, Ann, Perry, Matthew, Sadare (Olaleye), Ibiyemi, Serra, Maria, Springall, Christine, Thompson, Jane, Thomson, Sister Jane, Tree, Alison, Aya, Viviana, Beaney, Ronald, Bower, Lorna, Conway, Lisa-Jane, Donaghey, Sally, Gordon, Hussain, Grau, Isabel, Guerrero-Urbano, Teresa, Harris, Sarah, Hogan, Vesna, Hore, Brendan, Hughes, Simon, Jones, Katie, Kuenzig, Greg, Morris, Stephen, Nathan, Kannon, Nunney, Dawn, Rashid, Jessica, Reynolds, Philip, Rooprai, Bali, Chikkamuniyappa, Bindu, Dalton-Short, Ross, Edwards, Steve, Hassan, Nebah, Magwaro, Sophia, Mangar, Stephen, Rezkallah, Sarah, Stewart, Simon, Thoi, Anne-Maree, Westrop, Anna, Azirar, Hanna, Caumont, Tom, Gupta, Nishi, Hill, Judy, Karp, Stephen, Kullane, Sagal, Macavoy, Tina, Melcher, Lucinda, Neave, Farhad, Newby, Jackie, Van Someren, Chloe, Anderson, Naomi, Annan, Lynda, Cooke, Juniebel, Douch, Emma, Fawcitt, Sara, Gore, Alexandra, Grieve, Sylvia, Jarvis, Claire, McCadden, Angela, Needleman, Sarah, Pigott, Katherine, Powell, Hannah, Ramtohul, Kaliyanee, Rosenfelder, Nicola, Vilarino-Varela, Maria, Ali, Jennyfa, Ali, Maryam, Banda, Laillah-Crystal, Bott, Trevor, Brown, Emmanuel, Bryant, Hanna, Carlino, Giulia, Grant, Cordelia, Hogan, Holly, Khoo, Vincent, Mojidra, Chintan, Montebello, Joseph, Morrison, Jennifer, Olabanji, Matthew, Pillai, Sijy, Siu, Bernard, Stafferton, Ruth, Storrs, Sarah, Townsend-Thorn, Debra, Tree, Alison, Van As, Nicholas, Vijayakumar, Vijitha, Welsh, Liam, Dearnaley, David, Gao, Annie, Horwich, Alan, Huddart, Robert, Lewington, Val, Marshall, John, Murphy, Louise, Parker, Chris, Venkitaraman, Ramachandran, Beaton, Kirsty, Bech-Nielsen, Nanette, Bolton, Olivia, Chetiyawardana, Samantha, Conibear, John, Dekaj, Fatjon, Hillman, Paul, Jayachandran, Resmi, Kaliski, Alexandre, Kiff, Janet, Nicholson, Alastair, Nixon, Jude, Oladimeji, Janet, Riches, Oscar, Smith, Dan, Tipples, Karen, Wells, Paula, Anderson, Chris, Daly, Deirdre, Dover, Serena, Gilmartin, Claire, Golden, Sophie, Gregg, Jane, Issa, Rami, Khoo, Vincent, Oni, Titilayo, Patel, Chandni, Perry, Matthew, Quarrell, Mark, Rolfe, Debbie, Tighe, Helen, Toledo, Jesusa, Tuazon, Juel, Varro, Robert, Bohra, Vikram, Caballes, Mark, Custins, Laura, Hornzee, Gillian, Ivie, Stephanie, Joshi, Manisha, Liau, Joy, Magwaro, Sophia, Naim, Farhan, Patel, Anup, Pathmanathan, Byiravey, Russell, Emily, Stewart, Simon, Thoi, Anne-Maree, Tippins, Anna, Westrop, Anna, Agdiran, Didem, Aggarwal, Ajay, Amoaten, Thomas, Aslam, Natasha, Bonsu, Nicole, Butt, Zainab, Danaswamy, Patricia, Davda, Reena, Gillesen, Annelies, Goel, Roshni, Hall, Julia, Khong, Rachel, Lowi, Suzy, Merrick, Richard, Mitra, Anita, Payne, Heather, Teh, Jonathan, Whinn, Samantha, Woolf, David, Zilkha, Helene, Bech-Nielsen, Nanette, Bolton, Olivia, Danaswamy, Patricia, Dawson-Athey, Linda, Foster, Emma, Gillian, Andrew, Green, James, Hillman, Paul, Hines, John, Holden, Simon, Jayachandran, Resmi, Kazingizi, Memory, O'Neill, John, Olaoni, Thompson, Peters, John, Philp, Timothy, Wells, Paula, Woolf, David, Zaidi, Sadaf, Adeyoju, Adebanji, Adinkra, Victoria, Brough, Richard, Brown, Stephen CW, Collins, Gerald, Hardstaff, Lisa, Hill, Pippa, Kujawa, Magda, McCurrie, Marilyn, Townley, Barbara, Woodhouse, Iain, Zafar, Waheed, Beesley, Sharon, Breen, Vivienne, Brulinski, Patryk, Burrage, Su, Calvert, Clare, Davison, Alison, Dias, Heather, Fossey, Gavin, Hotine, Jodie, Jones, Ifan, Jope, Carmel, Lines, Yvonne, Martins, Sarah, Mercier, Barbara, Mikropoulos, Christos, Murray, Jane, Neshiri, Innocent, Pamphlett, Ian, Phillips, Ann, Rai, Pavnish, Rezkallah, Sarah, Roberts, Verity, Taylor, Henry, Taylor, Katy, Thomas, Carys, Tribe, Lisa, Wood, Pauline, Bottomley, Ian, Buchan, Jonathan, Carpio, Viviana, Choudhury, Ananya, Conroy, Ruth, Cowan, Richard, Davison, Sue, Duncan, Ian, Elliott, Tony, Fair, Kim, Fallaize, Adrian, Flanagan, Laura, Green, Sarah, Harris, Catherine, Hart, Amber, Jones, Cathryn, Livsey, Jacqueline, Logue, John, McCaul, Damian, Nazari, Roonak, Newbery, Carol, O'Connor, Kate, O'Dwyer, Sister Jackie, Oliver, Joanne, Onoge, Ekugbe, Petsa, Maria, Redshaw, Catherine, Smith, Sarah-Ellen, Spoor, Willemijn, Thomas, Sister Viv, White, Holly, Worsley, Lucy, Wylie, James, Bailey, Linda, Bennett, Molly, Corless, Rebecca, Duffy, Annie, Fellows, Kathryn, Gipson, Anna, Haydock, Helen, Knox, WF, Liptrott, Sarah, McCarth, Fiona, McCartin, Fiona, Mistry, Heena, Mthethwa, Thobekile, Partington, Lillian, Piper, Lindsay, Ramani, Vijay, Sahin, Sarah, Sangar, Vijay, Slevin, Kathryn, Tuck, Jonathan, Wylie, James, Atkinson, Lorraine, Barnbrooke, Sarah, Barnes, Alison, Brownless, Caroline, Chadwick, David, Hardman, John, Harland, Keith, Long, Carol, McAuliffe, Sarah, McBride, Julia, McClurey, Sister Patricia, Mohan, Rita, Naylor, Lynne, Peedell, Clive, Robson, Paula, Shakespeare, Devadasan, Thompson, Emma, Van der Voet, Hans, Wilson, David, Barron, Chris, Frew, John, Lambert, Katy, McMenemin, Rhona, Pedley, Ian, Riseborough, Danielle, Roberts, Trevor, Stockley, Emma, Stoddart, Jonathan, Tate, Janine, Walker, Jayashree, Workman, Bridget, Barnett, Debra, Carter, Adam, Cox, Adam, Davey, Alison, Davies, Kirstin, Gilby, Lisa, Hodge, Simon, Kearney, S, Lavender, Helene, Marty, Janet, Nash, Maxine, Rahman, Syhed, Richards, Jayne, Simpson, Julie, Wall, Elaine, Webb, Paula, Wild, Karen, Williams, Rachel, Wilson, Jim, Branagan, Jenny, Camilleri, Philip, Dell, Gillian, Elwell, Christine, Foo, Maxine, Goyena, Ruby, Jones, Andrea, McBain, Hazel, McGrath, Katherine, O'Callaghan, Jane, Ramanathan, Dorai, Tee, Elizabeth, Thominson, Bronwen, Tighe (nee Bussey), Rachel, Archer, Denise, Beety, Jane, Birch, Richard, Bloomfield, Mark, Callam, Sara, Cooper, Adele, Darby, Helen, Healey, Gail, Heasley, Karen, Kapur, Guarav, London, Clare, Nobes, Jenny, Oosterom, Katrien, Ostrowski, Joe, Shobrook, Bridget, Turner, Sarah, Wade, Rob, Walker, Suzanne, Walton, Sharon, Websdale, Cheryl, White, Joanna, Alder, Leanne, Chivers, Rachael, Gooch, Carol, Howard, Lucy, Hutchinson, Camille, Jevons, Cody, Kumar, Daniel, Marley, Hazel, Mills, Jamie, Purves, Hanna, Sayers, Ian, Sundar, Santhanam, Szolin-Jones, Jacob, Walker, Georgina, Wood, Catherine, Choudhury, Ananya, Conroy, Ruth, Cook, Wendy, Gill, Lisa, Johnson, Joanne, Johnstone, Dawn, Jones, Richard, Livsey, Jacqueline, Mistry, Leena, Scarratt, Lyndsay, Aiku, Abimbola, Ajzensztejn, Daniel, Al-Chamali, Hiba, Andrade, Gerard, Barber, Dave, Bekulart, Sylwia, Bhatnagar, Gagan, Boutflower, Jane, Brewster, Simon, Butterfield, Anne, Camilleri, Philip, Carroll, Weronika, Cole, David J, Coutts, Tim, Doyle, Kerrie, Durrant, Lisa, Green, Trish, Hyde, Katherine, Jampana, Raj, Jastrzebska, Patrycja, Kiltie, Anne, Kyffin, Phillippa, Langridge, Nicole, Lawrey, Sarah, Markus, Sarah, Marston, Kerrie, Mooney, Matthew, Mukkath, Sandra, Murphy, Ann, Owens, Rob, Panakis, Niki, Rabsztyn, Weronika, Ruane, Sarah, Sabbagh, Ahmad, Sabharwal, Ami, Samkange, Grace, Sankighatta, Naveen, Skwarski, Michael, Stuart, Robert, Wellman, Sandie, Wilson, Jo, Cavanagh, Kerrie, Hollingdale, Abigail, Allen, Susan, Barter, Elisa, Benson, Richard, Gregory, Debbie, Ingle, Charlotte, Lawrence, Steph, Rooney, Paula-Joanne, Bishop, Emma, Brennan, Maria, Cogley, Lyn, Hammonds, John Christopher, McInerney, Paul, McLarty, Esther, Natale, Salvatore, Pascoe, Sarah, Purcell, Keith, Sells, Henry, Tyner, Sharah, yates, Victoria, Beamish, Neal, Blaney, Hilary, Brock, Sue, Clapp, Felicity, Clarke, Elizabeth, Coffin, Teresa, Crellin, Perric, Davies, Joe, Downs, Nichola, Gillespie, Sally, Goodsell, Josie, Harinarayanan, Sathish, Heckford, Louise, Iskender, Amanda, Orford, Sara, Pressdee, Sandy, Rix, Sophie, Wheelwright, Roger, Woodward, Elizabeth, Wright, Seonaid, Blight, Kathy, Dobson, Tracey, Hale, Jennifer, Harris-Burland, Angie, Khoury, Ghassan, Madhava, Kudingila, Meadows, Lorna, Nagar, Yoodhvir, Roca, Mila, Stacey, Wendy, Stephenson, Anna, Uherek, Maja, Virgo, Azarel, Watkinson, Catrin, Birtle, Alison, Chauhan, Zainab, Cornthwaite, Stephanie, Curtis, Sandra, Ellard, Rose, Fish, Nathan, Hefferon, Billy, Parikh, Omi, Searle, Claire, Spickett, Helen, Walmsley, Catherine, Wise, Marcus, Atkinson, Jane, Brown, Richard B, Cartwright, Debbie, Coomber, Kristy, Hunt, Allison, Kowalczyk-Williams, Wioletta, Lewis, Christina, Parr, Stephen, Purdon, Helen, Rogers, Paul, Vowell, Emma, Capaldi, Lisa, Featherstone, Wendy, Hamilton, Joanna, Harrison, Alison, Hindle, Margaret, Hodson, Hayley, Isaew, Andrea, Knapp, Jeanette, Moss, Sarah, Gibbs, Stephanie, Gujral, Sandeep, Jannapureddy, Revanth, Kelkar, Anand, Martinou, Maria, Mills-Baldock, Tina, Nicholson, Alastair, O'Brien, Neale, Subramaniam, Ramachandran, Tahir, Saad, Vandal, Mohammad Tanvir, Vu, Thi, Allen, Rachael, Betts, Chris, Bowmer, Amanda, Clarke, Noel, Cordwell, Amanda, Cowan, Richard, Dharmaprasad, Soney, Duncan (nee Keatley), Claire, Elliott, Tony, Farnworth, Christine, Farrell, Helen, George, Siny, Goulden, Kay, Harter, Leah, Kaushal, Malan, Kirk, Sister Sarah, Lau, Maurice, Lydon, Anne Marie, O'Flynn, Kieran, Platt, Danielle, Redshaw, Catherine, Richardson, Karen, Shackley, David, Stevenson, Garry, Taylor, Sister Melanie, Thompson, Chloe-Anne, Tran, Anna, Vinod, Cellins, Wadsworth, Oliver, Youd, Sister Jill, Attlee, Julie, Bhatnagar, Adityanarayan, Bunn, Sarah, Campbell, Allister, El-Saghir, Mohammed, Fennelly, Ruth, Guy, Peter, Gwilt, Julie, Mattocks, Lehentha, Mcintosh, Gregor, Murphy, Brenda, Reed, Catherine, Strong-Sheldrake, Sophia, Tedd, Gemma, Dent, Kathleen, Dixit, Sanjay, Hood, Marion, Martin, Karen, Pearson, Sandra, Potoczna, Dorota, Spencer, Sue, Towse, Jo, Whotton, Nikita, Bowen, Katie, Clark, Su, Clarke, Rachael, Ferguson, Catherine, Firth, Alexandra, Gibbins, Kate, Kirkbride, Peter, Martin, Jackie, Martindale, John, Smedley (nee McCabe), Janine, Smith, Suzanne, Spalton, Catherine, Tay, Jessica, Wood, Sister Kim, Adams, Marion, Bates, Emma, Chennattukungu, Indukala, Clancy, Joanna, Cross, Vanessa, Hughes, Hayley, Jose, Sanal, Kurian-Downer, Sunita, Michael, Elena, Moore, Sister Helen, Nicholas, Karen, Pope, Suzanne, Potts, Sally, Prashant, Ravi, Singh, Harpreet, Smith, Sandra, Srihari, Narayanan, Weaver, Emma, Abbas, Ali, Barnes, Nicky, Brown, Richard B, Dallas, Nicola, Jackson, Sister Ann, Kader, Nicole, Karim, Omer, Laniado, M, Litchfield, Jayne, Mahmood, Sana, O'Donnell, Helen, Robinson, Victoria, Sears, Alison, Sharif, Shahid, Sierra Pala, Ana, Sinclair, Julie-Ann, Smith, Sister Catherine, Ashcroft, Jan, Boardman, John, Bowes-Cavanagh, Jeanette, Brown, Rebecca, Chedham, Alison, Darch, Flora, Farrar, Michelle, Foot, Jayne, Goldsworthy, Simon, Graham, John, Gray, Emma, Green, Sara, Jelski, Joseph, Kalejaiye, Odunayo, Keni, Manjusha, Locke, Angela, Mahoney, Sue, McMeekin, Faith, Periasamy, Manivannan, Russell, Tamlyn, Ruszala, Elizabeth, Segaran, Surayne, Tighe, Mary, Varughese, Mohini, Wallbutton, Rebecca, Webster, Christine, Whitcher, Alison, Wiggins, Sarah, Wrigley, Martha, Zietz, Maria, Abab, Julie, Cumming, Kirsty, Galloway, Lucy, Ganabady, Malavika, Gwilt, Julie, Haskell, Annelise, Heath, Catherine, James, Naomi, Kennedy, Julie, McFarlane, Victoria, Morales-Azofra, Fabiola, Morton, Susan, Mundy, Carina, Noble, Mark, Paschalis, Alec, Patrick, Julie, Reader, Leanne, Stephenson, Anna, Taylor, Lisa, Wakefield, Shauna, Ahmed, Imtiaz, Bowman, Sue, Chan, Olivia, Cyriac, Abby, Davies, Tracey, Maitland, Katrina, Prejbisz, Jan, Reece, Sheila, Shires, Heather, Tsang, David, Anscombe, Eleanor, Brough, Richard, Brough, WA, Clitheroe, Pat, Cocks, Tracie, Collins, Gerald, Connolly nee McKenna, Sarah, Corcoran, Sam, Coughlan, Tricia, Gilmour, Christina, Goodwin, Emma, Graham, Susan, Hatimy, Umi, Haydock, Helen, Hodgkinson, Sheila, Kilmartin, John, Logue, John, Orrell, Lucy, Rotherham, Carol, Smallwood, Sarah, Taylor, Jill, Wiss, Elliott, Adab, Fawzi, Bhana, Rajanee, Dhinakaran, Kathirvelu, Evans, Marion, Myatt, Alison, Parkinson, Katrina, Peake, Angela, Sellars, Elizabeth, Smith, Rowena, Storer, Julie, Thomasson, Georgia, Vengalil, Salil, Walton, Jenny, Ward, Angela, Boam, Samantha, Burgoyne, Jamie-Rae, Chadwick, Eliot, Gill, Shafiq, Haigh, Steve, Hollis, Keri, Lim, Jun, McCabe, Alastair, Munson, Lyndsey, Nash, Dominic, Saunders, Daniel, Smith, Susan, Sundar, Santhanam, Wade, Lynne, Walker, Georgina, Evans, Elizabeth, Franklin, Alex, Harris (n. Marchant), Emily, Johnstone, Maria, Jones, Lewis, Lemon, Nicola, Phan, Mau-Don, Pudney, Delia, Spence, Rachael, Stretch, Alison, Tait, Ellen, Williams, Bethan, Belcher, Rebecca, Cole, David J, De Silva-Minor, Shiroma, Dodge, Sister Jan, Grayland, Sarah, Jones, Ania, Khan, Omar, Owen, Tim, Palmer, Debbie, Parajes, Cerila, Pegler, Suzannah, Poole, Abbie, Sargent, Sister Tracey, Starling, Ellen, Taylor, Becky, Winter, Sister Helen, Zinyemba, Vivian, Allison, Michele, Blundell, Martyn, Brookman, Catherine, Buckley, Jon, Chamberlain, Shelley, Cuffe, Donna, Davies, Stacey, Fairfax, Claire, Greedus, Helen, Griffin, Hannah, Koehler, Ingrid, Lydon, Anna, Michels, Jorg, Pym, Julia, Roberts, Fiona, Srinivasan, Rajaguru, Thornton, Lorraine, Vandecandalaere, Elaine, Watts, Erica, Welsh, Linda, Anderson, Jim, Anderson, Juliette, Ball, Julie, Benton, Louise, Bowers, Richard, Burlace, Barbara, Casanova, Nathalie, Chahal, Rohit, Coyle, Catherine, Dickinson, Peter, Fosker, Chris, Heeley, Janine, Henry, Ann, Kanaga-Sundaram, Subramanian, Littler, Stephen, Rodda, Sree, Slack, Jonathan, Sundaram, Subramanian Kanaga, Taylor, Beverley, Weston, Philip, Chan, Andrew, Chettle, Judith, Gamble, Tina, Griffiths, Theresa, Hartwell, Lyn, Johnson-Rollings, Ashley, Jones, Julia, Millage, Helen, O'Neill, Eilish, Simmons, Elaine, Stockdale, Andrew, Walsh, Donna, Webb, Kerrie, Williams, Jo, Anderson, John, Dymond, Harvey, Eleftheriadis, Symeon, Hilman, Serena, Lloyd-Jones, Hugh, Wells, Tom, Bennett, Sarah, Dunn, Sharon, Fildes, Diane, Hassall, Alison, Herbert, Gaynor, Hughes, Kathryn, Innes, Helen, Kelly, Alison, Littler, John, Lomax, Laurie, Miller, Nikki, Nutman, Chris, Stott, Matthew, Syndikus, Isabel, Tolan, Shaun, Weston, Alison, Baker, Ivanna, Carter, Vanda, Chatfield, Jenny, Churn, Mark, Cooke, Peter, Grant, Anna, Homer, David, Keng-Koh, Pek, Kirk, Christine, Lomas, Claire, McCormick, Marian, Mohseeni, Arizoo, Pawaroo, Renita, Radford, Liz, Rogers, Jason, Samanci, Ali, Sayers, Ian, Sharman, Emma, Spencer, Hazel, Spruce, Debbie, Worthington, Hannah, Bak, Dagmara, Bowen, Jo, Capaldi, Lisa, Cleary, Kristy, Davies, Sue, Forkes, Janet, Gauntlett, Monica, Gupta, Kamalnayan, Healey-Mariano, Jennifer, Hodson, Hayley, Holdsworth, Amanda, Ledger, Kate, Morrow, Hugh, Perry, Heather, Rimell, Patricia, Rosoman, Alison, Taylor, Jacob, Tranter, Helen, Tyler, Jayne, White, Ann, Bloomfield, David, Crowe (nee Hughes), Dawn, Flynn-Batham, Marian, Folkes, Linda, Funnell, Sarah, Gilbert, Jeanette, Gomez-Marcos, Raquel, Gonzalez, Celia, Hoskins, Chloe, Moore, Sally, Nikapota, Ashok, Plataniotis, George, Rippin, Sarah, Smith, Matthew, Tsawayo, Tan, Turner, Nikki, Wakelen, Paula, Brady, Joanne, Camilleri, Philip, Carpenter, Janice, Cserbane, Anita, Fernandez, Claire, Hogbin, Emma, Hyde, Katherine, Joshi, Manisha, Kavanagh, Roisin, Kurup, Rahul, Manyangadze, Sarah, McLain-Smith, Susan, Ngumo, Alice, Panakis, Niki, Protheroe, Andrew, Pwint, Thinn, Sabharwal, Ami, Trewsmith, Neil, Wynn, Hazel, Allison, Joanna, Barron, Claire, Beaumont, Erica, Beer, Nigel, Beesley, Kate, Donaldson, David, Duckett, Tracey, Fox, Shirley, Gray, Emma, Kotze, Michelle, Kumar, Shiyam, Macrory, Joanne, Perry, Jess, Pippard, Lucy, Porter, Tim, Rennie, Kerry, Sparrow, Geoffrey, Williams-Yesson, Barbara, Balderson, Jane, Bottomley, David, Bowling, Lorna, Brittain, Paul, Brookes, Claire, Campbell, Hilary, Campos, Monica, Conway, Rachel, Davies, Flor, Donnison, Michelle, Elliott, Mark, Evans, Richard, Fallah, Fereshteh, Fearnley, Mark, Giddings, Emma, Gollapothu, Srilakshmi, Harvey, Sally, Holbrook, Andy, Howard, Lucy, Ingham, Jo, Jakeman, Christine, Joseph, Joji, Mole, Lisa, Musunuru, Hima Bindu, Pashley, Sarah, Saleem, Shamila, Stower, Michael, Strider, Paula, Taylor, Carol, Thackray, Louis, Todd, Joanna, Wightman, John, Wilson, Russ, Youngs, Nora, Byrne, Mary Marg, Deenihan, Emma, Durkan, Garret, Jennings, Marian, Mahoney, Catriona, Martin, Joseph, McInerney, Veronica, Raftery, Liz, Rogers, Eamonn, Spillane, Maria, Sullivan, Frank, Teh, Chiaw Woon, Wright, Ann, Harving, Niels, Hejlesen, Finn, Kempel, Mette Moe, Steffensen, Kirsten, Bentzen, Lise, Borre, Michael, Buus, Simon, Fode, Kirsten, Kaa Bach, Birgit, Lassen, Yasmin, Laursen, Vibeke, Lemng Kruse, Helle, Lucakova, Slavka, Nørmark Iversen, Helle, Brasso, Klaus, Christensen, Anne Juel, Gruschy, Lisa, Klovgaard, Karina, Lunau, Anne Sofie, Meidhal Petersen, Peter, Moller Christensen, Trine, Poder, Lone Lund, Ribers, Kristine, Stilling, Sine, Thim, Stine, Torin Lehmann, Anna, Vandborg, Maria, Christensen, Tine, Kokholm, Kathrine Friser, Lindberg, Henriette, Nybom, Kirstine, Rønnengart, Eva, Saxe, Charlotte, Baird, Noelle, Braun, Michael W, Cheung, Arthur, Fourt, Monica, Jackson, Cathy, Jepson, Debbie, Johnson, Arlissa, Karvat, Anand, Kwan, Winkle, McIndoe, Sue, Mitchell, Donna, Sandhu, Sandeep, Serpanchy, Rosanne, Wu, Helen, Beausoleil, Rachelle, Crawford, Melanie, DiMarco, Christine, Ding, Ian, Gladwish, Adam, MacIsaac, Patricia, MarshGray, Kimberley, Morton, Gerard, Murphy, Cara, Pascoe, Caitlin, Pokhrel, Sujata, Stevens, Christiaan, Yoon, Frederick, Yu, Jason, Dubey, Arbind, Sivananthan, Gokulan, Bahary, JeanPaul, Barkati, Maroie, Beauchemin, MarieClaude, Benth, Silvine, Carbonaro, Adriana, Delouya, Guila, ElSokhn, Nidale, Frazzi, Alexandra, Lambert, Carole, Lavertu, Sophie, Menard, Cynthia, Pan, Siew Siew, Rousseau, Pierre, Saad, Fred, Taussky, Daniel, Trudel, Diane, Amanie, John Oliver, Batz, Margaret, Borynec, Carol, Danielson, Brita, Garcia, Leni Santiago, Gramlich, Colin, Lang, Monika, Larson, Beverly, Mallett, Debbie, McCrudden, Sylvia, Murtha, Albert, Parker, Shelley, Parliament, Matthew B, Patel, Samir, Pearcey, Robert, Pervez, Nadeem, Rose, Jim, Shewchuk, Caroline, Smith, Carlie, Usmani, Nawaid, Williams, Candra, Yee, Don, AlRashdan, Abdulla, Avery, Alison, Beaton, Heather, Bond, Brittany, BrucePayne, Kim, Burbridge, Susan, Bursey, Kara, Carvery, Lane, Dill, Kendra, Dugas, Stevie, Harding, Claudia, Hodder, June, Hollenhorst, Helmut, Hubley, Lynn, Joseph, Paul K, Little, Erin, MacDonald, Angela, MacIsaac, Kathy, MacVicar, Jennifer, McCracken, Jillian, Moffatt, Emily, Nieforth, Joan, Patil, Nikhilesh, Rijt, Kelsey van der, Roberts, Victoria, Rose, Tina, Rutledge, Robert DH, Salyzyn, Camryn, Simpson, Robin, Sutherland, Donna, Walker, Heather, Wilke, Derek, Yeo, Tanya, Yunace, Lorrie, Zinck, Connie, Bier, Bianca, Bucci, Catherine, Corbett, Thomas B, Dayes, Ian S, Eady, Robin, Gudelis, Susan O, Hill, Elaine, Kinrade, Yvonne, Laughlin, Anne, Lukka, Himu, Makepeace, Barbara, Marshall, Lynn, Nielsen, Linda, Oliverio, Carmela, Patel, Malti Behn, Tsakirdis, Thedoros, Wong, Jason W T, Allen, Kirsten, Bachand, Francois, Carefoot, Layton, Cotts, Shannon, Creelman, Brianna, Crook, Juanita, GeorgeChayka, Danielle, Halperin, Ross, Hartt, Nancy, Hayes, Jill A, Kim, David, Kim, TuongVan, Kronstal, Flo, Lee, Bernard, Lew, Tanya, McClelland, Marie, Opitz, Debbie, Overend, Aldyn, Reed, Melanie, Watson, Michaella, Wilkie, Karen, Winn, Carol, Yanchuk, Janet, Bonner, Scott, Brander, Mike, Brundage, Michael, Carlson, Cindy, Edwards, Jackie, ElKerdawy, Hala, Harpell, Rhonda, Hartman, Carrie, Kalyvas, Maria, KirbyBello, Denise, Kumar, Vikaash, Leach, Deborah, MacVicar, Karen, Mahmud, Aamer, Maize, Christine, Moelker, Yvonne, Murano, Abby, Paul, Nancy, Ross, Ashley, RossSmith, Marleen, Shenfield, Carey, Spencer, Craig, Tennant, Virginia, Vermette, Tracy, Willing, Stephanie, Zaza, Khaled O, Zeltser, Fred, Gearing, Jennifer, Girolametto, Carla, Gopaul, Darindra, Hamilton, Ronald, Hendel, Debra, Janke, Susan, Knackstedt, Jochen, Kuk, Joda, Morrison, MaryBeth, Mumin, Anissa, Nemirov, Stephanie, Rachakonda, Ramana, Shantz, Brenda S, Siddiqui, Atif, Thallury, Sheeba, Ahmad, Belal, Bailey, Laura, Bauman, Glenn, Camara, Mary Jane, Corkery, Danylle, DSouza, David, DiLullo, Elenor, Eichenberger, Lois, Francis, Peggy, Grant, Susan, Gratton, Albert, Gurzanski, Christina, Hare, Jennifer A, Johnson, Craig, Kenny, Shannon, Lock, Michael I, Loughlin, Kate, Mardell, Stephen, Mayer, Heather, Rodrigues, George B, Sax, Lori, Sebborn, Kes, Sexton, Tracy, Venkatesan, Varagur M, tenHaaf, Darlene, Chatelain, AnneMarie, Fortin, Jacqueline, Houle, Stefanie, Khaouam, Nader, Lachance, Nicole, Laliberte, Benoit, Lemaire, Celine, Nguyen, David H A, Sylvestre, Alma, Vavassis, Peter, Yassa, Michael, Adnaan, Jamila, Briand, Wayne, Casey, Linda, Chipman, Penny, Corwin, Charlotte, Duclos, Marie, Faria, Sergio Luiz, Ghoul, Mokhtar, Joncas, MarieClaude, Koulouris, Zoe, Larocque, Stephanie, Lee, Brenda, Olha, Carolynna, Robert, MarieFrance, Sharma, Rajesh, Shenouda, George, Souhami, Luis, Albrecht, Virginia, Froese, Jane, Froude, Nancy, Howitt, Pamela, Iqbal, Fawaad, Kift, Shannon, Koll, Wayne, Li, Audrey, MacGregor, Barbara, Manganaro, Aileen, McKee, Dianne, Mendoza, Edeliza, Mui, Jimmy, Myrehaug, Sten, Singson, Angelina, Slessor, Janet, Somasiri, Ashane, Stevens, Nicole, Berghout, Koralee, Bourque, JeanMarc, Carkner, Amy, E, Choan, Eapen, Libni, Eze, Nancy, Fish, Matt, Haddad, Alain, Hicks, Kimberly, Iordanidi, Elina, Kanji, Femina, Kendal, Wayne S, MacMullin, Lisa, MacRae, Robert M, Malone, Shawn, Morgan, Scott, Nguyen, Julie, OBrien, Anna, Page, Nancy, Perry, Gad A, Reeves, MarieClaude, Roach, Joanne, Sanchez, Maria, Spearman, Mary, Spencer, Patti, Tikoo, Archana, Turriff, Lisa, Wright, Natalie, Bedoya, Luis Diaz de, Cossette, Danielle, Cusan, Leonello, Desrosiers, Isabelle, Dufour, Nathalie, Foster, William, Gomez, JoseLuis, Hathout, Lara, Lajoie, MarieAndree, Martin, AndreGuy, Pouliot, Sophie, Vigneault, Eric, Bouchard, Myriam, Bujold, Rachel, Carmel, Michel, Cote, Genevieve, Dion, Helene, Ebacher, Annie, Gauthier, Isabelle C, Ladouceur, Cynthia, Lamoureux, Alexandra, Larouche, Julie, Marquis, Tania, Nabid, Abdenour, Pearson, Diane, Sabbagh, Robert, TetreaultLaflamme, Audrey, Baker, Erin, Blackwood, Geoffrey, Galway, Denise, Greenland, Jonathan, Hannaford, Zeta, Hoskins, Ashley, House, Gail, Kamran, Asim, Manning, Kimberley, Nelder, Matthew, Noel, Lorilee, Norman, Alia, ODea, Jamie, Pinnell, Erin, Pochini, Craig, Putt, Dawne, Rahman, Arifur, Rickert, Lauren, Sathya, Jinka R, Teasdale, Breanne, Thoms, John, Whiteway, Chrystal, Whittle, Stacy, Cheung, Patrick CF, Chu, William, Chung, Hans T, Commisso, Angela, Danjoux, Cyril, Escueta, Vivette, Klotz, Laurence, Leung, Michael, Liu, Stanley, Loblaw, Douglas Andrew, Malek, Leila, Morton, Gerard, Nam, Robert K, Szumacher, Ewa, Tseng, Eric Chia Lin, Vesprini, Danny, Abed, Jessy, Ang, Daniel, Bayley, Andrew J, Berlin, Alejandro, Bristow, Robert, Catton, Charles, Chan, Heidi, Chui, Amadeus, Chung, Peter, Degendorfer, Sarah, Gospodarowicz, Mary, Gregorcic, Emily, Gumapac, Nicole, Helou, Joelle, Hsien, John, Kong, Vickie, Lalani, Nafisha, Lao, Bernadeth, Lawson, Daeria, Menard, Cynthia, Merante, Pat, Milosevic, Michael, Mirmooji, Ramesh, Muraj, Zaynab, Peres, Sarin, Petroff, Avi, Petrovska, Aleksandra, Pineda, Sandra, Quintos, Judy, Raman, Srinivas, Ramotar, Sarah, Ruiz, Maria Jose Gaitan, Sabate, Kathryn, Shukla, Arundhati, Tharahan, Nanthini, Thevarajah, Vaishaali, Tiessen, Kyoko, Topalovich, Aleksandra, Torres, Cynthia, Tse, Karen, Tsuji, Debbie, Velec, Erin, Wallace, Kris, Warde, Padraig, Bisson, MarieEve, Dahmane, Rafika, Gagne, Vanessa, Harvey, Julie, Lebrun, Benoit, Marineau, Line, Montour, Julie, Samson, Julie, Sicard, MarieEve, Thibeault, Andreanne, Vincent, Francois, Vincent, LindaSuzanne, Blood, Paul A, Canavan, Joycelin, Clements, Mia AnnaMaria, Glick, Daniel, Holloway, Caroline, Holwerda, Eleanor, Joe, Howard, Lacey, Cathy, Lam, Jacqueline, Leong, Nelson, Lim, Jan, Lomas, Alia, Pai, Howard H, Priestley, Emilie, Shahid, Negin, Vallieres, Isabelle, Duncan, Graeme, Gleave, Martin E, Goldenberg, S Larry, Karunakaran, Subashini, Keyes, Mira, KimSing, Charmaine, McKenzie, Michael, Morris, William James, Nakashima, Lynne, Peacock, Michael, Pickles, Thomas A, Rodda, Sree, Sibug, Sheena, So, Alan I, Tyldesley, Scott, Zhang, Tina Wanting, Ahmed, Shahida, Ammeter, Barb, Anderson, Leanne, Ann, Taehee, Bakija, Zeljka, Bashir, Bashir, Benjaminson, Patricia, Bergen, Leah, Cadiz, Lovely, Chahine, Jacqueline, Cherepak, Kathy, Chornopyski, Tracy, Chowdhury, Amitava D, Davies, Heather, Dawe, David, Drachenberg, Darrel E, DuMontier, Joelle, Dubey, Arbind, Eska, Tiffani, Francisco, Cristina, Gamez, Raquel, Garrett, Gina, Giovannini, Julia, Graham, Jeffrey, Guarino, Robyn, Hunter, William, Johnston, Laurel, Kitkowski, Caitlin, Klapp, Kathi, Koul, Rashmi, Kuzyk, Valerie, Last, Debbie, Li, KuanLin, Long, Heather, Majumdar, Arpita, Marek, Kathleen, McNish, Jennifer, Mills, Laurie, Morales, Maria, Nashed, Maged, Nichol, Rhonda, Ong, Aldrich Dixon, Ouelette, Ashley, Patel, DeepKumar, Pawl, Marilyn, Peak, Lisa, Priebe, Shauna, Quon, Harvey, Rizzuto, Stacey, Rudnicki, Rayleen, Santos, Priscilla, Sharma, Atul, Sivananthan, Gokulan, Squires, Mandy, Trakalo, Kathy, Turner, Christy, Vaughn, Jamie, Walker, Lori, Watson, Melanie R, Wei, Joyce, Weir, JoAnn, Woloshyn, Rose, Wood, Allison, Zhang, Hanbo, and Zwarych, Darlene
Abstract: Previous evidence supports androgen deprivation therapy (ADT) with primary radiotherapy as initial treatment for intermediate-risk and high-risk localised prostate cancer. However, the use and optimal duration of ADT with postoperative radiotherapy after radical prostatectomy remains uncertain.
Published: 2024
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186. The molecular landscape of Asian breast cancers reveals clinically relevant population-specific differences

Author: Pan, Jia-Wern, Zabidi, Muhammad Mamduh Ahmad, Ng, Pei-Sze, Meng, Mei-Yee, Hasan, Siti Norhidayu, Sandey, Bethan, Sammut, Stephen-John, Yip, Cheng-Har, Rajadurai, Pathmanathan, Rueda, Oscar M., Caldas, Carlos, Chin, Suet-Feung, and Teo, Soo-Hwang
Published: 2020
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187. Monoamine oxidase A is down-regulated in EBV-associated nasopharyngeal carcinoma

Author: Lee, Hui Min, Sia, Alice Pei Eal, Li, Lili, Sathasivam, Hans Prakash, Chan, Melissa Sue Ann, Rajadurai, Pathmanathan, Tsang, Chi Man, Tsao, Sai Wah, Murray, Paul G., Tao, Qian, Paterson, Ian C., and Yap, Lee Fah
Published: 2020
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188. Methanol outbreak: a Malaysian tertiary hospital experience

Author: Md Noor, J., Hawari, R., Mokhtar, M. F., Yussof, S. J., Chew, N., Norzan, N. A., Rahimi, R., Ismail, Z., Singh, S., Baladas, J., Hashim, N. H., Mohamad, M. I. K., and Pathmanathan, M. D.
Published: 2020
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189. Structural Studies of Epithelial Mesenchymal Transition Breast Tissues

Author: Mohd Sobri, S. N., Abdul Sani, S. F., Sabtu, Siti Norbaini, Looi, L. M., Chiew, S. F., Pathmanathan, Dharini, Chio-Srichan, Sirinart, and Bradley, D. A.
Published: 2020
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190. Identification of Cacao Mild Mosaic Virus (CaMMV) and Cacao Yellow Vein-Banding Virus (CYVBV) in Cocoa (Theobroma cacao) Germplasm

Author: Ihsan Ullah, Andrew J. Daymond, Paul Hadley, Michelle J. End, Pathmanathan Umaharan, and Jim M. Dunwell
Subjects: badnavirus, cocoa, LAMP assay, Microbiology, QR1-502
Abstract: Cocoa, Theobroma cacao, is an important tropical perennial crop grown widely in the humid tropics. The exchange of cocoa germplasm between germplasm collections and breeding centres is vital for varietal development. Intermediate quarantine facilities, such as the International Cocoa Quarantine Centre, Reading UK (ICQC-R) play a vital role in ensuring the transfer of germplasm whilst minimising the risk of spreading pests and diseases. Current screening procedures combine visual inspection and molecular techniques, which are effective in detecting Cocoa swollen shoot virus (CSSV), a badnavirus, which causes severe losses but are restricted to West Africa. However, the detection of latent or mild virus infections that produce no visual symptoms has been a challenge. Recently two badnavirus species of cocoa producing mild symptoms, cacao mild mosaic virus (CaMMV) and cacao yellow vein-banding virus (CYVBV), have been sequenced. Here, we report new assays for the detection of these two species, for the first time in non-symptomatic accessions. Evolutionary and bioinformatic analyses of the viruses suggest their most recent source was from Trinidad, though there is historic evidence that these viruses may have their origin in South America and then become widespread globally over the last century. We also report a novel colorimetric Loop-mediated isothermal amplification (LAMP) assay for the detection of CYVBV. This simple and accurate method could be employed in field virus testing.
Published: 2021
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191. Hybrid of the Lee-Carter Model with Maximum Overlap Discrete Wavelet Transform Filters in Forecasting Mortality Rates

Author: Nurul Aityqah Yaacob, Jamil J. Jaber, Dharini Pathmanathan, Sadam Alwadi, and Ibrahim Mohamed
Subjects: MODWT, DWT, BL14, Coiflet, least asymmetric, wavelet, Mathematics, QA1-939
Abstract: This study implements various, maximum overlap, discrete wavelet transform filters to model and forecast the time-dependent mortality index of the Lee-Carter model. The choice of appropriate wavelet filters is essential in effectively capturing the dynamics in a period. This cannot be accomplished by using the ARIMA model alone. In this paper, the ARIMA model is enhanced with the integration of various maximal overlap discrete wavelet transform filters such as the least asymmetric, best-localized, and Coiflet filters. These models are then applied to the mortality data of Australia, England, France, Japan, and USA. The accuracy of the projecting log of death rates of the MODWT-ARIMA model with the aforementioned wavelet filters are assessed using mean absolute error, mean absolute percentage error, and mean absolute scaled error. The MODWT-ARIMA (5,1,0) model with the BL14 filter gives the best fit to the log of death rates data for males, females, and total population, for all five countries studied. Implementing the MODWT leads towards improvement in the performance of the standard framework of the LC model in forecasting mortality rates.
Published: 2021
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192. Prevalence of HER2 Positivity and Its Clinicopathological Correlation in Locally Advanced/Metastatic Gastric Cancer Patients in Malaysia

Author: Rajadurai, Pathmanathan, Fatt, Ho Kean, and Ching, Foo Yoke
Published: 2018
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193. Patient-Specific Cardiovascular Computational Modeling: Diversity of Personalization and Challenges

Author: Gray, Richard A. and Pathmanathan, Pras
Published: 2018
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194. Profiling differential microRNA expression between in situ, infiltrative and lympho-vascular space invasive breast cancer: a pilot study

Author: Soon, Patsy S., Provan, Pamela J., Kim, Edward, Pathmanathan, Nirmala, Graham, Dinny, Clarke, Christine L., and Balleine, Rosemary L.
Published: 2018
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195. Challenges in the management of massive intraorbital and hemifacial arteriovenous malformation as causing life-threatening epistaxis

Author: Anura Michelle Manuel, Santhi Kalimuthu, Sitra Siri Pathmanathan, Prepageran Narayanan, Zurina Zainal Abidin, Khairul Azmi, and Alizan Khalil
Subjects: arteriovenous malformation, epistaxis, hemifacial, Surgery, RD1-811
Abstract: Arteriovenous malformations are congenital lesions that may evolve with time and manifest in a plethora of presentations. They can occur as torrential epistaxis when it extensively involves the facial region. Multi-imaging modalities are available to assist in characterizing the structure of the lesion as well as its location and extent. This complex disease requires a multidisciplinary team approach with preoperative embolization and surgery. We present a rare cause of life-threatening epistaxis in a gentleman with a longstanding orbital and hemifacial arteriovenous malformation and discuss the complexities involved in its management.
Published: 2017
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196. Why does my diabetes make me so tired?

Author: Dharshini Karuppiah and Sivatharshya Pathmanathan
Subjects: Diseases of the endocrine glands. Clinical endocrinology, RC648-665
Abstract: No abstract available
Published: 2020
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197. Subclinical hypothyroidism: to treat or not to treat

Author: Sivatharshya Pathmanathan and Dharshini Karuppiah
Subjects: subclinical hypothyroidism, Diseases of the endocrine glands. Clinical endocrinology, RC648-665
Abstract: NAA
Published: 2020
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198. CUSTOMER PERCEIVED VALUE AND CUSTOMER RELATIONSHIP MARKETING IN B2B AGRIBUSINESSES: A CASE OF AGROCHEMICAL MARKET IN SRI LANKA

Author: Pathmanathan Sivashankar, Dilukshi Rangani, and Maheshika Rathnayake
Subjects: customers’ perceived value, customer relationship marketing, agrochemical industry, B2B markets, customer retention, re-purchase behaviour, Agriculture (General), S1-972, Agricultural industries, HD9000-9495
Abstract: This research examined the impact of customer’ perceived value on CRM in Business to Business (B2B) agrochemical agribusinesses in Sri Lanka. Customer perceived value was measured by economical value, emotional value and social value based on standard 36 attributes. A total of 110 respondents including distributors and direct dealers engaged in agrochemical marketing covering all island were interviewed. Results were obtained by both descriptive and inferential statistics. Two mediating variables; customer satisfaction and customer loyalty were used to find the impact of customer perceived value on relationship marketing attributes. Findings revealed that dealers perceived slightly higher economical values than emotional values and comparatively low social values. Weaker but positive correlations were found between dealer’s perceived emotional, economic and social values with their satisfaction and customer loyalty. Customer satisfaction and customer loyalty had mediating effects to the relationship of customer perceived value with CRM attributes hence with relationship marketing. The findings provide utilizable model for the agro chemical manufacturing companies to enhance dealer’s perceived value that contribute to higher dealer satisfaction, loyalty hence re-purchase behaviour and long term customer retention.
Published: 2019
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199. Comprehensive Uncertainty Quantification and Sensitivity Analysis for Cardiac Action Potential Models

Author: Pras Pathmanathan, Jonathan M. Cordeiro, and Richard A. Gray
Subjects: simulation, electrophysiology, credibility, robustness, canine, Physiology, QP1-981
Abstract: Recent efforts to ensure the reliability of computational model-based predictions in healthcare, such as the ASME V&V40 Standard, emphasize the importance of uncertainty quantification (UQ) and sensitivity analysis (SA) when evaluating computational models. UQ involves empirically determining the uncertainty in model inputs—typically resulting from natural variability or measurement error—and then calculating the resultant uncertainty in model outputs. SA involves calculating how uncertainty in model outputs can be apportioned to input uncertainty. Rigorous comprehensive UQ/SA provides confidence that model-based decisions are robust to underlying uncertainties. However, comprehensive UQ/SA is not currently feasible for whole heart models, due to numerous factors including model complexity and difficulty in measuring variability in the many parameters. Here, we present a significant step to developing a framework to overcome these limitations. We: (i) developed a novel action potential (AP) model of moderate complexity (six currents, seven variables, 36 parameters); (ii) prescribed input variability for all parameters (not empirically derived); (iii) used a single “hyper-parameter” to study increasing levels of parameter uncertainty; (iv) performed UQ and SA for a range of model-derived quantities with physiological relevance; and (v) present quantitative and qualitative ways to analyze different behaviors that occur under parameter uncertainty, including “model failure”. This is the first time uncertainty in every parameter (including conductances, steady-state parameters, and time constant parameters) of every ionic current in a cardiac model has been studied. This approach allowed us to demonstrate that, for this model, the simulated AP is fully robust to low levels of parameter uncertainty — to our knowledge the first time this has been shown of any cardiac model. A range of dynamics was observed at larger parameter uncertainty (e.g., oscillatory dynamics); analysis revealed that five parameters were highly influential in these dynamics. Overall, we demonstrate feasibility of performing comprehensive UQ/SA for cardiac cell models and demonstrate how to assess robustness and overcome model failure when performing cardiac UQ analyses. The approach presented here represents an important and significant step toward the development of model-based clinical tools which are demonstrably robust to all underlying uncertainties and therefore more reliable in safety-critical decision-making.
Published: 2019
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200. Effect of Heart Structure on Ventricular Fibrillation in the Rabbit: A Simulation Study

Author: Suran K. Galappaththige, Pras Pathmanathan, Martin J. Bishop, and Richard A. Gray
Subjects: rabbit, model, cardiac arrhythmia, filament dynamics, fibrillation, phase mapping, Physiology, QP1-981
Abstract: Ventricular fibrillation (VF) is a lethal condition that affects millions worldwide. The mechanism underlying VF is unstable reentrant electrical waves rotating around lines called filaments. These complex spatio-temporal patterns can be studied using both experimental and numerical methods. Computer simulations provide unique insights including high resolution dynamics throughout the heart and systematic control of quantities such as fiber orientation and cellular kinetics that are not feasible experimentally. Here we study filament dynamics using two bi-ventricular 3-D high-resolution rabbit heart geometries, one with detailed fine structure and another without fine structure. We studied filament dynamics using anisotropic and isotropic conductivities, and with four cellular action potential models with different recovery kinetics. Spiral wave dynamics observed in isotropic two-dimensional sheets were not predictive of the behavior in the whole heart. In 2-D the four cell models exhibited stable reentry, meandering spiral waves, and spiral-wave breakup. In the whole heart with fine structure, all simulation results exhibited complex dynamics reminiscent of fibrillation observed experimentally. In the whole heart without fine structure, anisotropy acted to destabilize filament dynamics although the number of filaments was reduced compared to the heart with structure. In addition, in isotropic hearts without structure the two cell models that exhibited meandering spiral waves in 2-D, stabilized into figure-of-eight surface patterns. We also studied the sensitivity of filament dynamics to computer system configuration and initial conditions. After large simulation times, different macroscopic results sometimes occurred across different system configurations, likely due to a lack of bitwise reproducibility. The study conclusions were insensitive to initial condition perturbations, however, the exact number of filaments over time and their trends were altered by these changes. In summary, we present the following new results. First, we provide a new cell model that resembles the surface patterns of VF in the rabbit heart both qualitatively and quantitatively. Second, filament dynamics in the whole heart cannot be predicted from spiral wave dynamics in 2-D and we identified anisotropy as one destabilizing factor. Third, the exact dynamics of filaments are sensitive to a variety of factors, so we suggest caution in their interpretation and their quantitative analyses.
Published: 2019
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