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1. The Development of Paediatric Malay Matrix Sentence Test (PaedMalayMST) Materials through the Construction of a Paediatric Malay Corpus

Author

Nur 'Azzah Zakaria, Saiful Adli Jamaluddin, Wan Ahmad Wan Aslynn, Greg A. O'Beirne, Nur Awatif Zulkeflee, and Atiffah Zawani Abd Razak

Subjects
Development, Malay, Paediatric, Matrix Sentence Test, Corpus, Phoneme distribution, Philology. Linguistics, P1-1091
Abstract

The selection of material is important in the development of a paediatric speech perception test. Even though several studies have documented various sources of speech testing materials, using corpus was recommended to develop a Matrix Sentence Test (MST). However, as available Malay corpus was limited to written words only, this research aims to construct a Paediatric Malay corpus from a combination of written and spoken words of children in Malaysia and to develop word-based matrix (WBM) for the Paediatric Malay Matrix Sentence Test (PaedMalayMST) based on the developed corpus. The WBM replicates the Malay grammatical structure in these three categories: number, object, and adjective. Based on these criteria, the words from the developed corpus were then filtered by considering the frequency of occurrences and its phonemic distributions to be included in the WBM. As a result, a matrix of three categories with six words for each category was constructed.

Published
2022

2. Variation in the metabolites and α-glucosidase inhibitory activity of Cosmos caudatus at different growth stages

Author

Wan Ahmad Wan-Nadilah, Muhammad Tayyab Akhtar, Khozirah Shaari, Alfi Khatib, Azizah Abdul Hamid, and Muhajir Hamid

Subjects
Cosmos caudatus, α-Glucosidase inhibition, Total phenolic content, Harvesting age, Quercetin derivatives, NMR-based metabolomics, Other systems of medicine, RZ201-999
Abstract

Abstract Background Cosmos caudatus is an annual plant known for its medicinal value in treating several health conditions, such as high blood pressure, arthritis, and diabetes mellitus. The α-glucosidase inhibitory activity and total phenolic content of the leaf aqueous ethanolic extracts of the plant at different growth stages (6, 8. 10, 12 and 14 weeks) were determined in an effort to ascertain the best time to harvest the plant for maximum medicinal quality with respect to its glucose-lowering effects. Methods The aqueous ethanolic leaf extracts of C. caudatus were characterized by NMR and LC-MS/MS. The total phenolic content and α-glucosidase inhibitory activity were evaluated by the Folin-Ciocalteu method and α-glucosidase inhibitory assay, respectively. The statistical significance of the results was evaluated using one-way ANOVA with Duncan’s post hoc test, and correlation among the different activities was performed by Pearson’s correlation test. NMR spectroscopy along with multivariate data analysis was used to identify the metabolites correlated with total phenolic content and α-glucosidase inhibitory activity of the C. caudatus leaf extracts. Results It was found that the α-glucosidase inhibitory activity and total phenolic content of the optimized ethanol:water (80:20) leaf extract of the plant increased significantly as the plant matured, reaching a maximum at the 10th week. The IC50 value for α-glucosidase inhibitory activity (39.18 μg mL− 1) at the 10th week showed greater potency than the positive standard, quercetin (110.50 μg mL− 1). Through an 1H NMR-based metabolomics approach, the 10-week-old samples were shown to be correlated with a high total phenolic content and α-glucosidase inhibitory activity. From the partial least squares biplot, rutin and flavonoid glycosides, consisting of quercetin 3-O-arabinofuranoside, quercetin 3-O-rhamnoside, quercetin 3-O-glucoside, and quercetin 3-O-xyloside, were identified as the major bioactive metabolites. The metabolites were identified by NMR spectroscopy (J-resolve, HSQC and HMBC experiments) and further supported by dereplication via LC-MS/MS. Conclusion For high phytomedicinal quality, the 10th week is recommended as the best time to harvest C. caudatus leaves with respect to its glucose lowering potential.

Published
2019
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5. The Readiness Level of Using e-Learning Among Teacher Graduate Programme (TGP)

Author

Abd Samad Noorazman, Wan Ahmad Wan Mohd Rashid, Harun Hairuddin, Mohd. Noor Siti Noor Fazelah, Ismail Affero, Abd. Rahman Mohd Wafi, and Justin Randy

Subjects
e-Learning, attitudes, skills, knowledge, Engineering (General). Civil engineering (General), TA1-2040
Abstract

Using e-Learning among Teacher Graduate Programme (TGP) to students who are newly exposed in Teaching and Learning (T&L) proses. The major problem among student’s TGP is the readiness level in knowledge, attitude and skills using e-Learning during Teaching and Learning (T&L) . This study is to identify any significant differences of the knowledge level between male and female TGP students. The design of this study is to survey research using instruments of questionnaire which are distributed to respondents among TGP students at the Faculty of Technical and Vocational, UTHM. The sample is given to 248 respondents and analysis using version SPSS 21.0. Data presented using descriptive method such as, Mean, Standard Deviation(SD), inferens statistic using Mann-Whitney U to identify the knowledge difference between male and female TGP students. The result showed that the level of application of e-Learning among TGP students in terms of knowledge, attitude and skill at moderate level. The study also showed that there are no significant knowledge difference among male and female TGP students because the result of p= 0.044 value is less

Published
2018
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6. Exploring the Educational Effectiveness of Culturally-enhanced Serious Game in Education

Author

Mohd Ariffin Mazeyanti, Wan Ahmad Wan Fatimah, and Sulaiman Suziah

Subjects
Social Sciences
Abstract

The main objective of this study is to explore the efficiency of utilizing a culturally - enhanced serious game in education particularly in Information Technology course. To attain this objective, the academic achievement of 61 participants was measured by using quantitative Quasi- Experimental research design approach. Sets of pre- and post-test questions were administered to all participants, where descriptive analysis and hypothesis evaluation were performed to the collected data. The finding revealed that there were significant increase on the mean values of participants who played culturally-enhanced serious game as compared to those who played contemporary serious game (M = 3.00). Therefore, the study suggested that culturally-enhanced serious game has potential to support education particularly in Information Technology course.

Published
2017
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13. Crystalline sirolimus-coated balloon (cSCB) angioplasty in an all-comers, patient population with stable and unstable coronary artery disease including chronic total occlusions: rationale, methodology and design of the SCORE trial

Author

Otto, Sylvia, Díaz, Victor Alfonso Jiménez, Weilenmann, Daniel, Cuculi, Florim, Nuruddin, Amin Ariff, Leibundgut, Gregor, Alfonso, Fernando, Wan Ahmad, Wan Azman, Pyxaras, Stylianos, Rittger, Harald, Steen, Philip, Gaede, Luise, Schulze, Christian, Wöhrle, Jochen, Rosenberg, Mark, and Waliszewski, Matthias W.

Published
2023
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16. Harmonising Individual Patient Level Cardiac Registry Data Across the Asia Pacific Region—A Feasibility Study of In-Hospital Outcomes of STEMI Patients From the Asia Pacific Evaluation of Cardiovascular Therapies (ASPECT) Network

Author

Reid, Christopher M., Chih, HuiJun, Duffy, Stephen J., Brennan, Angela L., Ajani, Andrew E., Beltrame, John, Tavella, Rosanna, Yan, Bryan P., Dinh, Diem, Chin, Chee Tang, Do, Loi Doan, Nguyen, Quang Ngoc, Nguyen, Hoai T.T., Wijaya, Ika Prasetya, Yamin, Muhammad, Rusdi, Lusiani, Alwi, Idrus, Sim, Kui Hian, Yip Fong, Alan Yean, Wan Ahmad, Wan Azman, and Yeo, Khung Keong

Published
2023
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21. Deep-Learning-Based Approach in Cancer-Region Assessment from HER2-SISH Breast Histopathology Whole Slide Images.

Author

Rehman, Zaka Ur, Ahmad Fauzi, Mohammad Faizal, Wan Ahmad, Wan Siti Halimatul Munirah, Abas, Fazly Salleh, Cheah, Phaik-Leng, Chiew, Seow-Fan, and Looi, Lai-Meng

Subjects
BREAST tumor diagnosis, INTERPROFESSIONAL relations, RESEARCH funding, BREAST tumors, CANCER patients, DESCRIPTIVE statistics, DIAGNOSTIC errors, DEEP learning, ONCOGENES, FLUORESCENCE in situ hybridization, RESEARCH, COMPARATIVE studies, DATA analysis software
Abstract

Simple Summary: Accurate detection of HER2 status in breast cancer is vital for effective diagnosis and treatment. While traditional fluorescence in situ hybridization (FISH) is the standard for assessing HER2, it has limitations, such as the need for expert handling and potential signal fading. Silver-enhanced in situ hybridization (SISH) offers a more stable alternative for analysis using conventional bright-field microscopy. This study explores an innovative deep-learning approach to automatically classify Normal, Amplified, and Non-Amplified regions in HER2-SISH images. Our two-stage process trains and evaluates models on annotated patches and applies the most effective model to entire slide images, generating region-specific visualizations. This method not only reduces manual workload but also shows strong potential for aiding pathologists in identifying HER2-amplified areas, thereby supporting precise breast cancer treatment. Statistical evaluations, including k-fold cross-validation and confidence intervals, further validate the robustness of our approach. Fluorescence in situ hybridization (FISH) is widely regarded as the gold standard for evaluating human epidermal growth factor receptor 2 (HER2) status in breast cancer; however, it poses challenges such as the need for specialized training and issues related to signal degradation from dye quenching. Silver-enhanced in situ hybridization (SISH) serves as an automated alternative, employing permanent staining suitable for bright-field microscopy. Determining HER2 status involves distinguishing between "Amplified" and "Non-Amplified" regions by assessing HER2 and centromere 17 (CEN17) signals in SISH-stained slides. This study is the first to leverage deep learning for classifying Normal, Amplified, and Non-Amplified regions within HER2-SISH whole slide images (WSIs), which are notably more complex to analyze compared to hematoxylin and eosin (H&E)-stained slides. Our proposed approach consists of a two-stage process: first, we evaluate deep-learning models on annotated image regions, and then we apply the most effective model to WSIs for regional identification and localization. Subsequently, pseudo-color maps representing each class are overlaid, and the WSIs are reconstructed with these mapped regions. Using a private dataset of HER2-SISH breast cancer slides digitized at 40× magnification, we achieved a patch-level classification accuracy of 99.9 % and a generalization accuracy of 78.8 % by applying transfer learning with a Vision Transformer (ViT) model. The robustness of the model was further evaluated through k-fold cross-validation, yielding an average performance accuracy of 98 % , with metrics reported alongside 95 % confidence intervals to ensure statistical reliability. This method shows significant promise for clinical applications, particularly in assessing HER2 expression status in HER2-SISH histopathology images. It provides an automated solution that can aid pathologists in efficiently identifying HER2-amplified regions, thus enhancing diagnostic outcomes for breast cancer treatment. [ABSTRACT FROM AUTHOR]

Published
2024
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24. Reporting on carbon emissions : corporate stories of implementation, motivation and challenge in the age of climate change

Author

Wan Ahmad, Wan

Subjects
363.738
Abstract

The main purpose of this research is to investigate the climate change and carbon reporting practises of UK FTSE 350 companies. The objectives cover three main areas of investigation including the extent of carbon reporting in the first mandatory year as compared to a voluntary year; the common stories told in companies’ climate change and carbon disclosure; and the dynamic of carbon reporting implementation, including the motivations for disclosure, the problems and issues faced and the disclosure implementation approach. Using storytelling as a theoretical lens, this research explore the content of disclosure, presentation and the motivation for disclosure using the storytelling concept. The data for this study was gathered through two sources: companies’ disclosure in annual reports, sustainability reports, websites and other online reporting; as well as interviews. The findings reveal that the introduction of Mandatory Carbon Reporting Requirements (MCRR) does improve the content and presentation of the companies’ carbon disclosure in annual reports, thus fulfilling the objective of the legislation in encouraging reporting and promoting transparency. The study has also found that the common story themes reported in companies’ disclosures relate to stories of climate change, emissions performance and reduction, and companies’ achievements in climate change and related areas. The findings of the study also demonstrate that maintaining a good reputation, complying with regulation, fulfilling stakeholders’ expectation, improve efficiency and cost saving, and projecting morality of management are the main motivations for climate change and carbon disclosure. It was also found that data collection and accuracy are perceived to be the most challenging issues faced by companies in disclosing their emission data, especially if they operate internationally. Last but not least, the findings show that companies will consult or choose to outsource their carbon disclosure or data collection to third parties when they do not have sufficient in-house resources and expertise.

Published
2017
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25. Computational approach for counting of SISH amplification signals for HER2 status assessment.

Author

Rehman, Zaka Ur, Ahmad Fauzi, Mohammad Faizal, Wan Ahmad, Wan Siti Halimatul Munirah, Abas, Fazly Salleh, Cheah, Phaik Leng, Chiew, Seow Fan, and Looi, Lai-Meng

Subjects
HER2 gene, IN situ hybridization, DIGITAL learning, IMAGE processing, BREAST cancer
Abstract

The human epidermal growth factor receptor 2 (HER2) gene is a critical biomarker for determining amplification status and targeting clinical therapies in breast cancer treatment. This study introduces a computer-aided method that automatically measures and scores HER2 gene status from invasive tissue regions of breast cancer using whole slide images (WSI) through silver in situ hybridization (SISH) staining. Image processing and deep learning techniques are employed to isolate untruncated and non-overlapping single nuclei from cancer regions. The Stardist deep learning model is fine-tuned on our HER2-SISH data to identify nuclei regions, followed by post-processing based on identified HER2 and CEP17 signals. Conventional thresholding techniques are used to segment HER2 and CEP17 signals. HER2 amplification status is determined by calculating the HER2-to-CEP17 signal ratio, in accordance with ASCO/CAP 2018 standards. The proposed method significantly reduces the effort and time required for quantification. Experimental results demonstrate a 0.91% correlation coefficient between pathologists manual enumeration and the proposed automatic SISH quantification approach. A one-sided paired t-test confirmed that the differences between the outcomes of the proposed method and the reference standard are statistically insignificant, with p-values exceeding 0.05. This study illustrates how deep learning can effectively automate HER2 status determination, demonstrating improvements over current manual methods and offering a robust, reproducible alternative for clinical practice. [ABSTRACT FROM AUTHOR]

Published
2024
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26. Review of In Situ Hybridization (ISH) Stain Images Using Computational Techniques.

Author

Rehman, Zaka Ur, Ahmad Fauzi, Mohammad Faizal, Wan Ahmad, Wan Siti Halimatul Munirah, Abas, Fazly Salleh, Cheah, Phaik Leng, Chiew, Seow Fan, and Looi, Lai-Meng

Subjects
FLUORESCENCE in situ hybridization, EPIDERMAL growth factor receptors, HER2 gene, DEEP learning, IN situ hybridization
Abstract

Recent advancements in medical imaging have greatly enhanced the application of computational techniques in digital pathology, particularly for the classification of breast cancer using in situ hybridization (ISH) imaging. HER2 amplification, a key prognostic marker in 20–25% of breast cancers, can be assessed through alterations in gene copy number or protein expression. However, challenges persist due to the heterogeneity of nuclear regions and complexities in cancer biomarker detection. This review examines semi-automated and fully automated computational methods for analyzing ISH images with a focus on HER2 gene amplification. Literature from 1997 to 2023 is analyzed, emphasizing silver-enhanced in situ hybridization (SISH) and its integration with image processing and machine learning techniques. Both conventional machine learning approaches and recent advances in deep learning are compared. The review reveals that automated ISH analysis in combination with bright-field microscopy provides a cost-effective and scalable solution for routine pathology. The integration of deep learning techniques shows promise in improving accuracy over conventional methods, although there are limitations related to data variability and computational demands. Automated ISH analysis can reduce manual labor and increase diagnostic accuracy. Future research should focus on refining these computational methods, particularly in handling the complex nature of HER2 status evaluation, and integrate best practices to further enhance clinical adoption of these techniques. [ABSTRACT FROM AUTHOR]

Published
2024
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31. Different vascular healing process between bioabsorbable polymer-coated everolimus-eluting stents versus bioresorbable vascular scaffolds via optical coherence tomography and coronary angioscopy (the ENHANCE study: ENdothelial Healing Assessment with Novel Coronary tEchnology)

Author

Wan Ahmad, Wan Azman, Nakayoshi, Takaharu, Mahmood Zuhdi, Ahmad Syadi, Ismail, Muhammad Dzafir, Zainal Abidin, Imran, Ino, Yasushi, Kubo, Takashi, Akasaka, Takashi, Fukumoto, Yoshihiro, and Ueno, Takafumi

Published
2020
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35. The best machine learning model for fraud detection on eplatforms: a systematic literature review.

Author

Yussiff, Alimatu-Saadia, Prikutse, Lemdi Frank, Asuah, Georgina, Yussiff, Abdul-Lateef, Tetteh, Emmanuel Dortey, Ibrahim, Norshahila, and Wan Ahmad, Wan Fatimah

Subjects
FRAUD investigation, MACHINE learning, FINANCIAL management, PUBLICATIONS
Abstract

The internet has been instrumental in the development and facilitation of online payment systems. However, its associated fraudulent activities on eplatforms cannot be overlooked. As a result, there has been a growing interest in the application of machine learning (ML) algorithms for fraud detection on financial e-platforms. The goal of this research is to identify common types of fraud on financial e-platform, highlight different machine learning algorithms employed in fraud detection, and derive the best machine learning algorithms for fraud detection on e-platforms. To achieve this goal, the research followed a nine steps systematic review approach to retrieve Journals and conference publications from science direct, Google Scholar and IEEE Xplore between 2018 and 2023. Out of 2,071 articles identified and screened, 44 publications (23 articles and 21 conference proceedings) satisfied the inclusion criteria for further analysis. The random forest algorithm turned out to be the best ML algorithm because it ranked first in the frequency of usage analysis and ranked first in the performance analysis with an average accuracy of 96.67%. Overall, this review has identified the kinds of fraud on financial e-platforms, and proclaimed the best and least ML algorithm for fraud detection on financial e-platform. This can help guide future research and inform the development of more effective fraud detection systems. [ABSTRACT FROM AUTHOR]

Published
2024
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36. Adoption of tax digitalisation among Malaysian tax practitioners.

Author

Zakaria, Maheran, Wan Ahmad, Wan Noraswaniaty, Hussin, Nuraini Che, Hassan, Rabaatul Azira, Marzuki, Marziana Madah, Syukur, Muhammad, and Sari, Eka Nurmala

Subjects
*PARTIAL least squares regression, *DIGITAL technology, *STRUCTURAL equation modeling
Abstract

The rapid advancement of digitalization has significantly impacted various aspects of accounting professions, particularly in taxation. Tax digitization offers numerous advantages, including streamlining tax processes, reducing administrative burdens, increasing efficiency, and enhancing data security. While tax practitioners in advanced economies have embraced digitalization, their Malaysian counterparts are still in the early stages of transitioning to a modern digital system. This situation has prompted researchers to predict factors that could accelerate the adoption of tax digitalization among Malaysian tax practitioners. Emulating the Unified theory of acceptance and use of technology (UTAUT), this study investigates the adoption of tax digitalization with performance expectancy, effort expectancy, social influence, and facilitating conditions. The researchers distributed 200 questionnaires to Malaysian tax practitioners. However, only 142 proceeded for further analysis. Results from multiple regression using partial least squares structural equation modelling (PLS-SEM) 3 indicate that all variables: effort and performance expectancy, social influence, and facilitating conditions exhibit a significant relationship with tax digitalization adoption. These findings provide valuable insights for policymakers, tax authorities, and professional bodies in developing strategies and initiatives to promote the adoption of tax digitalization among practitioners. Embracing digitalization is crucial for transforming the profession and fostering efficiency, sustainability, and resilience. [ABSTRACT FROM AUTHOR]

Published
2024
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37. JOB CREATORS OF FRESH GRADUATES IN TECHNICAL INSTITUTIONS.

Author

Nashir, Irdayanti Mat, Mustapha, Ramlee, Bin Ismail, Mohamad Amiruddin, Partodiwiryo, Sudiyatno Dikas, and Wan Ahmad, Wan Nurlisa

Subjects
COLLEGE curriculum, BUSINESSPEOPLE, BUSINESS enterprises, BUSINESS skills, BUSINESS education
Abstract

This article examines the elements of job creation among technical graduates and emphasizes the need for entrepreneurship and skill development in promoting economic growth. The study finds that technical graduates have a moderate perception of the elements of job creation and lack these elements in themselves. It suggests that curriculum development, administrators, and students should improve their skills and business knowledge to increase entrepreneurship and reduce unemployment rates. The article also discusses the skills and characteristics of job creators, such as leadership, communication, adaptability, and a focus on economic growth. It recommends that technical education institutions promote entrepreneurship and provide support for students to create their own job opportunities. [Extracted from the article]

Published
2024
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38. Zakat investment in Malaysia : a study of contemporary policy and practice in relation to Shari'a

Author

Wan Ahmad, Wan Marhaini, Roff, William, Newman, Andrew, Hillenbrand, Carole, and Shihadeh, Ayman

Subjects
362.5, zakat, zakat investment, Islamic economics, Malaysia, Islamic law, Shari'a law
Abstract

Zakat is an obligatory payment that a free and rational Muslim who owns a certain amount of wealth has to observe. The idea of zakat investment was initiated by the belief that providing the poor and needy with a non-substantial amount of fund that is mostly used to pay for their consumption is not enough to tackle poverty. Collection of zakat itself also has been shown insufficient even to provide them with a subsistence level of income. Muslim scholars suggest a long-term measure involving the investment of zakat that not only supplement the poor with a stream of income that is more consistent and continuous, equip them with the opportunity for equity participation or provide them with projects that help improve their livelihood but also provide a source of revenue to fund overall Muslim economic development. However, Muslim scholars found that investment of zakat may make the payment legally vulnerable from the Shari>ca (Islamic law) point of view. Thus, many fatwa>s and views have been expressed by the Muslim scholars as guidelines to ensure that the investment activities conform to Shari>ca. This study examines contemporary policy and practice in the management of zakat investment in Malaysia, particularly at two zakat institutions: Majlis Agama Islam Wilayah Persekutuan and Lembaga Zakat Selangor. These two institutions are selected because they are the most vibrant zakat institutions in the country in terms of zakat revenue and they also are the most active in investing zakat. Given the many background complaints about the institutions’ inefficiencies in managing zakat particularly the large amount of undistributed zakat, it is thus pertinent to analyze the compliancy of these investment activities with the principles and purposes of zakat. The study finds that these institutions accordingly have moulded their investment activities not only to observe the principles of zakat but also according to the modern changes in the administration of zakat in the country as well as to cater for the recipients’ current needs. The study also finds that various policies have been set up to guide the management of these investment activities. Still, there remain numerous possibilities for improving these further.

Published
2012

42. Rivaroxaban with or without aspirin in patients with stable coronary artery disease: an international, randomised, double-blind, placebo-controlled trial

Author

SALA, JORGELINA, CARTASEGNA, LUIS, VICO, MARISA, HOMINAL, MIGUEL ANGEL, HASBANI, EDUARDO, CACCAVO, ALBERTO, ZAIDMAN, CESAR, VOGEL, DANIEL, HRABAR, ADRIAN, SCHYGIEL, PABLO OMAR, CUNEO, CARLOS, LUQUEZ, HUGO, MACKINNON, IGNACIO J., AHUAD GUERRERO, RODOLFO ANDRES, COSTABEL, JUAN PABLO, BARTOLACCI, INES PALMIRA, MONTANA, OSCAR, BARBIERI, MARIA, GOMEZ VILAMAJO, OSCAR, GARCIA DURAN, RUBEN OMAR, SCHIAVI, LILIA BEATRIZ, GARRIDO, MARCELO, INGARAMO, ADRIAN, BORDONAVA, ANSELMO PAULINO, PELAGAGGE, MARIA JOSE, NOVARETTO, LEONARDO, ALBISU DI GENNERO, JUAN PABLO, IBANEZ SAGGIA, LUZ MARIA, ALVAREZ, MOIRA, VITA, NESTOR ALEJANDRO, MACIN, STELLA MARIS, DRAN, RICARDO DARIO, CARDONA, MARCELO, GUZMAN, LUIS, SARJANOVICH, RODOLFO JUAN, CUADRADO, JESUS, NANI, SEBASTIAN, LITVAK BRUNO, MARCOS RAUL, CHACON, CAROLINA, MAFFEI, LAURA ELENA, GRINFELD, DIEGO, VENSENTINI, NATALIA, MAJUL, CLAUDIO RODOLFO, LUCIARDI, HECTOR LUCAS, GONZALEZ COLASO, PATRICIA DEL CARMEN, FERRE PACORA, FREDY ANTONI, VAN DEN HEUVEL, PAUL, VERHAMME, PETER, ECTOR, BAVO, DEBONNAIRE, PHILIPPE, VAN DE BORNE, PHILIPPE, LEROY, JEAN, SCHROE, HERMAN, VRANCKX, PASCAL, ELEGEERT, IVAN, HOFFER, ETIENNE, DUJARDIN, KARL, INDIO DO BRASIL, CLARISSE, PRECOMA, DALTON, ABRANTES, JOSE ANTONIO, MANENTI, EULER, REIS, GILMAR, SARAIVA, JOSE, MAIA, LILIA, HERNANDES, MAURO, ROSSI, PAULO, ROSSI DOS SANTOS, FABIO, ZIMMERMANN, SERGIO LUIZ, RECH, RAFAEL, ABIB JR, EDUARDO, LEAES, PAULO, BOTELHO, ROBERTO, DUTRA, OSCAR, SOUZA, WEIMAR, BRAILE, MARIA, IZUKAWA, NILO, NICOLAU, JOSE CARLOS, TANAJURA, LUIZ FERNANDO, SERRANO JUNIOR, CARLOS VICENTE, MINELLI, CESAR, NASI, LUIZ ANTONIO, OLIVEIRA, LIVIA, DE CARVALHO CANTARELLI, MARCELO JOSE, TYTUS, RICHARD, PANDEY, SHEKHAR, LONN, EVA, CHA, JAMES, VIZEL, SAUL, BABAPULLE, MOHAN, LAMY, ANDRE, SAUNDERS, KEVIN, BERLINGIERI, JOSEPH, KIAII, BOB, BHARGAVA, RAKESH, MEHTA, PRAVINSAGAR, HILL, LAURIE, FELL, DAVID, LAM, ANDY, AL-QOOFI, FAISAL, BROWN, CRAIG, PETRELLA, ROBERT, RICCI, JOSEPH A, GLANZ, ANTHONY, NOISEUX, NICOLAS, BAINEY, KEVIN, MERALI, FATIMA, HEFFERNAN, MICHAEL, DELLA SIEGA, ANTHONY, DAGENAIS, GILLES R, DAGENAIS, FRANCOIS, BRULOTTE, STEEVE, NGUYEN, MICHEL, HARTLEIB, MICHAEL, GUZMAN, RANDOLPH, BOURGEOIS, RONALD, RUPKA, DENNIS, KHAYKIN, YAARIV, GOSSELIN, GILBERT, HUYNH, THAO, PILON, CLAUDE, CAMPEAU, JEAN, PICHETTE, FRANCIS, DIAZ, ARIEL, JOHNSTON, JAMES, SHUKLE, PRAVIN, HIRSCH, GREGORY, RHEAULT, PAUL, CZARNECKI, WLODZIMIERZ, ROY, ANNIE, NAWAZ, SHAH, FREMES, STEPHEN, SHUKLA, DINKAR, JANO, GABRIEL, COBOS, JORGE LEONARDO, CORBALAN, RAMON, MEDINA, MARCELO, NAHUELPAN, LEONARDO, RAFFO, CARLOS, PEREZ, LUIS, POTTHOFF, SERGIO, STOCKINS, BENJAMIN, SEPULVEDA, PABLO, PINCETTI, CHRISTIAN, VEJAR, MARGARITA, TIAN, HONGYAN, WU, XUESI, KE, YUANNAN, JIA, KAIYING, YIN, PENGFEI, WANG, ZHAOHUI, YU, LITIAN, WU, SHULIN, WU, ZONGQUI, LIU, SHAO WEN, BAI, XIAO JUAN, ZHENG, YANG, YANG, PING, YANG, YUN MEI, ZHANG, JIWEI, GE, JUNBO, CHEN, XIAO PING, LI, JUNXIA, HU, TAO HONG, ZHANG, RUIYAN, ZHENG, ZHE, CHEN, XIN, TAO, LIANG, LI, JIANPING, HUANG, WEIJIAN, FU, GUOSHENG, LI, CHUNJIAN, DONG, YUGANG, WANG, CHUNSHENG, ZHOU, XINMIN, KONG, YE, SOTOMAYOR, ARISTIDES, ACCINI MENDOZA, JOSE LUIS, CASTILLO, HENRY, URINA, MIGUEL, AROCA, GUSTAVO, PEREZ, MARITZA, MOLINA DE SALAZAR, DORA INES, SANCHEZ VALLEJO, GREGORIO, FERNANDO, MANZUR J, GARCIA, HENRY, GARCIA, LUIS HERNANDO, ARCOS, EDGAR, GOMEZ, JUAN, CUERVO MILLAN, FRANCISCO, TRUJILLO DADA, FREDY ALBERTO, VESGA, BORIS, MORENO SILGADO, GUSTAVO ADOLFO, ZIDKOVA, EVA, LUBANDA, JEAN-CLAUDE, KALETOVA, MARKETA, KRYZA, RADIM, MARCINEK, GABRIEL, RICHTER, MAREK, SPINAR, JINDRICH, MATUSKA, JIRI, TESAK, MARTIN, MOTOVSKA, ZUZANA, BRANNY, MARIAN, MALY, JIRI, MALY, MARTIN, WIENDL, MARTIN, FOLTYNOVA CAISOVA, LENKA, SLABY, JOSEF, VOJTISEK, PETR, PIRK, JAN, SPINAROVA, LENKA, BENESOVA, MIROSLAVA, CANADYOVA, JULIA, HOMZA, MIROSLAV, FLORIAN, JINDRICH, POLASEK, ROSTISLAV, COUFAL, ZDENEK, SKALNIKOVA, VLADIMIRA, BRAT, RADIM, BRTKO, MIROSLAV, JANSKY, PETR, LINDNER, JAROSLAV, MARCIAN, PAVEL, STRAKA, ZBYNEK, TRETINA, MARTIN, DUARTE, YAN CARLOS, POW CHON LONG, FREDDY, SANCHEZ, MAYRA, LOPEZ, JOSE, PERUGACHI, CARMITA, MARMOL, RICARDO, TRUJILLO, FREDDY, TERAN, PABLO, TUOMILEHTO, JAAKKO, TUOMILEHTO, HENRI, TUOMINEN, MARJA-LEENA, KANTOLA, ILKKA, STEG, GABRIEL, ABOYANS, VICTOR, LECLERCQ, FLORENCE, FERRARI, EMILE, BOCCARA, FRANCK, MESSAS, EMMANUEL, MISMETTI, PATRICK, SEVESTRE, MARIE ANTOINETTE, CAYLA, GUILLAUME, MOTREFF, PASCAL, STOERK, STEFAN, DUENGEN, HANS-DIRK, STELLBRINK, CHRISTOPH, GUEROCAK, OSMAN, KADEL, CHRISTOPH, BRAUN-DULLAEUS, RUEDIGER, JESERICH, MICHAEL, OPITZ, CHRISTIAN, VOEHRINGER, HANS-FRIEDRICH, APPEL, KARL-FRIEDRICH, WINKELMANN, BERNHARD, DORSEL, THOMAS, NIKOL, SIGRID, DARIUS, HARALD, RANFT, JURGEN, SCHELLONG, SEBASTIAN, JUNGMAIR, WOLFGANG, DAVIERWALA, PIROZE, VORPAHL, MARC, BAJNOK, LASZLO, LASZLO, ZOLTAN, NOORI, EBRAHIM, VERESS, GABOR, VERTES, ANDRAS, ZSARY, ANDRAS, KIS, ERNO, KORANYI, LASZLO, BAKAI, JUDIT, BODA, ZOLTAN, POOR, FERENC, JARAI, ZOLTAN, KEMENY, VENDEL, 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FRANK, BARTELS, GERARD, NIEROP, PETER, VAN DER ZWAAN, COEN, VAN ECK, JACOB, VAN GORSELEN, EDWIN, GROENEMEIJER, BJORN, HOOGSLAG, PIETER, DE GROOT, MARC ROBERT, LOYOLA, ALDRIN, SULIT, DENNIS JOSE, REY, NANNETTE, ABOLA, MARIA TERESA, MORALES, DANTE, PALOMARES, ELLEN, ABAT, MARC EVANS, ROGELIO, GREGORIO, CHUA, PHILIP, DEL PILAR, JOSE CARLO, ALCARAZ, JOHN DENNIS, EBO, GERALDINE, TIRADOR, LOUIE, CRUZ, JOSEFINA, ANONUEVO, JOHN, PITARGUE, ARTHUR, JANION, MARIANNA, GUZIK, TOMASZ, GAJOS, GRZEGORZ, ZABOWKA, MACIEJ, RYNKIEWICZ, ANDRZEJ, BRONCEL, MARLENA, SZUBA, ANDRZEJ, CZARNECKA, DANUTA, MAGA, PAWEL, STRAZHESKO, IRINA, VASYUK, YURY, SIZOVA, ZHANNA, POZDNYAKOV, YURY, BARBARASH, OLGA, VOEVODA, MIKHAIL, POPONINA, TATIANA, REPIN, ALEXEY, OSIPOVA, IRINA, EFREMUSHKINA, ANNA, NOVIKOVA, NINA, AVERKOV, OLEG, ZATEYSHCHIKOV, DMITRY, VERTKIN, ARKADIY, AUSHEVA, AZA, COMMERFORD, PATRICK, SEEDAT, SAADIYA, VAN ZYL, LOUIS, ENGELBRECHT, JAN, MAKOTOKO, ELLEN MAKONLI, PRETORIUS, CATHARINA ELIZABETH, MOHAMED, ZAID, HORAK, ADRIAN, MABIN, THOMAS, KLUG, ERIC, BAE, JANG-HO, KIM, CHEOLHO, KIM, CHONG-JIN, KIM, DONG-SOO, KIM, YONG JIN, JOO, SEUNGJAE, HA, JONG-WON, PARK, CHUL SOO, KIM, JANG YOUNG, KIM, YOUNG-KWON, JARNERT, CHRISTINA, MOOE, THOMAS, DELLBORG, MIKAEL, TORSTENSSON, INGEMAR, ALBERTSSON, PER, JOHANSSON, LARS, AL-KHALILI, FARIS, ALMROTH, HENRIK, ANDERSSON, TOMMY, PANTEV, EMIL, TENGMARK, BENGT-OLOV, LIU, BO, RASMANIS, GUNDARS, WAHLGREN, CARL-MAGNUS, MOCCETTI, TIZIANO, PARKHOMENKO, ALEXANDER, TSELUYKO, VIRA, VOLKOV, VOLODYMYR, KOVAL, OLENA, KONONENKO, LYUDMYLA, PROKHOROV, OLEKSANDR, VDOVYCHENKO, VALERIY, BAZYLEVYCH, ANDRIY, RUDENKO, LEONID, VIZIR, VADYM, KARPENKO, OLEKSANDR, MALYNOVSKY, YAROSLAV, KOVAL, VALENTYNA, STOROZHUK, BORYS, COTTON, JAMES, VENKATARAMAN, ASOK, MORIARTY, ANDREW, CONNOLLY, DEREK, DAVEY, PATRICK, SENIOR, ROXY, BIRDI, INDERPAUL, CALVERT, JOHN, DONNELLY, PATRICK, TREVELYAN, JASPER, CARTER, JUSTIN, PEACE, AARON, AUSTIN, DAVID, KUKREJA, NEVILLE, HILTON, THOMAS, SRIVASTAVA, SUNNY, WALSH, RONALD, FIELDS, RONALD, HAKAS, JOSEPH, PORTNAY, EDWARD, GOGIA, HARINDER, SALACATA, ABRAHAM, HUNTER, JOHN J., BACHARACH, J MICHAEL, SHAMMAS, NICOLAS, SURESH, DAMODHAR, SCHNEIDER, RICKY, GURBEL, PAUL, BANERJEE, SUBHASH, GRENA, PAUL, BEDWELL, NOEL, SLOAN, STEPHEN, LUPOVITCH, STEVEN, SONI, ANAND, GIBSON, KATHLEEN, SANGRIGOLI, RENEE, MEHTA, RAJENDRA, I-HSUAN TSAI, PETER, GILLESPIE, EVE, DEMPSEY, STEPHEN, HAMROFF, GLENN, BLACK, ROBERT, LADER, ELLIS, KOSTIS, JOHN B., BITTNER, VERA, MCGUINN, WILLIAM, BRANCH, KELLEY, MALHOTRA, VINAY, MICHAELSON, STEPHEN, VACANTE, MICHAEL, MCCORMICK, MATTHEW, ARIMIE, RALUCA, CAMP, ALAN, DAGHER, GEORGE, KOSHY, N. MATHEW, THEW, STEPHEN, COSTELLO, FREDERICK, HEIMAN, MARK, CHILTON, ROBERT, MORAN, MICHAEL, ADLER, FREDRIC, COMEROTA, ANTHONY, SEIWERT, ANDREW, FRENCH, WILLIAM, SEROTA, HARVEY, HARRISON, ROBERT, BAKAEEN, FAISAL, OMER, SHUAB, CHANDRA, LOKESH, WHELAN, ALAN, BOYLE, ANDREW, ROBERTS-THOMSON, PHILIP, ROGERS, JAMES, CARROLL, PATRICK, COLQUHOUN, DAVID, SHAW, JAMES, BLOMBERY, PETER, AMERENA, JOHN, HII, CHRIS, ROYSE, ALISTAIR, SINGH, BHUWAN, SELVANAYAGAM, JOSEPH, JANSEN, SHIRLEY, LO, WINGCHI, HAMMETT, CHRISTOPHER, POULTER, ROHAN, NARASIMHAN, SESHASAYEE, WIGGERS, HENRIK, NIELSEN, HENRIK, GISLASON, GUNNAR, KOBER, LARS, HOULIND, KIM, BOENELYKKE SOERENSEN, VIBEKE, DIXEN, ULRIK, REFSGAARD, JENS, ZEUTHEN, ELISABETH, SOEGAARD, PETER, HRANAI, MARIAN, GASPAR, LUDOVIT, PELLA, DANIEL, HATALOVA, KATARINA, DROZDAKOVA, ERIKA, COMAN, IOAN, DIMULESCU, DOINA, VINEREANU, DRAGOS, CINTEZA, MIRCEA, SINESCU, CRINA, ARSENESCU, CATALINA, BENEDEK, IMRE, BOBESCU, ELENA, DOBREANU, DAN, GAITA, DAN, IANCU, ADRIAN, ILIESIU, ADRIANA, LIGHEZAN, DANIEL, PETRESCU, LUCIAN, PIRVU, OCTAVIAN, TEODORESCU, IULIA, TESLOIANU, DAN, VINTILA, MARIUS MARCIAN, CHIONCEL, OVIDIU, Connolly, Stuart J, Eikelboom, John W, Bosch, Jackie, Dagenais, Gilles, Dyal, Leanne, Lanas, Fernando, Metsarinne, Kaj, O'Donnell, Martin, Dans, Anthony L, Ha, Jong-Won, Parkhomenko, Alexandr N, Avezum, Alvaro A, Lonn, Eva, Lisheng, Liu, Torp-Pedersen, Christian, Widimsky, Petr, Maggioni, Aldo P, Felix, Camilo, Keltai, Katalin, Hori, Masatsugu, Yusoff, Khalid, Guzik, Tomasz J, Bhatt, Deepak L, Branch, Kelley R H, Cook Bruns, Nancy, Berkowitz, Scott D, Anand, Sonia S, Varigos, John D, Fox, Keith A A, and Yusuf, Salim

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2018
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43. Rivaroxaban with or without aspirin in patients with stable peripheral or carotid artery disease: an international, randomised, double-blind, placebo-controlled trial

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SALA, JORGELINA, CARTASEGNA, LUIS, VICO, MARISA, HOMINAL, MIGUEL ANGEL, HASBANI, EDUARDO, CACCAVO, ALBERTO, ZAIDMAN, CESAR, VOGEL, DANIEL, HRABAR, ADRIAN, SCHYGIEL, PABLO OMAR, CUNEO, CARLOS, LUQUEZ, HUGO, MACKINNON, IGNACIO J., AHUAD GUERRERO, RODOLFO ANDRES, COSTABEL, JUAN PABLO, BARTOLACCI, INES PALMIRA, MONTANA, OSCAR, BARBIERI, MARIA, GOMEZ VILAMAJO, OSCAR, GARCIA DURAN, RUBEN OMAR, SCHIAVI, LILIA BEATRIZ, GARRIDO, MARCELO, INGARAMO, ADRIAN, BORDONAVA, ANSELMO PAULINO, PELAGAGGE, MARIA JOSE, NOVARETTO, LEONARDO, ALBISU DI GENNERO, JUAN PABLO, IBANEZ SAGGIA, LUZ MARIA, ALVAREZ, MOIRA, VITA, NESTOR ALEJANDRO, MACIN, STELLA MARIS, DRAN, RICARDO DARIO, CARDONA, MARCELO, GUZMAN, LUIS, SARJANOVICH, RODOLFO JUAN, CUADRADO, JESUS, NANI, SEBASTIAN, LITVAK BRUNO, MARCOS RAUL, CHACON, CAROLINA, MAFFEI, LAURA ELENA, GRINFELD, DIEGO, VENSENTINI, NATALIA, MAJUL, CLAUDIO RODOLFO, LUCIARDI, HECTOR LUCAS, GONZALEZ COLASO, PATRICIA DEL CARMEN, FERRE PACORA, FREDY ANTONI, VAN DEN HEUVEL, PAUL, VERHAMME, PETER, ECTOR, BAVO, DEBONNAIRE, PHILIPPE, VAN DE BORNE, PHILIPPE, LEROY, JEAN, SCHROE, HERMAN, VRANCKX, PASCAL, ELEGEERT, IVAN, HOFFER, ETIENNE, DUJARDIN, KARL, INDIO DO BRASIL, CLARISSE, PRECOMA, DALTON, ABRANTES, JOSE ANTONIO, MANENTI, EULER, REIS, GILMAR, SARAIVA, JOSE, MAIA, LILIA, HERNANDES, MAURO, ROSSI, PAULO, ROSSI DOS SANTOS, FABIO, ZIMMERMANN, SERGIO LUIZ, RECH, RAFAEL, ABIB JR, EDUARDO, LEAES, PAULO, BOTELHO, ROBERTO, DUTRA, OSCAR, SOUZA, WEIMAR, BRAILE, MARIA, IZUKAWA, NILO, NICOLAU, JOSE CARLOS, TANAJURA, LUIZ FERNANDO, SERRANO JUNIOR, CARLOS VICENTE, MINELLI, CESAR, NASI, LUIZ ANTONIO, OLIVEIRA, LIVIA, DE CARVALHO CANTARELLI, MARCELO JOSE, TYTUS, RICHARD, PANDEY, SHEKHAR, LONN, EVA, CHA, JAMES, VIZEL, SAUL, BABAPULLE, MOHAN, LAMY, ANDRE, SAUNDERS, KEVIN, BERLINGIERI, JOSEPH, KIAII, BOB, BHARGAVA, RAKESH, MEHTA, PRAVINSAGAR, HILL, LAURIE, FELL, DAVID, LAM, ANDY, AL-QOOFI, FAISAL, BROWN, CRAIG, PETRELLA, ROBERT, RICCI, JOSEPH A, GLANZ, ANTHONY, NOISEUX, NICOLAS, BAINEY, KEVIN, MERALI, FATIMA, HEFFERNAN, MICHAEL, DELLA SIEGA, ANTHONY, DAGENAIS, GILLES R, DAGENAIS, FRANCOIS, BRULOTTE, STEEVE, NGUYEN, MICHEL, HARTLEIB, MICHAEL, GUZMAN, RANDOLPH, BOURGEOIS, RONALD, RUPKA, DENNIS, KHAYKIN, YAARIV, GOSSELIN, GILBERT, HUYNH, THAO, PILON, CLAUDE, CAMPEAU, JEAN, PICHETTE, FRANCIS, DIAZ, ARIEL, JOHNSTON, JAMES, SHUKLE, PRAVIN, HIRSCH, GREGORY, RHEAULT, PAUL, CZARNECKI, WLODZIMIERZ, ROY, ANNIE, NAWAZ, SHAH, FREMES, STEPHEN, SHUKLA, DINKAR, JANO, GABRIEL, COBOS, JORGE LEONARDO, CORBALAN, RAMON, MEDINA, MARCELO, NAHUELPAN, LEONARDO, RAFFO, CARLOS, PEREZ, LUIS, POTTHOFF, SERGIO, STOCKINS, BENJAMIN, SEPULVEDA, PABLO, PINCETTI, CHRISTIAN, VEJAR, MARGARITA, TIAN, HONGYAN, WU, XUESI, KE, YUANNAN, JIA, KAIYING, YIN, PENGFEI, WANG, ZHAOHUI, YU, LITIAN, WU, SHULIN, WU, ZONGQUI, LIU, SHAO WEN, BAI, XIAO JUAN, ZHENG, YANG, YANG, PING, YANG, YUN MEI, ZHANG, JIWEI, GE, JUNBO, CHEN, XIAO PING, LI, JUNXIA, HU, TAO HONG, ZHANG, RUIYAN, ZHENG, ZHE, CHEN, XIN, TAO, LIANG, LI, JIANPING, HUANG, WEIJIAN, FU, GUOSHENG, LI, CHUNJIAN, DONG, YUGANG, WANG, CHUNSHENG, ZHOU, XINMIN, KONG, YE, SOTOMAYOR, ARISTIDES, ACCINI MENDOZA, JOSE LUIS, CASTILLO, HENRY, URINA, MIGUEL, AROCA, GUSTAVO, PEREZ, MARITZA, MOLINA DE SALAZAR, DORA INES, SANCHEZ VALLEJO, GREGORIO, FERNANDO, MANZUR J, GARCIA, HENRY, GARCIA, LUIS HERNANDO, ARCOS, EDGAR, GOMEZ, JUAN, CUERVO MILLAN, FRANCISCO, TRUJILLO DADA, FREDY ALBERTO, VESGA, BORIS, MORENO SILGADO, GUSTAVO ADOLFO, ZIDKOVA, EVA, LUBANDA, JEAN-CLAUDE, KALETOVA, MARKETA, KRYZA, RADIM, MARCINEK, GABRIEL, RICHTER, MAREK, SPINAR, JINDRICH, MATUSKA, JIRI, TESAK, MARTIN, MOTOVSKA, ZUZANA, BRANNY, MARIAN, MALY, JIRI, MALY, MARTIN, WIENDL, MARTIN, FOLTYNOVA CAISOVA, LENKA, SLABY, JOSEF, VOJTISEK, PETR, PIRK, JAN, SPINAROVA, LENKA, BENESOVA, MIROSLAVA, CANADYOVA, JULIA, HOMZA, MIROSLAV, FLORIAN, JINDRICH, POLASEK, ROSTISLAV, COUFAL, ZDENEK, SKALNIKOVA, VLADIMIRA, BRAT, RADIM, BRTKO, MIROSLAV, JANSKY, PETR, LINDNER, JAROSLAV, MARCIAN, PAVEL, STRAKA, ZBYNEK, TRETINA, MARTIN, DUARTE, YAN CARLOS, POW CHON LONG, FREDDY, SANCHEZ, MAYRA, LOPEZ, JOSE, PERUGACHI, CARMITA, MARMOL, RICARDO, TRUJILLO, FREDDY, TERAN, PABLO, TUOMILEHTO, JAAKKO, TUOMILEHTO, HENRI, TUOMINEN, MARJA-LEENA, KANTOLA, ILKKA, STEG, GABRIEL, ABOYANS, VICTOR, LECLERCQ, FLORENCE, FERRARI, EMILE, BOCCARA, FRANCK, MESSAS, EMMANUEL, MISMETTI, PATRICK, SEVESTRE, MARIE ANTOINETTE, CAYLA, GUILLAUME, MOTREFF, PASCAL, STOERK, STEFAN, DUENGEN, HANS-DIRK, STELLBRINK, CHRISTOPH, GUEROCAK, OSMAN, KADEL, CHRISTOPH, BRAUN-DULLAEUS, RUEDIGER, JESERICH, MICHAEL, OPITZ, CHRISTIAN, VOEHRINGER, HANS-FRIEDRICH, APPEL, KARL-FRIEDRICH, WINKELMANN, BERNHARD, DORSEL, THOMAS, NIKOL, SIGRID, DARIUS, HARALD, RANFT, JURGEN, SCHELLONG, SEBASTIAN, JUNGMAIR, WOLFGANG, DAVIERWALA, PIROZE, VORPAHL, MARC, BAJNOK, LASZLO, LASZLO, ZOLTAN, NOORI, EBRAHIM, VERESS, GABOR, VERTES, ANDRAS, ZSARY, ANDRAS, KIS, ERNO, KORANYI, LASZLO, BAKAI, JUDIT, BODA, ZOLTAN, POOR, FERENC, JARAI, ZOLTAN, KEMENY, VENDEL, BARTON, JOHN, MCADAM, BRENDAN, MURPHY, ANDREW, CREAN, PETER, MAHON, NIALL, CURTIN, RONAN, MACNEILL, BRIAIN, DINNEEN, SEAN, HALABI, MAJDI, ZIMLICHMAN, REUVEN, ZELTSER, DAVID, TURGEMAN, YOAV, KLAINMAN, ELIEZER, LEWIS, BASIL, KATZ, AMOS, ATAR, SHAUL, NIKOLSKY, EUGENIA, BOSI, STEFANO, NALDI, MONICA, FAGGIANO, POMPILIO, ROBBA, DEBORA, MOS, LUCIO, SINAGRA, GIANFRANCO, COSMI, FRANCO, OLTRONA VISCONTI, LUIGI, CARMINE, DE MATTEIS, DI PASQUALE, GIUSEPPE, DI BIASE, MATTEO, MANDORLA, SARA, BERNARDINANGELI, MARINO, PICCINNI, GIOVANNI CARLO, GULIZIA, MICHELE MASSIMO, GALVANI, MARCELLO, VENTURI, FLAVIO, MOROCUTTI, GIORGIO, BALDIN, MARIA GRAZIA, OLIVIERI, CARLO, PERNA, GIAN PIERO, CIRRINCIONE, VINCENZO, KANNO, TAKAYASU, DAIDA, HIROYUKI, OZAKI, YUKIO, MIYAMOTO, NAOMASA, HIGASHIUE, SHINICHI, DOMAE, HIROSHI, HOSOKAWA, SHINOBU, KOBAYASHI, HIROO, KURAMOCHI, TAKEHIKO, FUJII, KENSHI, MIZUTOMI, KAZUAKI, SAKU, KEIJIRO, KIMURA, KAZUO, HIGUCHI, YOSHIHARU, ABE, MITSUNORI, OKUDA, HARUHITO, NODA, TOSHIYUKI, MITA, TERUAKI, HIRAYAMA, ATSUSHI, ONAKA, HARUHIKO, INOKO, MORIAKI, HIROKAMI, MITSUGU, OKUBO, MUNENORI, AKATSUKA, YUTAKA, IMAMAKI, MIZUHO, KAMIYA, HARUO, MANITA, MAMORU, HIMI, TOSHIHARU, UENO, HIDEKI, HISAMATSU, YUJI, AKO, JUNYA, NISHINO, YASUHIRO, KAWAKAMI, HIDEO, YAMADA, YUTAKA, KORETSUNE, YUKIHIRO, YAMADA, TAKAHISA, YOSHIDA, TETSURO, SHIMOMURA, HIDEKI, KINOSHITA, NORIYUKI, TAKAHASHI, AKIHIKO, YUSOFF, KHALID, WAN AHMAD, WAN AZMAN, ABU HASSAN, MUHAMMAD RADZI, KASIM, SAZZLI, ABDUL RAHIM, AIZAI AZAN, MOHD ZAMRIN, DIMON, MACHIDA, MASAHARU, HIGASHINO, YORIHIKO, UTSU, NORIAKI, NAKANO, AKIHIKO, NAKAMURA, SHIGERU, HASHIMOTO, TETSUO, ANDO, KENJI, SAKAMOTO, TOMOHIRO, PRINS, F.J., LOK, DIRK, MILHOUS, JOHANNES GERT-JAN, VIERGEVER, ERIC, WILLEMS, FRANK, SWART, HENK, ALINGS, MARCO, BREEDVELD, ROB, DE VRIES, KEES-JAN, VAN DER BORGH, ROGER, OEI, FANNY, ZOET-NUGTEREN, STIENEKE, KRAGTEN, HANS, HERRMAN, JEAN PAUL, VAN BERGEN, PAUL, GOSSELINK, MARCEL, HOEKSTRA, EDUARD, ZEGERS, ERWIN, RONNER, EELKO, DEN HARTOG, FRANK, BARTELS, GERARD, NIEROP, PETER, VAN DER ZWAAN, COEN, VAN ECK, JACOB, VAN GORSELEN, EDWIN, GROENEMEIJER, BJORN, HOOGSLAG, PIETER, DE GROOT, MARC ROBERT, LOYOLA, ALDRIN, SULIT, DENNIS JOSE, REY, NANNETTE, ABOLA, MARIA TERESA, MORALES, DANTE, PALOMARES, ELLEN, ABAT, MARC EVANS, ROGELIO, GREGORIO, CHUA, PHILIP, DEL PILAR, JOSE CARLO, ALCARAZ, JOHN DENNIS, EBO, GERALDINE, TIRADOR, LOUIE, CRUZ, JOSEFINA, ANONUEVO, JOHN, PITARGUE, ARTHUR, JANION, MARIANNA, GUZIK, TOMASZ, GAJOS, GRZEGORZ, ZABOWKA, MACIEJ, RYNKIEWICZ, ANDRZEJ, BRONCEL, MARLENA, SZUBA, ANDRZEJ, CZARNECKA, DANUTA, MAGA, PAWEL, STRAZHESKO, IRINA, VASYUK, YURY, SIZOVA, ZHANNA, POZDNYAKOV, YURY, BARBARASH, OLGA, VOEVODA, MIKHAIL, POPONINA, TATIANA, REPIN, ALEXEY, OSIPOVA, IRINA, EFREMUSHKINA, ANNA, NOVIKOVA, NINA, AVERKOV, OLEG, ZATEYSHCHIKOV, DMITRY, VERTKIN, ARKADIY, AUSHEVA, AZA, COMMERFORD, PATRICK, SEEDAT, SAADIYA, VAN ZYL, LOUIS, ENGELBRECHT, JAN, MAKOTOKO, ELLEN MAKONLI, PRETORIUS, CATHARINA ELIZABETH, MOHAMED, ZAID, HORAK, ADRIAN, MABIN, THOMAS, KLUG, ERIC, BAE, JANG-HO, KIM, CHEOLHO, KIM, CHONG-JIN, KIM, DONG-SOO, KIM, YONG JIN, JOO, SEUNGJAE, HA, JONG-WON, PARK, CHUL SOO, KIM, JANG YOUNG, KIM, YOUNG-KWON, JARNERT, CHRISTINA, MOOE, THOMAS, DELLBORG, MIKAEL, TORSTENSSON, INGEMAR, ALBERTSSON, PER, JOHANSSON, LARS, AL-KHALILI, FARIS, ALMROTH, HENRIK, ANDERSSON, TOMMY, PANTEV, EMIL, TENGMARK, BENGT-OLOV, LIU, BO, RASMANIS, GUNDARS, WAHLGREN, CARL-MAGNUS, MOCCETTI, TIZIANO, PARKHOMENKO, ALEXANDER, TSELUYKO, VIRA, VOLKOV, VOLODYMYR, KOVAL, OLENA, KONONENKO, LYUDMYLA, PROKHOROV, OLEKSANDR, VDOVYCHENKO, VALERIY, BAZYLEVYCH, ANDRIY, RUDENKO, LEONID, VIZIR, VADYM, KARPENKO, OLEKSANDR, MALYNOVSKY, YAROSLAV, KOVAL, VALENTYNA, STOROZHUK, BORYS, COTTON, JAMES, VENKATARAMAN, ASOK, MORIARTY, ANDREW, CONNOLLY, DEREK, DAVEY, PATRICK, SENIOR, ROXY, BIRDI, INDERPAUL, CALVERT, JOHN, DONNELLY, PATRICK, TREVELYAN, JASPER, CARTER, JUSTIN, PEACE, AARON, AUSTIN, DAVID, KUKREJA, NEVILLE, HILTON, THOMAS, SRIVASTAVA, SUNNY, WALSH, RONALD, FIELDS, RONALD, HAKAS, JOSEPH, PORTNAY, EDWARD, GOGIA, HARINDER, SALACATA, ABRAHAM, HUNTER, JOHN J., BACHARACH, J MICHAEL, SHAMMAS, NICOLAS, SURESH, DAMODHAR, SCHNEIDER, RICKY, GURBEL, PAUL, BANERJEE, SUBHASH, GRENA, PAUL, BEDWELL, NOEL, SLOAN, STEPHEN, LUPOVITCH, STEVEN, SONI, ANAND, GIBSON, KATHLEEN, SANGRIGOLI, RENEE, MEHTA, RAJENDRA, I-HSUAN TSAI, PETER, GILLESPIE, EVE, DEMPSEY, STEPHEN, HAMROFF, GLENN, BLACK, ROBERT, LADER, ELLIS, KOSTIS, JOHN B., BITTNER, VERA, MCGUINN, WILLIAM, BRANCH, KELLEY, MALHOTRA, VINAY, MICHAELSON, STEPHEN, VACANTE, MICHAEL, MCCORMICK, MATTHEW, ARIMIE, RALUCA, CAMP, ALAN, DAGHER, GEORGE, KOSHY, N. MATHEW, THEW, STEPHEN, COSTELLO, FREDERICK, HEIMAN, MARK, CHILTON, ROBERT, MORAN, MICHAEL, ADLER, FREDRIC, COMEROTA, ANTHONY, SEIWERT, ANDREW, FRENCH, WILLIAM, SEROTA, HARVEY, HARRISON, ROBERT, BAKAEEN, FAISAL, OMER, SHUAB, CHANDRA, LOKESH, WHELAN, ALAN, BOYLE, ANDREW, ROBERTS-THOMSON, PHILIP, ROGERS, JAMES, CARROLL, PATRICK, COLQUHOUN, DAVID, SHAW, JAMES, BLOMBERY, PETER, AMERENA, JOHN, HII, CHRIS, ROYSE, ALISTAIR, SINGH, BHUWAN, SELVANAYAGAM, JOSEPH, JANSEN, SHIRLEY, LO, WINGCHI, HAMMETT, CHRISTOPHER, POULTER, ROHAN, NARASIMHAN, SESHASAYEE, WIGGERS, HENRIK, NIELSEN, HENRIK, GISLASON, GUNNAR, KOBER, LARS, HOULIND, KIM, BOENELYKKE SOERENSEN, VIBEKE, DIXEN, ULRIK, REFSGAARD, JENS, ZEUTHEN, ELISABETH, SOEGAARD, PETER, HRANAI, MARIAN, GASPAR, LUDOVIT, PELLA, DANIEL, HATALOVA, KATARINA, DROZDAKOVA, ERIKA, COMAN, IOAN, DIMULESCU, DOINA, VINEREANU, DRAGOS, CINTEZA, MIRCEA, SINESCU, CRINA, ARSENESCU, CATALINA, BENEDEK, IMRE, BOBESCU, ELENA, DOBREANU, DAN, GAITA, DAN, IANCU, ADRIAN, ILIESIU, ADRIANA, LIGHEZAN, DANIEL, PETRESCU, LUCIAN, PIRVU, OCTAVIAN, TEODORESCU, IULIA, TESLOIANU, DAN, VINTILA, MARIUS MARCIAN, CHIONCEL, OVIDIU, Anand, Sonia S, Bosch, Jackie, Eikelboom, John W, Connolly, Stuart J, Diaz, Rafael, Widimsky, Peter, Aboyans, Victor, Alings, Marco, Kakkar, Ajay K, Keltai, Katalin, Maggioni, Aldo P, Lewis, Basil S, Störk, Stefan, Zhu, Jun, Lopez-Jaramillo, Patricio, O'Donnell, Martin, Commerford, Patrick J, Vinereanu, Dragos, Pogosova, Nana, Ryden, Lars, Fox, Keith A A, Bhatt, Deepak L, Misselwitz, Frank, Varigos, John D, Vanassche, Thomas, Avezum, Alvaro A, Chen, Edmond, Branch, Kelley, Leong, Darryl P, Bangdiwala, Shrikant I, Hart, Robert G, and Yusuf, Salim

Published
2018
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47. Characteristics of patients admitted with heart failure: Insights from the first Malaysian Heart Failure Registry.

Author

Wan Ahmad, Wan Azman, Abdul Ghapar, Abdul Kahar, Zainal Abidin, Hafisyatul Aiza, Karthikesan, Dharmaraj, Ross, Noel Thomas, S.K. Abdul Kader, Muhamad Ali, Loch, Alexander, Mahendran, Kauthaman, Ramli, Ahmad Wazi, Ong, Tiong Kiam, Mohd Amin, Nor Hanim, Lee, Chuey Yan, Che Hassan, Hamat Hamdi, Zainal Abidin, Siti Khairani, Liew, Houng Bang, Ho, Wing Sze, and Mohd Ghazi, Azmee

Subjects
HEART failure patients, HEART failure, INTENSIVE care patients, GLOMERULAR filtration rate, HOSPITAL mortality
Abstract

Aims: Heart failure (HF) is a growing health problem, yet there are limited data on patients with HF in Malaysia. The Malaysian Heart Failure (MY‐HF) Registry aims to gain insights into the epidemiology, aetiology, management, and outcome of Malaysian patients with HF and identify areas for improvement within the national HF services. Methods and results: The MY‐HF Registry is a 3‐year prospective, observational study comprising 2717 Malaysian patients admitted for acute HF. We report the description of baseline data at admission and outcomes of index hospitalization of these patients. The mean age was 60.2 ± 13.6 years, 66.8% were male, and 34.3% had de novo HF. Collectively, 55.7% of patients presented with New York Heart Association (NYHA) Class III or IV; ischaemic heart disease was the most frequent aetiology (63.2%). Most admissions (87.3%) occurred via the emergency department, with 13.7% of patients requiring intensive care, and of these, 21.8% needed intubation. The proportion of patients receiving guideline‐directed medical therapy increased at discharge (84.2% vs. 93.6%). The median length of stay (LOS) was 5 days, and in‐hospital mortality was 2.9%. Predictors of LOS and/or in‐hospital mortality were age, NYHA class, estimated glomerular filtration rate, and comorbid anaemia. LOS and in‐hospital mortality were similar regardless of ejection fraction. Conclusions: The MY‐HF Registry showed that the HF population in Malaysia is younger, predominantly male, and ischaemic‐driven and has good prospects with hospitalization for optimization of treatment. These findings suggest a need to reassess current clinical practice and guide resource allocation to improve patient outcomes. [ABSTRACT FROM AUTHOR]

Published
2024
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48. Cultivating excellence: a case study of enterprise architecture transformational journey in higher education.

Author

Amin, Fatimah Azzaharah, Hussein, Surya Sumarni, Daud, Nor Aziah, Bakar, Nur Azaliah Abu, and Wan Ahmad, Wan Azlin Zurita

Subjects
HIGHER education, MAP design, UNIVERSITIES & colleges, COMPLEX organizations, THEMATIC analysis
Abstract

An enterprise architecture (EA) is a critical framework that clarifies the complex elements of organizations, including business processes and technology. It coordinates the interaction of these elements to achieve predetermined business goals. Higher education institutions (HEIs) may become susceptible to rigidity, duplication, and intricate operations without a comprehensive EA. The primary aims of this study are to ascertain the necessary information for each EA domain: business, data, and application at Universiti Teknologi MARA (UiTM) and to design the landscape map viewpoint architecture for UiTM by utilizing the EA framework. This research uses qualitative methods, namely interviews and document analysis, to comprehensively comprehend the intricacies and prerequisites within every EA domain. Critical insights for each UiTM domain: business, data, application, and technology were uncovered through the discernments. From the thematic analysis, the frequency of issues according to the EA domain is business 13 issues, application eight issues, data seven issues, and technology seven issues. Following a thorough analysis, these findings led to the development of the landscape map viewpoint architecture diagram. In conclusion, the results of this research hold the potential to provide HEIs with invaluable knowledge for enhancing their organizational transformation via EA, thereby propelling the overall quality and efficacy of higher education systems forward. [ABSTRACT FROM AUTHOR]

Published
2024
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49. Achieving Sustainable Digital Transformation in TVET Institutions through Enterprise Architecture.

Author

Hussein, Surya Sumarni, Ramly, Norlida, Zurita Wan Ahmad, Wan Azlin, Arif Mohammed Ridzuan, Muhammad Irfan, Wesma Salehen, Puteh Melor, and Duong Dang

Subjects
DIGITAL transformation, TECHNOLOGICAL innovations, EVIDENCE gaps, SEMI-structured interviews, ARTIFICIAL intelligence
Abstract

The approach of Enterprise Architecture (EA) in Technical and Vocational Education and Training (TVET) institutions holds significant potential for the institution's digital transformation. This study explores key factors to sustain digital transformation in TVET Institutions using a Systematic Literature Review (SLR) and semistructured interviews. The SLR provides a comprehensive overview of existing literature on EA in educational contexts, focusing on its role in aligning institutional goals with industry requirements. Common themes and research gaps identified through this review set the stage for empirical investigation. Interviews with IT administrators, academia, and experts offer practical insights into the challenges and opportunities associated with EA implementation in TVET institutions, validating findings from the literature and uncovering unique contextual factors relevant to TVET institutions in Malaysia. The thematic analysis highlights several key factors, namely, management support and their involvement, a clear business vision and goal strategy, and the adoption of emerging technologies such as Artificial Intelligence and the Internet of Things towards sustainable digital transformation in TVET institutions. The findings indicate that a wellimplemented EA framework can significantly drive the digital transformation of TVET institutions, leading to better-prepared graduates and a more adaptable workforce. This study provides a detailed analysis of the key factors to sustain digital transformation in TVET Institutions and offers practical recommendations for policymakers and educational leaders. The insights gained can guide future initiatives aimed at the digital transformation of TVET institutions, contributing to a more competitive and responsive educational sector in Malaysia. [ABSTRACT FROM AUTHOR]

Published
2024
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50. Data analytics approach for short- and long-term mortality prediction following acute non-ST-elevation myocardial infarction (NSTEMI) and Unstable Angina (UA) in Asians.

Author

Kasim, Sazzli, Amir Rudin, Putri Nur Fatin, Malek, Sorayya, Aziz, Firdaus, Wan Ahmad, Wan Azman, Ibrahim, Khairul Shafiq, Muhmad Hamidi, Muhammad Hanis, Raja Shariff, Raja Ezman, Fong, Alan Yean Yip, and Song, Cheen

Subjects
NON-ST elevated myocardial infarction, MYOCARDIAL infarction, ANGINA pectoris, MACHINE learning, LOW-molecular-weight heparin, DEATH forecasting
Abstract

Background: Traditional risk assessment tools often lack accuracy when predicting the short- and long-term mortality following a non-ST-segment elevation myocardial infarction (NSTEMI) or Unstable Angina (UA) in specific population. Objective: To employ machine learning (ML) and stacked ensemble learning (EL) methods in predicting short- and long-term mortality in Asian patients diagnosed with NSTEMI/UA and to identify the associated features, subsequently evaluating these findings against established risk scores. Methods: We analyzed data from the National Cardiovascular Disease Database for Malaysia (2006–2019), representing a diverse NSTEMI/UA Asian cohort. Algorithm development utilized in-hospital records of 9,518 patients, 30-day data from 7,133 patients, and 1-year data from 7,031 patients. This study utilized 39 features, including demographic, cardiovascular risk, medication, and clinical features. In the development of the stacked EL model, four base learner algorithms were employed: eXtreme Gradient Boosting (XGB), Support Vector Machine (SVM), Naive Bayes (NB), and Random Forest (RF), with the Generalized Linear Model (GLM) serving as the meta learner. Significant features were chosen and ranked using ML feature importance with backward elimination. The predictive performance of the algorithms was assessed using the area under the curve (AUC) as a metric. Validation of the algorithms was conducted against the TIMI for NSTEMI/UA using a separate validation dataset, and the net reclassification index (NRI) was subsequently determined. Results: Using both complete and reduced features, the algorithm performance achieved an AUC ranging from 0.73 to 0.89. The top-performing ML algorithm consistently surpassed the TIMI risk score for in-hospital, 30-day, and 1-year predictions (with AUC values of 0.88, 0.88, and 0.81, respectively, all p < 0.001), while the TIMI scores registered significantly lower at 0.55, 0.54, and 0.61. This suggests the TIMI score tends to underestimate patient mortality risk. The net reclassification index (NRI) of the best ML algorithm for NSTEMI/UA patients across these periods yielded an NRI between 40–60% (p < 0.001) relative to the TIMI NSTEMI/UA risk score. Key features identified for both short- and long-term mortality included age, Killip class, heart rate, and Low-Molecular-Weight Heparin (LMWH) administration. Conclusions: In a broad multi-ethnic population, ML approaches outperformed conventional TIMI scoring in classifying patients with NSTEMI and UA. ML allows for the precise identification of unique characteristics within individual Asian populations, improving the accuracy of mortality predictions. Continuous development, testing, and validation of these ML algorithms holds the promise of enhanced risk stratification, thereby revolutionizing future management strategies and patient outcomes. [ABSTRACT FROM AUTHOR]

Published
2024
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