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1,982 results on '"Allam, Ahmed"'

1. BiasDPO: Mitigating Bias in Language Models through Direct Preference Optimization

Author

Allam, Ahmed

Subjects
Computer Science - Computation and Language
Abstract

Large Language Models (LLMs) have become pivotal in advancing natural language processing, yet their potential to perpetuate biases poses significant concerns. This paper introduces a new framework employing Direct Preference Optimization (DPO) to mitigate gender, racial, and religious biases in LLM-generated English text. By developing a loss function that favors less biased over biased completions, our approach cultivates a preference for respectful and non-discriminatory language in LLMs. We also contribute a manually designed dataset for training LLMs to recognize and correct biases. This dataset encompasses a diverse range of prompts paired with both biased and unbiased completions. Implementing this approach on the Microsoft Phi-2 model, we demonstrate substantial reductions in biased outputs as our model outperforms the baseline model on almost all bias benchmarks. Our model also achieves better performance compared to other open-source models on most benchmarks. By reducing biases in the language generated by the model, our study marks a significant step towards developing more ethical and socially responsible LLMs. We publicly release BiasDPO dataset on HuggingFace.

Published
2024

2. Semi-Supervised Generative Models for Disease Trajectories: A Case Study on Systemic Sclerosis

Author

Trottet, Cécile, Schürch, Manuel, Allam, Ahmed, Barua, Imon, Petelytska, Liubov, Launay, David, Airò, Paolo, Bečvář, Radim, Denton, Christopher, Radic, Mislav, Distler, Oliver, Hoffmann-Vold, Anna-Maria, Krauthammer, Michael, and collaborators, the EUSTAR

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

We propose a deep generative approach using latent temporal processes for modeling and holistically analyzing complex disease trajectories, with a particular focus on Systemic Sclerosis (SSc). We aim to learn temporal latent representations of the underlying generative process that explain the observed patient disease trajectories in an interpretable and comprehensive way. To enhance the interpretability of these latent temporal processes, we develop a semi-supervised approach for disentangling the latent space using established medical knowledge. By combining the generative approach with medical definitions of different characteristics of SSc, we facilitate the discovery of new aspects of the disease. We show that the learned temporal latent processes can be utilized for further data analysis and clinical hypothesis testing, including finding similar patients and clustering SSc patient trajectories into novel sub-types. Moreover, our method enables personalized online monitoring and prediction of multivariate time series with uncertainty quantification., Comment: Accepted at Machine Learning for Healthcare 2024. arXiv admin note: substantial text overlap with arXiv:2311.08149

Published
2024

3. RTL-Repo: A Benchmark for Evaluating LLMs on Large-Scale RTL Design Projects

Author

Allam, Ahmed and Shalan, Mohamed

Subjects
Computer Science - Machine Learning, Computer Science - Hardware Architecture
Abstract

Large Language Models (LLMs) have demonstrated potential in assisting with Register Transfer Level (RTL) design tasks. Nevertheless, there remains to be a significant gap in benchmarks that accurately reflect the complexity of real-world RTL projects. To address this, this paper presents RTL-Repo, a benchmark specifically designed to evaluate LLMs on large-scale RTL design projects. RTL-Repo includes a comprehensive dataset of more than 4000 Verilog code samples extracted from public GitHub repositories, with each sample providing the full context of the corresponding repository. We evaluate several state-of-the-art models on the RTL-Repo benchmark, including GPT-4, GPT-3.5, Starcoder2, alongside Verilog-specific models like VeriGen and RTLCoder, and compare their performance in generating Verilog code for complex projects. The RTL-Repo benchmark provides a valuable resource for the hardware design community to assess and compare LLMs' performance in real-world RTL design scenarios and train LLMs specifically for Verilog code generation in complex, multi-file RTL projects. RTL-Repo is open-source and publicly available on Github.

Published
2024

4. Clustering of Disease Trajectories with Explainable Machine Learning: A Case Study on Postoperative Delirium Phenotypes

Author

Zheng, Xiaochen, Schürch, Manuel, Chen, Xingyu, Komninou, Maria Angeliki, Schüpbach, Reto, Allam, Ahmed, Bartussek, Jan, and Krauthammer, Michael

Subjects
Computer Science - Machine Learning
Abstract

The identification of phenotypes within complex diseases or syndromes is a fundamental component of precision medicine, which aims to adapt healthcare to individual patient characteristics. Postoperative delirium (POD) is a complex neuropsychiatric condition with significant heterogeneity in its clinical manifestations and underlying pathophysiology. We hypothesize that POD comprises several distinct phenotypes, which cannot be directly observed in clinical practice. Identifying these phenotypes could enhance our understanding of POD pathogenesis and facilitate the development of targeted prevention and treatment strategies. In this paper, we propose an approach that combines supervised machine learning for personalized POD risk prediction with unsupervised clustering techniques to uncover potential POD phenotypes. We first demonstrate our approach using synthetic data, where we simulate patient cohorts with predefined phenotypes based on distinct sets of informative features. We aim to mimic any clinical disease with our synthetic data generation method. By training a predictive model and applying SHAP, we show that clustering patients in the SHAP feature importance space successfully recovers the true underlying phenotypes, outperforming clustering in the raw feature space. We then present a case study using real-world data from a cohort of elderly surgical patients. The results showcase the utility of our approach in uncovering clinically relevant subtypes of complex disorders like POD, paving the way for more precise and personalized treatment strategies.

Published
2024

8. Modeling Complex Disease Trajectories using Deep Generative Models with Semi-Supervised Latent Processes

Author

Trottet, Cécile, Schürch, Manuel, Allam, Ahmed, Barua, Imon, Petelytska, Liubov, Distler, Oliver, Hoffmann-Vold, Anna-Maria, Krauthammer, Michael, and collaborators, the EUSTAR

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

In this paper, we propose a deep generative time series approach using latent temporal processes for modeling and holistically analyzing complex disease trajectories. We aim to find meaningful temporal latent representations of an underlying generative process that explain the observed disease trajectories in an interpretable and comprehensive way. To enhance the interpretability of these latent temporal processes, we develop a semi-supervised approach for disentangling the latent space using established medical concepts. By combining the generative approach with medical knowledge, we leverage the ability to discover novel aspects of the disease while integrating medical concepts into the model. We show that the learned temporal latent processes can be utilized for further data analysis and clinical hypothesis testing, including finding similar patients and clustering the disease into new sub-types. Moreover, our method enables personalized online monitoring and prediction of multivariate time series including uncertainty quantification. We demonstrate the effectiveness of our approach in modeling systemic sclerosis, showcasing the potential of our machine learning model to capture complex disease trajectories and acquire new medical knowledge., Comment: Extended Abstract presented at Machine Learning for Health (ML4H) symposium 2023, December 10th, 2023, New Orleans, United States, 23 pages

Published
2023

9. Two-Stage Aggregation with Dynamic Local Attention for Irregular Time Series

Author

Chen, Xingyu, Zheng, Xiaochen, Mollaysa, Amina, Schürch, Manuel, Allam, Ahmed, and Krauthammer, Michael

Subjects
Computer Science - Machine Learning, Computer Science - Computers and Society
Abstract

Irregular multivariate time series data is characterized by varying time intervals between consecutive observations of measured variables/signals (i.e., features) and varying sampling rates (i.e., recordings/measurement) across these features. Modeling time series while taking into account these irregularities is still a challenging task for machine learning methods. Here, we introduce TADA, a Two-stageAggregation process with Dynamic local Attention to harmonize time-wise and feature-wise irregularities in multivariate time series. In the first stage, the irregular time series undergoes temporal embedding (TE) using all available features at each time step. This process preserves the contribution of each available feature and generates a fixed-dimensional representation per time step. The second stage introduces a dynamic local attention (DLA) mechanism with adaptive window sizes. DLA aggregates time recordings using feature-specific windows to harmonize irregular time intervals capturing feature-specific sampling rates. Then hierarchical MLP mixer layers process the output of DLA through multiscale patching to leverage information at various scales for the downstream tasks. TADA outperforms state-of-the-art methods on three real-world datasets, including the latest MIMIC IV dataset, and highlights its effectiveness in handling irregular multivariate time series and its potential for various real-world applications., Comment: A short version of this paper has been accepted for presentation at the Findings of Machine Learning for Health (ML4H) 2023 conference

Published
2023

10. Attention-based Multi-task Learning for Base Editor Outcome Prediction

Author

Mollaysa, Amina, Allam, Ahmed, and Krauthammer, Michael

Subjects
Quantitative Biology - Genomics, Computer Science - Machine Learning
Abstract

Human genetic diseases often arise from point mutations, emphasizing the critical need for precise genome editing techniques. Among these, base editing stands out as it allows targeted alterations at the single nucleotide level. However, its clinical application is hindered by low editing efficiency and unintended mutations, necessitating extensive trial-and-error experimentation in the laboratory. To speed up this process, we present an attention-based two-stage machine learning model that learns to predict the likelihood of all possible editing outcomes for a given genomic target sequence. We further propose a multi-task learning schema to jointly learn multiple base editors (i.e. variants) at once. Our model's predictions consistently demonstrated a strong correlation with the actual experimental results on multiple datasets and base editor variants. These results provide further validation for the models' capacity to enhance and accelerate the process of refining base editing designs., Comment: Extended Abstract presented at Machine Learning for Health (ML4H) symposium 2023, December 10th, 2023, New Orleans, United States, 15 pages. arXiv admin note: substantial text overlap with arXiv:2310.02919

Published
2023

12. Generating Personalized Insulin Treatments Strategies with Deep Conditional Generative Time Series Models

Author

Schürch, Manuel, Li, Xiang, Allam, Ahmed, Rathmes, Giulia, Mollaysa, Amina, Cavelti-Weder, Claudia, and Krauthammer, Michael

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning
Abstract

We propose a novel framework that combines deep generative time series models with decision theory for generating personalized treatment strategies. It leverages historical patient trajectory data to jointly learn the generation of realistic personalized treatment and future outcome trajectories through deep generative time series models. In particular, our framework enables the generation of novel multivariate treatment strategies tailored to the personalized patient history and trained for optimal expected future outcomes based on conditional expected utility maximization. We demonstrate our framework by generating personalized insulin treatment strategies and blood glucose predictions for hospitalized diabetes patients, showcasing the potential of our approach for generating improved personalized treatment strategies. Keywords: deep generative model, probabilistic decision support, personalized treatment generation, insulin and blood glucose prediction, Comment: Extended Abstract presented at Machine Learning for Health (ML4H) symposium 2023, December 10th, 2023, New Orleans, United States, 17 pages

Published
2023

17. SimTS: Rethinking Contrastive Representation Learning for Time Series Forecasting

Author

Zheng, Xiaochen, Chen, Xingyu, Schürch, Manuel, Mollaysa, Amina, Allam, Ahmed, and Krauthammer, Michael

Subjects
Computer Science - Machine Learning
Abstract

Contrastive learning methods have shown an impressive ability to learn meaningful representations for image or time series classification. However, these methods are less effective for time series forecasting, as optimization of instance discrimination is not directly applicable to predicting the future state from the history context. Moreover, the construction of positive and negative pairs in current technologies strongly relies on specific time series characteristics, restricting their generalization across diverse types of time series data. To address these limitations, we propose SimTS, a simple representation learning approach for improving time series forecasting by learning to predict the future from the past in the latent space. SimTS does not rely on negative pairs or specific assumptions about the characteristics of the particular time series. Our extensive experiments on several benchmark time series forecasting datasets show that SimTS achieves competitive performance compared to existing contrastive learning methods. Furthermore, we show the shortcomings of the current contrastive learning framework used for time series forecasting through a detailed ablation study. Overall, our work suggests that SimTS is a promising alternative to other contrastive learning approaches for time series forecasting., Comment: 13 pages, 6 figures

Published
2023

18. Exploratory Analysis of Federated Learning Methods with Differential Privacy on MIMIC-III

Author

Horvath, Aron N., Berchier, Matteo, Nooralahzadeh, Farhad, Allam, Ahmed, and Krauthammer, Michael

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Background: Federated learning methods offer the possibility of training machine learning models on privacy-sensitive data sets, which cannot be easily shared. Multiple regulations pose strict requirements on the storage and usage of healthcare data, leading to data being in silos (i.e. locked-in at healthcare facilities). The application of federated algorithms on these datasets could accelerate disease diagnostic, drug development, as well as improve patient care. Methods: We present an extensive evaluation of the impact of different federation and differential privacy techniques when training models on the open-source MIMIC-III dataset. We analyze a set of parameters influencing a federated model performance, namely data distribution (homogeneous and heterogeneous), communication strategies (communication rounds vs. local training epochs), federation strategies (FedAvg vs. FedProx). Furthermore, we assess and compare two differential privacy (DP) techniques during model training: a stochastic gradient descent-based differential privacy algorithm (DP-SGD), and a sparse vector differential privacy technique (DP-SVT). Results: Our experiments show that extreme data distributions across sites (imbalance either in the number of patients or the positive label ratios between sites) lead to a deterioration of model performance when trained using the FedAvg strategy. This issue is resolved when using FedProx with the use of appropriate hyperparameter tuning. Furthermore, the results show that both differential privacy techniques can reach model performances similar to those of models trained without DP, however at the expense of a large quantifiable privacy leakage. Conclusions: We evaluate empirically the benefits of two federation strategies and propose optimal strategies for the choice of parameters when using differential privacy techniques.

Published
2023

19. Unmanned Aerial Vehicle Localization Using an Advanced Technique Based on the Smooth Variable Structure Filter

Author

Demim, Fethi, Allam, Ahmed, Rouigueb, Abdenebi, Benghezal, Amar, Nemra, Abdelkrim, Bazoula, Abdelouahab, Kobzili, Elhaouari, Hamerlain, Mustapha, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, Ziani, Salim, editor, Chadli, Mohammed, editor, Bououden, Sofiane, editor, and Zelinka, Ivan, editor

Published
2024
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20. Autonomous Underwater Vehicle Localization and Map Building Based SVSF Using Smoothing Boundary Layer Width

Author

Demim, Fethi, Bouzerzour, Hamza, Rouigueb, Abdenebi, Benghezal, Amar, Allam, Ahmed, Nemra, Abdelkrim, Gauiatni, Mohamed, Kobzili, Elhaouari, Bazoula, Abdelouahab, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, Ziani, Salim, editor, Chadli, Mohammed, editor, Bououden, Sofiane, editor, and Zelinka, Ivan, editor

Published
2024
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21. Unicycle Mobile Robots Formation Tracking: Application to Dynamic Target Entrapping

Author

Allam, Ahmed, Boudiba, Ouissam, Kobzili, Elhaouari, Nemra, Abdelkrim, Tadjine, Mohamed, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, Ziani, Salim, editor, Chadli, Mohammed, editor, Bououden, Sofiane, editor, and Zelinka, Ivan, editor

Published
2024
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22. Consensus Approach Based Cooperative Guidance of a Multi-quadrotors System

Author

Allam, Ahmed, Kobzili, Elhaouari, Boudiba, Ouissam, Bouzerzour, Hamza, Nemra, Abdelkrim, Tadjine, Mohamed, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, Ziani, Salim, editor, Chadli, Mohammed, editor, Bououden, Sofiane, editor, and Zelinka, Ivan, editor

Published
2024
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23. Vision-Based Modeling and Control of Quadcopter for Uncooperative Ground Mobile Target Tracking

Author

Bouzerzour, Hamza, Allam, Ahmed, Guiatni, Mohamed, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, Ziani, Salim, editor, Chadli, Mohammed, editor, Bououden, Sofiane, editor, and Zelinka, Ivan, editor

Published
2024
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24. DDoS: A Graph Neural Network based Drug Synergy Prediction Algorithm

Author

Schwarz, Kyriakos, Pliego-Mendieta, Alicia, Mollaysa, Amina, Planas-Paz, Lara, Pauli, Chantal, Allam, Ahmed, and Krauthammer, Michael

Subjects
Quantitative Biology - Quantitative Methods, Computer Science - Machine Learning, Quantitative Biology - Biomolecules
Abstract

Drug synergy arises when the combined impact of two drugs exceeds the sum of their individual effects. While single-drug effects on cell lines are well-documented, the scarcity of data on drug synergy, considering the vast array of potential drug combinations, prompts a growing interest in computational approaches for predicting synergies in untested drug pairs. We introduce a Graph Neural Network (\textit{GNN}) based model for drug synergy prediction, which utilizes drug chemical structures and cell line gene expression data. We extract data from the largest available drug combination database (DrugComb) and generate multiple synergy scores (commonly used in the literature) to create seven datasets that serve as a reliable benchmark with high confidence. In contrast to conventional models relying on pre-computed chemical features, our GNN-based approach learns task-specific drug representations directly from the graph structure of the drugs, providing superior performance in predicting drug synergies. Our work suggests that learning task-specific drug representations and leveraging a diverse dataset is a promising approach to advancing our understanding of drug-drug interaction and synergy.

Published
2022

26. The Effect of Social Support Features and Gamification on a Web-Based Intervention for Rheumatoid Arthritis Patients: Randomized Controlled Trial

Author

Allam, Ahmed, Kostova, Zlatina, Nakamoto, Kent, and Schulz, Peter Johannes

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270
Abstract

BackgroundRheumatoid arthritis (RA) is chronic systematic disease that affects people during the most productive period of their lives. Web-based health interventions have been effective in many studies; however, there is little evidence and few studies showing the effectiveness of online social support and especially gamification on patients’ behavioral and health outcomes. ObjectiveThe aim of this study was to look into the effects of a Web-based intervention that included online social support features and gamification on physical activity, health care utilization, medication overuse, empowerment, and RA knowledge of RA patients. The effect of gamification on website use was also investigated. MethodsWe conducted a 5-arm parallel randomized controlled trial for RA patients in Ticino (Italian-speaking part of Switzerland). A total of 157 patients were recruited through brochures left with physicians and were randomly allocated to 1 of 4 experimental conditions with different types of access to online social support and gamification features and a control group that had no access to the website. Data were collected at 3 time points through questionnaires at baseline, posttest 2 months later, and at follow-up after another 2 months. Primary outcomes were physical activity, health care utilization, and medication overuse; secondary outcomes included empowerment and RA knowledge. All outcomes were self-reported. Intention-to-treat analysis was followed and multilevel linear mixed models were used to study the change of outcomes over time. ResultsThe best-fit multilevel models (growth curve models) that described the change in the primary outcomes over the course of the intervention included time and empowerment as time-variant predictors. The growth curve analyses of experimental conditions were compared to the control group. Physical activity increased over time for patients having access to social support sections plus gaming (unstandardized beta coefficient [B]=3.39, P=.02). Health care utilization showed a significant decrease for patients accessing social support features (B=–0.41, P=.01) and patients accessing both social support features and gaming (B=–0.33, P=.03). Patients who had access to either social support sections or the gaming experience of the website gained more empowerment (B=2.59, P=.03; B=2.29, P=.05; respectively). Patients who were offered a gamified experience used the website more often than the ones without gaming (t91=–2.41, P=.02; U=812, P=.02). ConclusionsThe Web-based intervention had a positive impact (more desirable outcomes) on intervention groups compared to the control group. Social support sections on the website decreased health care utilization and medication overuse and increased empowerment. Gamification alone or with social support increased physical activity and empowerment and decreased health care utilization. This study provides evidence demonstrating the potential positive effect of gamification and online social support on health and behavioral outcomes. Trial RegistrationInternational Standard Randomized Controlled Trial Number (ISRCTN): 57366516; http://www.controlled-trials. com/ISRCTN57366516 (Archived by webcite at http://www.webcitation.org/6PBvvAvvV).

Published
2015
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29. Interactive Sections of an Internet-Based Intervention Increase Empowerment of Chronic Back Pain Patients: Randomized Controlled Trial

Author

Riva, Silvia, Camerini, Anne-Linda, Allam, Ahmed, and Schulz, Peter J

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270
Abstract

BackgroundChronic back pain (CBP) represents a significant public health problem. As one of the most common causes of disability and sick leave, there is a need to develop cost-effective ways, such as Internet-based interventions, to help empower patients to manage their disease. Research has provided evidence for the effectiveness of Internet-based interventions in many fields, but it has paid little attention to the reasons why they are effective. ObjectiveThis study aims to assess the impact of interactive sections of an Internet-based self-management intervention on patient empowerment, their management of the disease, and, ultimately, health outcomes. MethodsA total of 51 patients were recruited through their health care providers and randomly assigned to either an experimental group with full access to the Internet-based intervention or a control group that was denied access to the interactive sections and knew nothing thereof. The intervention took 8 weeks. A baseline, a mid-term after 4 weeks, and a final assessment after 8 weeks measured patient empowerment, physical exercise, medication misuse, and pain burden. ResultsAll patients completed the study. Overall, the intervention had a moderate effect (F1.52=2.83, P=.03, η2=0.30, d=0.55). Compared to the control group, the availability of interactive sections significantly increased patient empowerment (midterm assessment: mean difference=+1.2, P=.03, d=0.63; final assessment: mean difference=+0.8, P=.09, d=0.44) and reduced medication misuse (midterm assessment: mean difference=−1.5, P=.04, d=0.28; final assessment: mean difference=−1.6, P=.03, d=−0.55) in the intervention group. Both the frequency of physical exercise and pain burden decreased, but to equal measures in both groups. ConclusionsResults suggest that interactive sections as part of Internet-based interventions can positively alter patients’ feelings of empowerment and help prevent medication misuse. Detrimental effects were not observed. Trial RegistrationClinicalTrials.gov: NCT02114788; http://www.clinicaltrials.gov/ct2/show/NCT02114788 (Archived by WebCite at http://www.webcitation.org/6ROXYVoPR).

Published
2014
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30. The Impact of Search Engine Selection and Sorting Criteria on Vaccination Beliefs and Attitudes: Two Experiments Manipulating Google Output

Author

Allam, Ahmed, Schulz, Peter Johannes, and Nakamoto, Kent

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270
Abstract

BackgroundDuring the past 2 decades, the Internet has evolved to become a necessity in our daily lives. The selection and sorting algorithms of search engines exert tremendous influence over the global spread of information and other communication processes. ObjectiveThis study is concerned with demonstrating the influence of selection and sorting/ranking criteria operating in search engines on users’ knowledge, beliefs, and attitudes of websites about vaccination. In particular, it is to compare the effects of search engines that deliver websites emphasizing on the pro side of vaccination with those focusing on the con side and with normal Google as a control group. MethodWe conducted 2 online experiments using manipulated search engines. A pilot study was to verify the existence of dangerous health literacy in connection with searching and using health information on the Internet by exploring the effect of 2 manipulated search engines that yielded either pro or con vaccination sites only, with a group receiving normal Google as control. A pre-post test design was used; participants were American marketing students enrolled in a study-abroad program in Lugano, Switzerland. The second experiment manipulated the search engine by applying different ratios of con versus pro vaccination webpages displayed in the search results. Participants were recruited from Amazon’s Mechanical Turk platform where it was published as a human intelligence task (HIT). ResultsBoth experiments showed knowledge highest in the group offered only pro vaccination sites (Z=–2.088, P=.03; Kruskal-Wallis H test [H5]=11.30, P=.04). They acknowledged the importance/benefits (Z=–2.326, P=.02; H5=11.34, P=.04) and effectiveness (Z=–2.230, P=.03) of vaccination more, whereas groups offered antivaccination sites only showed increased concern about effects (Z=–2.582, P=.01; H5=16.88, P=.005) and harmful health outcomes (Z=–2.200, P=.02) of vaccination. Normal Google users perceived information quality to be positive despite a small effect on knowledge and a negative effect on their beliefs and attitudes toward vaccination and willingness to recommend the information (χ25=14.1, P=.01). More exposure to antivaccination websites lowered participants’ knowledge (J=4783.5, z=−2.142, P=.03) increased their fear of side effects (J=6496, z=2.724, P=.006), and lowered their acknowledgment of benefits (J=4805, z=–2.067, P=.03). ConclusionThe selection and sorting/ranking criteria of search engines play a vital role in online health information seeking. Search engines delivering websites containing credible and evidence-based medical information impact positively Internet users seeking health information. Whereas sites retrieved by biased search engines create some opinion change in users. These effects are apparently independent of users’ site credibility and evaluation judgments. Users are affected beneficially or detrimentally but are unaware, suggesting they are not consciously perceptive of indicators that steer them toward the credible sources or away from the dangerous ones. In this sense, the online health information seeker is flying blind.

Published
2014
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42. Assessment of hepatic steatosis of potential living donor before liver transplantation using liver/spleen CT attenuation ratio compared to liver biopsy

Author

Hesham Mohamed Atef, Enas Mohamed Korayem, Naglaa Allam Ahmed, Mohamed Mohamed Houseni, Ahmed Mohamed El-Refaie, and Manal Ibrahim Gomaa

Subjects
Hepatic steatosis, Liver transplantation, CT Indices, Liver biopsy, Medical physics. Medical radiology. Nuclear medicine, R895-920
Abstract

Abstract Background Hepatic steatosis has become a major worldwide health problem, so assessment of hepatic steatosis in potential living donors is crucial prior to liver transplantation. Until now liver biopsy (LB) is considered the gold standard for diagnosing steatosis before transplantation, however steatosis assessment using imaging modalities, such as computerized tomography (CT), would be better for the donor, due its non-invasiveness. This study aimed to assess the efficacy of CT as a semiquantitaive tool for liver steatosis assessment in liver donors in comparison to liver biopsy results. Methods This cross-sectional study was carried out on 53 potential liver graft donors. All patients were subjected to non-contrast CT of the abdomen, tru-cut liver biopsy, and histopathological evaluation. The CT liver attenuation (CTL), the hepatic/splenic CT attenuation ratio (CTL/S) and difference between hepatic attenuation value and splenic attenuation values (CTL–S) were determined as well as the correlations of these indices and the findings of Liver biopsy (LB) were compared. Results According to the hepatosteatosis grades in the pathology results, the patients were divided into two groups: group A: 38 patients with grade 0 hepatosteatosis and group B: 15 patients with grade 1 and 2 hepatosteatosis. CTL, CTL–S, CTL/S, ratio of mean right hepatic lobe and splenic attenuation (RT/S) and ratio of mean left hepatic lobe and splenic attenuation (LT/S) were found to be effective in the diagnosis of hepatosteatosis grades at cutoff values ≤ 55.4, ≤ 8.7, ≤ 1.17, ≤ 1.1548 and ≤ 1.2971 with 80%, 80%, 73.3%, 86.7% and 86.7% sensitivity and 71.1%, 56.8%, 73.7% 71.1% and 50.0% specificity respectively. Also, CTL/S was found to be very effective in the diagnosis of grade II hepatosteatosis at cutoff values ≤ 0.9 with 100% sensitivity and 100% specificity. There was significant negative correlations between the CT indexes and degree of hepatosteatosis. Conclusions Compared to biopsy results, CT noninvasive indices strongly predicted the presence of hepatosteatosis, which can help in avoiding the necessity for this invasive technique.

Published
2023
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45. Access to and quality of elective care: a prospective cohort study using hernia surgery as a tracer condition in 83 countries

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Dönmez, A Eylül, Goswami, Aakansha Giri, Raheja, Aashna, Bhadani, Aayush, El Kady, Abd Elrahman Safwat, Alniemi, Abdalaziz, Awad, Abdalkarim, Aladl, Abdalla, Younis, Abdalla, Alwali, Abdallah, Khalil, Abdallah, Mansour, Abdallah, Elkhouly, Abdallah Mohamed, Rashad Temerik, Abdallah, Hasan, Abdallatef, Chied, Abdelhafid, Abdraba, Abdelhamid, Hrora, Abdelmalek, Abdelshafi, Abdelrahman, Alwali, Abdelrahman, Maraqa, Abdelrahman, Ghaffar, Abdul, Chekfa, Abdul Jabar, Al Balushi, Abdul Majeed, Quddus, Abdul, Hammadieh, Abdul Rahman, Muntaka, Abdul-Jalilu Mohammed, Asekun, Abdulazeez, Alshahrani, Abdulaziz, Jowharji, Abdulaziz, Alshukre, Abdulbaset, Khair Etareig, Abdulhafid, Abubakar Abdulkarim, Abdulhafiz, Ashiru, Abdulhafiz, Alailesh, Abdulhamid, Al-Mallah, Abdullah, Baydoun, Abdullah, Chaudhry, Abdullah, Askin, Abdullah Emre, Almutairi, Abdulmjeed, Belkhair, Abdulmunem, Abdllah, Abdulrahman, Alhammali, Abdulrahman, Basalim, Abdulrahman, Abouelnagah, Abdulrahman Galal Mohamed Mostafa, Ghazal, Abdulrahman, Mohammed, Abdulrahman, Qasem, Abdulrahman, Anadani, Abdulrazak, Jimoh, Abdulrazaq, Lawal, Abdulrazzaq Oluwagbemiga, Khaity, Abdulrhman, Lawal, Abdulwahab, Elzoubi, Abdurrahim Esseid, Sheshe, Abdurrahman Abba, Cetişli, Abdurrahman Furkan, Oyedele, Abeeb, Aljahdali, Abeer, Aboelnasr, Abeer Gamal Elsyed, Demessie, Abel, Bhargava, Abhilasha, Sonkar, Abhinav Arun, Bavaharan, Abhirame, Khan, Abid, Campbell, Abigail, Oyelekan, Abimbola Adeola Akintunde, Okunlola, Abiodun Idowu, Ojewuyi, Abiodun, Ekerin, Abiola, Saleh, Abobakr, Khairy, Abouelatta, Muthunayagam, Abraham Benjamin, Botha, Abraham, Teshome Sahilemariam, Abraham, Nawawi, Abrar, Muhammad, Abubakar Bala, Dahiru, Abubakar, Yahaya, Abubakar, Abdelmalik, Abubaker, Anuar, Abul Akmal Arif, Ousseini, Adakal, Leon -Del- Angel, Adalberto, Frankel, Adam, Gyedu, Adam, Lim, Adam, Lukáč, Adam, Peckham-Cooper, Adam, Sabbah, Adam, Varga, Adam, Sanou, Adama, Issaka, Adamu, Ningi, Adamu, Yeshitila, Addis, Yingess, Addisu, Olugbami, Adebayo, Moses, Damola-Okesiji, Adebola, Lawal, Adedayo, Ojo, Adedoyin, Hashmi, Adeel, Fakoya, Adegbolahan, Ishola, Adegoke, Abiodun, Adekunle, Ajayi, Adekunle, Mouffokes, Adel, Bonelli, Adele, Adekoya, Adeleke, Akeem Aderogba, Adeleke, Özcan, Adem, Ayoade, Ademidun, Adeyeye, Ademola, Agbaje, Ademola, Talabi, Ademola, Ademuyiwa, Adesoji, Adewumiya, Adesoji, Ademuyiwa, Adesoji O, Aderounmu, Adewale, Adisa, Adewale, Oke, Adewale, Ragab, Adham, Alzadjali, Adil, Baksi, Aditya, Jayapalan, Aditya, Md Yunos, Adnan, Pisanu, Adolfo, Salazar, Adolfo, Salinas Barragan, Adrian Alberto, Fox, Adrian, Stollberg, Adrian, Franco, Adriana, Neves, Adriana, Vricheva, Adriana, Hodonou, Adrien Montcho, Aghayeva, Afag, Liaquat, Affifa, Alsultan, Afnan, Morad, Afnan, Kalidis, Afroditi Angella, Gkouniaroudi, Agathi, Karakosta, Agathi, Lovi, Agbenya, Yevide, Agossou Barthélémy, Duro, Agustin, Rodriguez Gonzalez, Agustin, Tariq, Ahbala, Abaidalla, Ahmad, Abuhammad, Ahmad, Abuhantash, Ahmad, Mansour, Ahmad Adel, Ahmad Zaidi, Ahmad Al Hafeez, Alahmad Alismael, Ahmad, Alayed, Ahmad, Alfarwan, Ahmad, Alhaj, Ahmad, Ali, Ahmad, Alroobi, Ahmad, Alsheikh, Ahmad, Alusef, Ahmad, Alzu'Bi, Ahmad, Kayali, Ahmad Amir, Bouhuwaish, Ahmad, Doghaim, Ahmad, El-Sherbiney, Ahmad, Ghazal, Ahmad, Ramly, Ahmad Nasir, Raza, Ahmad, Qureshi, Ahmad Uzair, Arnaout, Ahmad Yamen, Abdelhamid, Ahmed, Abuthaher, Ahmed, Adel, Ahmed, Ahmayda, Ahmed, Al-Mouahhed, Ahmed, Alagha, Ahmed, Albalawi, Ahmed, Albhaisi, Ahmed, Alrifaee, Ahmed, Azam, Ahmed, Barakat, Ahmed, Al-Hajjaj, Ahmed Basim Abed, Dawoud, Ahmed, El Shamarka, Ahmed, El-Bastwesy, Ahmed, Elghrieb, Ahmed, Elkhalifa, Ahmed, Elmosalamy, Ahmed, Elshazli Mahmoud, Ahmed, Montaser, Ahmed G., Gbeadese, Ahmed, Haty, Ahmed, K. 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Santos, Catarina, Rodrigues, Catarina S., Allmer, Caterina, Bradshaw, Catherine Jane, Rukundo, Cédric, Seneza, Celestin, Raphael, Celine, Guldogan, Cem Emir, Bilir, Cemal, Hacıalioğlu, Cemal, Ersavas, Cenk, Özkan, Cenk, Huaroto Landeo, Cesar Fernando, Ginesta, Cesar, Infante, César, Pastás, César, Ciubotaru, Cezar, Narayan, Chanchal, Mahakalkar, Chandrashekhar, Floros, Charalampos, Demiri, Charikleia, Chatzinikolaou, Charito, Dempers, Charl, Ansah Larbi, Charles, Banka, Charles, Cini, Charles, Dally, Charles, Doku, Charles, Ntow-Boahen, Charles Kwaku, Nicholson Jr., Charles, Cornwell, Charlotte, Stennard, Charlotte, Lionel, Charre, Azmat, Chaudhary Ehtsham, Nyampinga, Chelsea, Tandup, Cherring, Agarwal, Chetan, Parmar, Chetan, Pun, Chetna, Un, Chi Wen, Baars, Chiara Anne, Celano, Chiara, Fumagalli, Chiara, Marafante, Chiara, Zandonella, Chiara, Nwanmah, Chibueze, Osuigwe, Chidimma, Nwokoro, Chigbundu, Nwosu, Chikaodili, Nwosu, Chimdiebele Daisy, Lim, Chin Kent, Ónyeka, Chinonso, Uche, Chisom, Jamieson-Grigg, Chloe, Ong, Chon Sum, Alphonse, Chris, Bode, Chris, Varghese, Chris, Carrillo Sarango, Christian, Esteo Verdu, Christian, Krautz, Christian, Mosca, Christian, Nimbona, Christian, Nikolaou, Christina, Wiesner, Christina, Kloppers, Christo, Kourouniotis, Christoforos, Seiler, Christoph, Knee, Christopha, Mpirimbanyi, Christophe, Aboah, Christopher, Amah, Christopher, Cheong, Christopher, Harmston, Christopher, Leung, Christopher, Seet, Christopher, Smart, Christopher, Chouliaras, Christos, Damaskos, Christos, Doudakmanis, Christos, Kaselas, Christos, Limas, Christos, Ntagkas, Christos, Papazacharias, Christos, Stefanou, Christos, Thrasyvoulou, Christos, Tsalikidis, Christos, Karagianni, Chrysanthi, Kolla, Chrysoula-Christina, Ugwunne, Chuka, Ilo, Chukwudi, Nwegbu, Chukwuemeka, Onwuzu, Chukwuka, Weadick, Ciara, De Martino, Ciro, Donohoe, Claire, Gutierrez De La Rosa, Clara Elena, Florián Villa, Clara Marcela, Petcu, Clara Teodora, Cecconi, Claudia, Banal, Claudine, Brandi, Claudio, Hossu, Claudiu, Onuoha, Clement, Osime, Clement, Shelton, Clifford, Fuentes Orozco, Clotilde, Adumah, Collins, Dina, Constantin, Nastos, Constantinos, Aguero, Coral, Markin, Coretta, Hoogerboord, Cornelis, Da Lio, Corrado, Saviello, Cosimo, Bezede, Cosmin, Distefano, Costanza, Elliot-Wilson, Courtney, Bokossa, Covalic, Lupercio Figueroa, Cristian Israel, Riboni, Cristiana, Meola, Cristiano, Costa, Cristina, Gas, Cristina, Larotonda, Cristina, Soddu, Cristina, Vacca, Cristina, Vera Mansilla, Cristina, Li, Crystal, Agbonrofo, Cynthia, Akli-Nartey, Cynthia, Molewa, Cynthia, Nelly Rosine, Cyuzuzo Ngwije, Rega, D., Koike, Daisuke, Wickramasinghe, Dakshitha, Clarke, Damian, Fry, Damian, Hannedige, Damsadie, Powell, Dan-Beck, Kasasbeh, Dana, Samardali, Dana, Sharl Ajami, Dani, Badran, Dania, Adjei, Daniel, Muhie, Daniel Ahmed, Pérez Muñoz, Daniel Alejandro, Alifoe, Daniel, Aparicio-López, Daniel, Baños Méndez, Daniel, Cristian, Daniel, Eaton, Daniel, Gero, Daniel, Makary, Daniel, Montwedi, Daniel, Muñoz, Daniel, Patterson, Daniel, Reim, Daniel, Agbley, Daniel Yao Dodzie, Patiño García, Daniela Cristina, Di Pietrantonio, Daniela, Pais, Daniela, Rosado, Daniela, Tavares, Daniela, Alberti, Daniele, Fusario, Daniele, Massaro, Daniele, Paglione, Daniele, Pironi, Daniele, Kurochka, Daniil, Centonze, Danilo Corrado, Hackner, Danilo, Herrera, Danilo, Amangaliyev, Daniyar, Silva-Cano, Danny Wladimir, Chaudhry, Daoud, Palmisano, Dario, Ribero, Dario, Vukosav, Dario, Misca, Darius, Lowen, Darren, Rattray, Darren, Walia, Darshanjit Singh, Maan, Darvesh, Muyenzi, Daryle, Verdi, Daunia, Bardsley, David, Reyes Rodríguez, David Estuardo, Fra Corral, David, Garcia López De Goicoechea, David, Gavit, David, Gómez, David, Idowu, David, Konkin, David, Labadah, David Kplorla Kekeli, Liu, David, Merlini, David, Mitchell, David, Moro-Valdezate, David, Osifo, David, Proud, David, Ridder, David, Gobatti, Davide, Muschitiello, Davide, Pertile, Davide, Zulian, Davide, Mengiste, Dawit, Mikami, Dean, Mohanty, Debajyoti, Chatterjee, Debarghya, Komolafe, Deborah Olaitan, Scotto Di Carlo, Deborah, Wright, Deborah, Rahme, Deema, Talreja, Deep Parkash, Jain, Deepak, Rajput, Deepak, Singh, Deepak, Anbu, Deepika, Ibrahim, Dena, Phom, Denchu, Hili, Denise, Osei, Dennis Broni, Daary, Dennis, Kyeremeh, Dennis, Shen, Dennis, Uymaz, Derya Salim, Cianflocca, Desiree, Ashitey, Desmond, Evripidou, Despo, Papageorgouli, Despoina, Kaushal, Devesh, Abeysirigunawardana, Dewmi, Dugar, Dharmendra, Mallik, Dhiraj, Ghosh, Dhruva, Houmran, Diaa, Abdulrahman, Diala, Pantoja Pachajoa, Diana Alejandra, Cardona Gomez, Diana Catalina, Fenner, Diana, Gonzalez, Diana Samantha, Siriwardena, Diana, Garcés Palacios, Diana Sofía, Stoian, Diana, Zuikyte, Diana, Sosa Méndez, Dianne Alitzá, Wickramarathna, Dickson, Mutter, Didier, Palma Portillo, Diego Andrés, Chavez Fernandez, Diego, Córdova García, Diego, Enjuto, Diego, Giulitti, Diego, Sasia, Diego, Visconti, Diego, Yiğit, Dilara, George, Dilip, Rabaia, Dima, Georgiev, Dimitar, Hadzhiev, Dimitar, Alexandrou, Dimitrios, Balalis, Dimitrios, Giakoustidis, Dimitrios, Manatakis, Dimitrios K., Korkolis, Dimitrios, Lytras, Dimitrios, Massaras, Dimitrios, Moris, Dimitrios, Politis, Dimitrios, Sampanis, Dimitrios, Schizas, Dimitrios, Tsiardas, Dimitrios, Vardakostas, Dimitrios, Kotsaris, Dimitris, Khattab, Dina Hesham, M. Awad, Dina, Alrokh, Dina Sami, Schippers, Dina, Waleed, Dina, Salinovic, Dinko, Gonçalves Múrias Gomes, Diogo Miguel, Silva, Diogo, Vaz Acosta, Diogo, Morton, Dion, Panagopoulos, Dionisios, Kelgiorgi, Dionysia, Dellaportas, Dionysios, Prevezanos, Dionysios, Joshi, Dipti, Ozal, Direm, Yigit, Direnc, Chimkaomasiri, Divine, Jabbar, Diya, Nepogodiev, Dmitri, Belekar, Dnyanesh, Abdalaziz, Doaa, Abunemer, Doaa, Attia, Doaa, Elkafrawy, Doaa, Dimitrov, Dobromir, Pignatelli, Domenica, Merlicco, Domenico, Soriero, Domenico, Zerbo, Domenico, Atouguia, Domingas, Fairbrass, Dominic, T. Enti, Donald, Venskutonis, Donatas, Rukavina, Dora, Caruso, Dorena, Herappe, Dorihela, Lee, Dorinda Norbetha, Ofosuhene, Doris, Hajela, Apoorva, Badwan, Basem, Kulkarni, Datta, Dhawan, Deep, Dhillon, Jeevanjit, Mehmood, Manzer, Mahmood, Namrah, Nikam, Narendra, Ramchandani, Radhakrishna, Thind, Ravinder Singh, Riazhussain, D Hafiz, Maqsood, Saliha, Alalawi, Yousef, Tyagi, Harshdeep, Kshirsagar, Shivani, Ahmed, Hisham, Juan Carlos, Luna Cydejko, Kone, Dramane, Omar, Duaa, Alves, Duarte Gil, Subasinghe, Duminda, Ommi, Durgesh, Joshiba, Durshney, Chakarov, Dzhevdet, Arkoh, Ebenezer Ameyaw, Frimpong-Manso, Ebenezer, Ada, Ece, Lincango, Eddy P., Lozada Hernandez, Edgard Efren, Hodžić, Edin, Di Marco, Edith, Baccalini, Edoardo Andrea, Baldini, Edoardo, González Marín, Eduardo, Gutierrez, Eduardo, Sepúlveda, Eduardo, Dainius, Edvinas, Zhang, Edward, Yenli, Edwin Mwintiereh Ta-Ang, Antoniou, Efstathios, Kapasakis, Efstathios, Baili, Efstratia, Poulios, Efthymios, Kubiliute, Egle, Osaze, Ehioghae, Afeikhena, Ehis, Elshennawy, Eiad, Athanasopoulou, Eirini, Synekidou, Eirini, Ekaladze, Eka, Tampaki, Ekaterini Christina, Adam, Elaf, Adel Hamdoun Aziz, Elaf, Yousuf, Elamin, Gialamas, Eleftherios, Spartalis, Eleftherios, Bedzhanyan, Elen, Toma, Elena Adelina, Daukšaitė, Elena, Desiato, Elena, Dvivedi, Elena, Hadzhieva, Elena, Martí Cuñat, Elena, Montalbán Martínez, Elena, Montanari, Elena, Signaroli, Elenasofia, Efremidou, Eleni, Gavriil, Eleni, Karakeke, Eleni, Mavrodimitraki, Eleni, Papamattheou, Eleni, Jose, Elenza, Cianci, Eleonora, Ciccioli, Eleonora, Daleva, Eleonora, Locci, Eleonora, Lori, Eleonora, Silanos, Eleonora, Ayala, Elí, Bankart, Eliana, Brolese, Eliane, Efstathiou, Elias, Salloum, Elias, Colak, Elif, Terzi, Elif Didem, Ametefe, Elikem, Cagigal Ortega, Elima Pilar, Donnarumma, Elio, Mina, Elisa, Reitano, Elisa, Wallner, Elisabeth, Moggia, Elisabetta, Betz, Elise, Kontis, Elissaios, Anestiadou, Elissavet, Gimson, Eliza, Lun, Elizabeth, Fraser, Elizabeth, Li, Elizabeth, Torres De Anda, Elizabeth Magaly, Murphy, Elizabeth Mary Ann, O'Connell, Elizabeth, Redondo, Elizabeth, Lun, Elizabeth Weng Yan, Francis, Ella, Haiden, Elmar, Hamed, Elmonzir, Alkhalifa, Elmustafa, Dexter, Eloise, Daketsey, Elorm, Setsoafia, Elorm, Mohamed, Elsagad, Mohammed, Elsamoual, Hegab, Elsayed, Ay, Elvin, Adinolfi, Elvira, Mbanzabugabo, Elvis, Chin, Elynn, Ahmed, Emad Ali, Younes, Eman, Basile, Emanuela, Gessa, Emanuela, Pontecorvi, Emanuele, Romairone, Emanuele, Hatangimana, Eméry, Ndizeye, Emile, Ciofic, Emilia, Dijan, Emilio, López-Negrete Cueto, Emilio, Baker, Emily, Reid, Emily, Schmidt, Emily, Alić, Emir, Bičakčić, Emir, Agbowada, Emma, Colton, Emma, Downing, Emma, Haege, Emma, O'Neill, Emma, Roussos, Emmanouel, Nachelleh, Emmanuel Aadereyir, Adebunmi, Emmanuel, Obiesie, Emmanuel Ahuizechukwu, Akakpo, Emmanuel Bismark, Konlan, Emmanuel Dammigu Francis, Nwangwu, Emmanuel Ifeanyi, Kafui Ayodeji, Emmanuel, Ofori, Emmanuel Owusu, Spanoudakis, Emmanuel, Abate, Emmanuele, Balik, Emre, Bozdağ, Emre, Bozkurt, Emre, Karabulut, Emre Cagri, Ozoran, Emre, Tunçcan, Emre, Tuzuner, Emre, Halilović, Emsad, Alabed, Enaam, Zidan, Enas, Hodžić, Enes, Aybar, Engin, Yildirim, Engin Okan, Wong, Enoch, Yhoshu, Enono, Bisogno, Enrica, Andolfi, Enrico, Ferri, Enrico, Potenza, Enrico, Spalice, Enrico, Volpin, Enrico, González, Enrique, Downes, Eoin, Gallo, Erdachew, Kamer, Erdinc, Naidoo, Eressa, Erginöz, Ergin, Bara, Eric, Irungu, Eric, García-Villegas, Eric Yusif, Mazzotta, Erica, Monati, Erica, Chan, Erick, Gómez Mejía, Erika, Dylja, Eris, Somuncu, Erkan, Yavuz, Erkan, Begunić, Ernes, Margelis, Ernest, Osei, Ernest Owusu, Saitoglou, Ertzan, Agastra, Ervis, Mazumdar, Eshan, Soyer Güldoğan, Esra, Abu Siam, Esra'A, Abdelbaset, Esraa, Abdu, Esraa, Adel Mahmod Sultan, Esraa, Nubi Mohamed, Esraa Gamal, Abuobaida Banaga Hag El Tayeb, Essam Eldien, Ali, Essraa, Page-Taylor, Estelle, Marra, Ester, Garcia Santos, Esther Pilar, Elsheikh, Ethar, Unal, Ethem, Tan, Eugene, De Leo, Eugenio, Borges, Eva, Gáspár, Éva, Ruiz-Daum, Eva, Apostolopoulos, Evaggelos, Samara, Evangelia, Barkolias, Evangelos, Fradelos, Evangelos, Hopping, Eve, Romo Ascencio, Evelia Veronica, Sosa Ferreira, Ever David, Shestakov, Evgenii, Tyurina, Evgenya, Hristova, Evguenia, Efthymiou, Evripidis, Tokidis, Evripidis, Harrison, Ewen, Dontoh, Ewurama Akroma, Mansour, Eyad, Yeshialem, Eyosiyas, Dabbagh, Ezeddin, Amer, Ezzeldin, Manasci, Fabiana, Gomes, Fabio, Medas, Fabio, d'Acapito, Fabrizio, Vassallo, Fabrizio, Haddad, Fadi, Elhabishi, Fadwa, Akhtar, Fahad, Tauheed, Fahad, Al Abbood, Fahd, Gareb, Fahed, Ghazali, Fairuz, Alnazawi, Faisal, Ondago, Faith, Rasoaherinomenjanahary, Fanjandrainy, Wiese, Fanny, González Ponce, Fanny Yesenia, Georgiades, Fanourios, Ali, Farah, Galley, Fareeda, Huda, Farhanul, Amir, Farhat Naaz, Ashraf, Fariha, Alfarsi, Faris, Ayasra, Faris, Ali, Farman, Mahmoud, Faten, Aliskander, Fater, Tahir Lwdie, Fathea, Buyuker, Fatih, Aguilar Del Castillo, Fátima, Mohammed Daoud, Fatima Ahmed, Alowjaly, Fatima, Oni, Fatima, Rauf, Fatima, Elkhafeefi, Fatimah, Hourri, Fatine, Monib, Fatma A., Alshreef, Fatma, Benghalbon, Fatma, Ntirenganya, Faustin, Ashraf, Fauziyah, Chahrour, Fayez, Ahmad, Fazal, Parray, Fazlul Qadir, Philips, Febi, Campus, Federica, Di Marco, Federica, Ferraina, Federica, Frongia, Federica, Maffeis, Federica, Natali, Federica, Pederiva, Federica, Scolari, Federica, Tofani, Federica, Marin, Federico Alberto Niccolò, Biolchini, Federico, Cappellacci, Federico, Corronca, Federico, Mazzola, Federico, Suldrup, Federico, Nyarko, Felicia, Casas J, Felipe, Zapata, Felipe, Aigner, Felix, Alakaloko, Felix, Amoako, Felix, Camacho, Felix, Hammett, Felix, Kuubetersob B. N, Felix, Alahmad, Feras, Alnimri, Feras, Haddad, Feras, Gool, Ferhana, Castro, Fernanda, Diaz, Fernanda, Lucero, Fernanda, Vovola, Fernanda, Mahnic, Fernando, Mercado Sanchez, Fernando, Prieto La Noire, Fernando, Soggiu, Fiammetta, Tsige, Fikreab, Brzeszczyński, Filip, Rudisch, Filip, Valente Costa Pinto, Filipa, Ramalho De Almeida, Filipe, Banchini, Filippo, Palomino Escalante, Fiorella, Fadzlullah, Fitri, Terefe, Fitsum, Sulo, Fjorenta, Ciccarone, Flavia, Tirelli, Flavio, Krämer, Florian, Grama, Florin, Spanos, Foivos, Solimene, Folco, Akinwande, Foluso, Osman, Forzia, Stefou, Foteini, Kyramargios, Fotios, Stavratis, Fotios, Alassani, Fousseni, Ascari, Francesca, Cammelli, Francesca, D'Agostino, Francesca, Tropeano, Francesca Paola, Peters, Francesca, Ré, Francesca, Brucchi, Francesco, Casti, Francesco, Coratti, Francesco, Ferrara, Francesco, Floris, Francesco, Izzo, Francesco, Maffei, Francesco, Palmieri, Francesco, Pata, Francesco, Porcelli, Francesco, Selvaggi, Francesco, Velluti, Francesco, Mulita, Francesk, Bello, Francis, Caiquo, Francis, Dossou, Francis, Eshun, Francis Junior, Opoku Twene, Francis, Tierenye, Francis, Quayson, Francis William, Romo Rosales, Francisco David, Ibañez Ortiz, Francisco, León Frutos, Francisco Javier, Redondo Calvo, Francisco Javier, Silva Rivera, Francisco Javier, Barbosa Camacho, Francisco José, Bujalance Cabrera, Francisco Manuel, Ruescas, Francisco, Irakiza, Franck Fanon, Corvatta, Franco, Pascual, Franco Raúl, Roviello, Franco, Roodt, Francois, Gyamfi, Frank Enoch, Olaniru, Frank, Owusu, Frank, Bader, Franz G., Anzinger, Franz-Xaver, Agbodo, Fred David Anku, Ris, Frederic, Agbemafoh, Frederick Mawuli, Afonso, Frederico, Nazareth, Frederico, Berrevoet, Frederik, Hounde, Fréjus, Demiral, Furkan, Hassooni, Furqan Ridha Hassooni, Kafa, Gabi, Cárdenas Rivera, Gabriel, Ntwari, Gabriel, Szydlo Shein, Gabriel, Ambriz González, Gabriela, Nita, Gabriela Elisa, Bellio, Gabriele, Moitzi, Gabriele, Urdang, Gabriella, Marom, Gad, Gallo, Gaetano, Luglio, Gaetano, Poillucci, Gaetano, Singh, Gagandeep, Saletta, Gaia, Abouelnagah, Galal, Yarovenko, Galina, Kazobinka, Gallina, Ahmed, Gamal Mutwakil Gamal, Rahman, Ganiyu Adebisi, Smith, Garett, Pillai, Gargi, Saxena, Garima, Gbessi, Gaspard, Nkunguzi, Gaston, Jalal, Gaurav, Chillarge, Gauri, Singh, Gautam, Mevognon, Gedeon, Getachew, Gedion, George, Gejoe, De La Peña González, Gema, Martínez Izquierdo, Gema, Grande, Gennaro, Birikorang, Geoffrey, Weber, Georg F., Singer, Georg, Ansong, George, Bashour, George, Brown, George Darko, Hneino, George, Kavalieratos, George, Kenchadze, George, Nagy, George, Pappas Gogos, George, Skroubis, George, Waiyaki, George, Cox, Georgia, Zeringa, Georgia, De Wee, Georgin, Hart, Georgina, Bekakos, Georgios, Fragulidis, Georgios, Kapogiannatos, Georgios, Karakaidos, Georgios, Kokkinos, Georgios, Kostoulas, Georgios, Koukoulis, Georgios, Peros, Georgios, Rados, Georgios, Tsoulfas, Georgios, Verras, Georgios-Ioannis, Berger, Geraint, Adoro, Gerard Ayuka, Chamoso Mialdea, Gerardo, Palmieri, Gerardo, Procaccini, Gerardo, Steenkamp, Gerhard, Viscido, Germán Rodrigo, Dajti, Gerti, Alemayehu, Getachew, Seyfu, Gezachew, Birqeeq, Ghada, Alsheikh, Ghadeer, Alrayyes, Ghaith, El Hunjul, Ghalib Nashaat, Zaza, Ghayath, Matroud, Ghazlah, Alhadwah, Ghena, Maltinti, Gherardo, Awais, Ghina, Shamsi, Ghina, Calini, Giacomo, Carganico, Giacomo, Zanus, Giacomo, Canu, Gian Luigi, Mendiola, Gian, Ibero Casadiego, Giana Pamela, Cocorullo, Gianfranco, Baronio, Gianluca, Pellino, Gianluca, Zanni, Gianluca, Poto, Gianmario Edoardo, Gravante, Gianpiero, Aduroja, Gideon, Almogy, Gidon, Bond-Smith, Giles, Natchagande, Gilles, Bechara, Gio, Brachini, Gioia, Riccioli, Giordana, Ercolani, Giorgio, Farris, Giorgio, Giraudo, Giorgio, Mammolo, Giovanna, Pavone, Giovanna, Sgarzini, Giovanna, Aprea, Giovanni, Fonsi, Giovanni Battista, Boroni, Giovanni, De Angeli, Giovanni, De Palma, Giovanni Domenico, Emiliani, Giovanni, Grava, Giovanni, Guercio, Giovanni, De Carlo, Giulia, Del Corpo, Giulia, Duranti, Giulia, Galatioto, Giulia, Grassi, Giulia, Graziano, Giulia, Marini, Giulia Maria Francesca, Munzi, Giulia, Tarantino, Giulia, Borda-Luque, Giuliano, Pellicano', Giuseppe Antonino, Canonico, Giuseppe, Carollo, Giuseppe, Ietto, Giuseppe, Palomba, Giuseppe, Pascarella, Giuseppe, Salamone, Giuseppe, Sica, Giuseppe, Sorrentino, Giuseppe, Orlando, Giuseppina, Pignata, Giusto, Pisarevi, Givi, Akcakoca, Gizem, Owusu, Godfred, Yohanna Abrak, Godwin, Kurtoglu, Gokalp Kagan, Kurtoglugk, Gokalp Kagan, Alıcı, Gökhan, Kurtoglu, Goktug Mert, Delgado Hernandez, Gonzalo, Augustin, Goran, Bhat, Gowhar Aziz, Sundaram Venkatesan, Gowtham, Chang, Grace, Eke, Grace, Lee, Grace, Massarra, Grace, Travaglini, Grazia, Scialandrone, Grazisa, Stavrou, Gregor A., Di Franco, Gregorio, Charalambous, Gregory, Christodoulidis, Gregory, Kouraklis, Gregory, Goodwin, Gregory Richard, Devidze, Grigol, Fialho, Guilherme, Santos, Guilherme, Cabezudo, Guillermo, García-Santos, Guillermo, Pou, Guillermo, Romero Reyna, Guillermo, Yanowsky-Reyes, Guillermo, Ozyuksel, Gul, Karadeniz Cakmak, Guldeniz, Kucuk, Gultekin Ozan, Bulbuloglu, Guney Cem, Sarp, Guray, Kaur, Gurleen, Nibogora, Guy Darcy, Hamdan, Gymar, Ogundipe, Habeeb, Yunes, Habib, Ayesh, Hadeel, Al Atassi, Hadi, Jalilehvand, Hadis, Masalma, Hafez, Jafarkhan, Hafis, Shahid, Hafiz Huzaifa, Younus, Hafsa, Al-Derume, Hail, Shames, Haitam, Mimouni, Haithem Ahmed, Aldare, Hajar, Odah Bashi, Hajar Omar, Abusnina, Hajer, Altounsi, Hajir, Qayum, Hajra, Baysal, Hakan, Bolukbasi, Hakan, Yigitbas, Hakan, Alkhatib, Hala, Afzal, Haleema, Mftah, Halima, Oyinlola, Halimah, Tümer, Haluk, Dolo, Hamadoun, Braham, Hamdi, Ozsahin, Hamdi, Abu-Arish, Hamdoon, Al-Aamri, Hamed, Fadlalmola, Hammad Ali, Abu Obead, Hamza, Al-Fahel, Hamza, Al-Zoubi, Hamza, Asaad, Hamza, Ejaz, Hamza, Jaber, Hamza, Bhatti, Hamza Waqar, Mohammadi Sardoo, Hamzeh, Farhat, Hana, Idheiraj, Hanadi, Zwaraa, Hanady, Al Sohabi, Hanan, Basharat, Hanan, Said, Hanan, Al-Abdallat, Haneen, Foad, Haneen, Daaboul, Hanen, Al Qadhi, Hani, Elhallaq, Hani, Alhusaini, Hanin, Lidbetter, Hannah, Reilly, Hannah, Moya- Ambriz, Hannia Betzabeth, Lederhuber, Hans, Fehrer, Hans-Jörg, Xiao, Haotian, Iswariah, Harish, Kaur, Harmeena, Cheema, Harnoor Kaur, Foda, Haroun, Chauhan, Harpreet, Kaur, Harpreet, Sagar, Harry, Unwin, Harry, Kaur, Harsharan, Panga, Harshavardhan, Gupta, Harshit, Prabhakar, Harshit Kumar, Anand, Harshitha, Pahwa, Harvinder Singh, Abbadi, Hasan, Amoudi, Hasan, Zaini, Hasan Hadi Zaini, Škiljo, Hasan, Miyasika, Hasangi, Altabbaa, Hashem, Haj Freej, Hasn, Abdelazim, Hassan, Habiba, Hassan, Kamal, Hassan, Sabir, Hassan, Zaigham, Hassan, Pehlivan, Hatice, Telci, Hatice, Abuobaida, Hayat, Tetley, Heather, Taher, Heba, Aguado López, Héctor, Askarpour, Hedyeh, Sherif Farouk Ahmed Hassan, Heidi, Devesa, Helena, Arbogast, Helmut, Nuñez Del Barrio, Henar, Abiyere, Henry, Delacave, Henry, Rice, Henry E., Pleass, Henry, Bradly, Hera, Islam, Heraa, Lule, Herman, Abozied, Hesham, Le Roux, Hettie, Hamdar, Hiba, Yaghmour, Hiba, Bileid Bakeer, Hibah, Al Miskry, Hilal, Ulman, Hilmican, Abdoun, Hind, Essalim, Hind, Yonekura, Hiroshi, Kato, Hiroyuki, Amin-Tai, Hizami, Kynaston, Holly, Pérez Baca, Honorio De Jesús, Irfan Khan, Hoor, Amir, Hosam, Elghadban, Hosam, Elfeki, Hossam, Wain, Howard, Adjei, Hubert, Dąbrowski, Hubert, Novák, Hubert, Alzuhd, Huda, Capote, Hugo, Gomez-Fernandez, Hugo, Stark, Hugo, Willy-Chidire, Hulda, Aouagbe Behanzin, Hulrich, Oneizah, Humam M.A., Dalati, Husam, Ebied, Husam, Mahajna, Husam, Ahmad, Husnain, Altawati, Husni, Adi, Hussam, Albalawi, Hussam, Alfakeer, Hussein, Embarek, Hussein, Ibrahim, Hussein, Malkawi, Hussein, Ekwuazi, Hyginus, Psilopatis, Iason, Wasiu, Ibitowa, Kereet, Ibraheem M., Suleiman, Ibrahem, Fortune, Ibrahim, Aburumman, Ibrahim, Adel, Ibrahim, Adham, Ibrahim, Ali, Ibrahim, Arnaout, Ibrahim, Çakır, Ibrahim, Suleiman, Ibrahim Eneye, Fagiri, Ibrahim, Hajmohammed, Ibrahim, 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Bertrand, Martin, Dum, Martin, Kajic, Martin, Karamanliev, Martin, Kyereh, Martin, Lucchini, Martin, Schön, Martin, Morna, Martin Tangnaa, Binda, Martina, Ceolin, Martina, Martorana, Martina, Zambon, Martina, Shams, Marvi, Alansary, Marwa, Alnuwayli, Marwa, Khalifa, Marwa, Bahnacy, Marwan, Iqreewi, Marway, Nnabagulanyi, Mary, Saleem, Marya, Alhimyar, Maryam, Kamal, Maryam, Masood, Maryam, Razaq, Maryam, Siddique, Maryam, Lazo Ramírez, Maryzela, Edeeb, Masaoud, Solkar, Maseera, Adetoyi, Mashood, Adeniyi, Caccetta, Massimiliano, Casati, Massimiliano, Veroux, Massimiliano, Dugo, Massimo, Bevanda, Mateo, Santillan, Mateo, Mietła, Mateusz, Pryt, Mateusz, Muir, Mathew, Bei, Matilda, McGuinness, Matt, Aulicino, Matteo, Castrovillari, Matteo, Filardo, Matteo, Palmeri, Matteo, Pisano, Matteo, Rottoli, Matteo, Gjoka, Mattheus, Binks, Matthew, Corcos, Matthew, Kwon, Matthew, Maybury, Matthew, Sammut, Matthew, Tutton, Matthew, Watson, Matthew, Biebl, Matthias, Janda, Matthias, Maak, Matthias, Wichmann, Matthias, Berselli, Mattia, Gritti, Mattia, Molteni, Mattia, Pacilli, Maurizio, Favoriti, Mauro, Garino, Mauro, Podda, Mauro, Santarelli, Mauro, Alfaid, Mawada, Albert, Mawadri Jimmy Isaac, Oyortey, Mawuenyo Attawa, Enßlin, Max, Berlet, Maximilian, Brunner, Maximilian, Kießler, Maximilian, Scheiterle, Maximilian, Sohn, Maximilian, Busch, Maximiliane, Perkins, Maya, Sabboh, Maya, Badkur, Mayank, Okudero, Mayowa, Mohamedshafee, Maysoon, Al-Juaifari, Maytham Aqeel, Elesseily, Mazen, Mohammad, Mazen, Nikberg, Maziar, Jean Claude, Mbonicura, Şermet, Medeni, Chisthi, Meer, Luthra, Meera, Sulciner, Megan, Kaple, Meghali, Bhat, Meghana, Koç, Mehmet Ali, Avci, Mehmet Alperen, Kizilkaya, Mehmet Celal, Kara, Mehmet Emin, Ozcelik, Mehmet Faik, Ozsoy, Mehmet Sait, Zaigham, Mehreen, Mesfin, Mekdelawit, Kalender, Mekselina, Mellado Tellez, Mel Paul, González De Miguel, Melania, Farrugia, Melanie, Theodoridou, Melanie, Barcelona, Melden Darrell, Aktas, Melik Kagan, Fernández Zurita, Melissa Andrea, Bautista, Melissa, Park, Melissa, Rodríguez-Ordoñez, Melissa-Zohemia, Yashar, Meltem, Worku, Mengistu, Al-Fraijat, Meran, Estaire Gómez, Mercedes, Shoshkova, Meri Danailova, Kruščica, Merima, Tanal, Mert, Çuhadar, Merve, Tokocin, Merve, Agyapong, Meshach Manu, Erkaya, Metincan, Okechukwu, Mgbako, Adinku, Michael, Bickford, Michael, Ediale, Michael, Issa, Michael, Konstantinidis, Michael K., Lie, Michael, M. Nathan, Michael, Maclean, Michael, Nortey, Michael, Pellen, Michael, Pham, Michael, Pitiakoudis, Michael, Singh, Michael Prodip, Spartalis, Michael, Van Der Colf, Michael, Wilson, Michael, Billis, Michail, Karanikas, Michail, Papamichail, Michail, Vailas, Michail, Kisielewski, Michał, Kusiński, Michał, Nycz, Michał, Święch, Michał, Fagbayimu, Micheal, Adamina, Michel, Fiogbe, Michel, Prudhomme, Michel, Angelucci, Michela, Campanelli, Michela, Cricrì, Michele, Grande, Michele, Manigrasso, Michele, Scopelliti, Michele, George, Michelle, Mozel, Michelle, Galasyuk, Mick, Chevallay, Mickael, Giuliano, Micol, Dobón Rascón, Miguel Ángel, Fernandez Romero, Miguel Angel, García Ureña, Miguel Ángel, Zaragoza Mendieta, Miguel Ángel, Bejarano Serrano, Miguel, Brito, Miguel, Calderon, Miguel, Jurado Román, Miguel, Palas, Miguel, Li Valencia, Miguel Roberto, Sebastián-Mendoza, Miguel-Julián, Muresan, Mihai-Stefan, Slavchev, Mihail, Agapov, Mikhail, Milentijević, Milica, Catterall, Millie, Stjepanovic, Milos, Doss, Mina, Natey, Mina, Aikins, Minas, Mattis, Minca, Ekanayake, Minoli Tharushika, Tora, Mir Mehrab Sharmin, Alsharayri, Mira, Shalaby, Mira, Boras, Miran, Riba Martínez, Mireia, Mogiatti, Mirella, Salibašić, Mirhan, Biancu, Miriam, Cappiello, Miriam, Koschke, Miriam Luise, Sadek, Mirna, Szura, Miroslaw, Horga, Miruna, Dason, Mishael, Tayyab, Mishal, Imirski, Mitko, Pejović, Mladen, Tabaza, Mo'Ath, Alneasan, Moaaz, Shalkamy, Moamen, Issa, Moammad Issa, Erfan, Moatasem, Ewedah, Moataz, Srour, Moayad, Beletachew, Moges, Abbas, Mohamad, Farho, Mohamad Ali, Marawy, Mohamad Kher, Klib, Mohamad, Morjan, Mohamad, Sawas, Mohamad Nabhan, Elghriani, Mohamed Abdelbaset Masoud, Abdelmaboud, Mohamed, Abdelmaksoud, Mohamed, Abdullahi, Mohamed, Abo Abdo, Mohamed, Ahmed, Mohamed, Ahmoud, Mohamed, Al Sayed, Mohamed, Suliman, Mohamed Almouafaq, Alsori, Mohamed, Altajouri, Mohamed, Assalhi, Mohamed, Berrakkouch, Mohamed, Elnour, Mohamed, Abdelsalam, Mohamed Elsayed Elaraby Abdelrahim, Elsayed Metwally, Mohamed, Hajalamin, Mohamed, Kamal Matter, Mohamed, Khaled, Mohamed, Kouta, Mohamed, Masoud, Mohamed, Almihashhish, Mohamed Miftah, Mokhtar, Mohamed, Mostafa, Mohamed, Yassin, Mohamed Musa, Bouhafs, Mohamed Nedjmeddine, Herdan, Mohamed Omar, Omer, Mohamed, Osman, Mohamed, Zaazou, Mohamed, Zahed Abdalla, Mohamed, Melouane, Mohamed-Sami, Abed, Mohammad, Sawaftah, Mohammad Ahmad Mohammad, Al-Masri, Mohammad, Al-Qannas, Mohammad, Ali-Hassanzadeh, Mohammad, Yunus, Mohammad Alif, Aloulou, Mohammad, Alrantisi, Mohammad, Araydah, Mohammad, Bani Hani, Mohammad, Barakat, Mohammad, Durrani, Mohammad, Khan, Mohammad Faisal, Dwikat, Mohammad Fuad, Hasan, Mohammad, Nabhan, Mohammad Hassan, Hijazi, Mohammad, Abu Albahrain, Mohammad Karam, Barakat, Mohammad Mounzer, Mubarak, Mohammad, Omar, Mohammad, Pourfridoni, Mohammad, Mslmani, Mohammad Rabee, Salah, Mohammad, Tanashat, Mohammad, Yahmad, Mohammad, Elmujtaba, Mohammed, Abd Al-Fattah, Mohammed, Abdelkabir, Mohammed, Abosedra, Mohammed, Sarker, Mohammed Abu Kawsar, Abudabbous, Mohammed, Abuwarda, Mohammed, Elgak, Mohammed Ahmed, Al Hinai, Mohammed, Al Zebda, Mohammed, Al-Shehari, Mohammed, Alharthi, Mohammed, Ali, Mohammed, Ismael Alamin, Mohammed Ali, Alqadasi, Mohammed, Alshanwani, Mohammed, Alshehri, Mohammed, Asaad, Mohammed, Bashir, Mohammed, Bohlala, Mohammed, Meghaizerou, Mohammed El Amine, Albanna, Mohammed Emad Hashem, Ghunaim, Mohammed, Goga, Mohammed, Hajhamad, Mohammed, Hamada Takrouney, Mohammed, Hammoda, Mohammed, Hamoud Almahly, Mohammed, Muragi, Mohammed, Mohammed, Mohammed Mustafa Hassan, Obaid, Mohammed, Omar, Mohammed, Said, Mohammed, Salah, Mohammed, Saleh Khatab, Mohammed, Aliyu, Mohammed Salele, Shakhshir, Mohammed, Sheriff, Mohammed, Tageldin, Mohammed, Abdelfattah, Mohanad, Daher, Mohanad, Lotfy, Mohanad, Alqedrh, Mohannad, Nasani, Mohannad, Mohd Shah, Mohd Shahrulsalam, Madany, Mohie El-Din Mostafa, Mobolaji-Ojibara, Mojisola, Nkogatse, Moleseng, Alazabi, Mona, Gupta, Money, Bogdan, Monica, Guerrero, Monica, Noguez Castillo, Monica, Gureh, Monika, Kacem, Montasser Jamaleddine, Tarar, Moosa Feroze, Seto, Morel, Steinruecke, Moritz, Edena, Morrison, Abdulsalam, Moruf, Abdelhadi Suliman Adam, Mosab, Saleh, Mosab, Anati, Mosaikah, Dokurugu, Moses Abdulai, Agunloye, Moses, Ibadin, Moses, Momoh, Moses, Abdelkarim, Mostafa, Kamar, Mostafa, Kotb, Mostafa, Mahran, Mostafa, Mohamad Amin, Mostafa, Shalaby, Mostafa, Waseem, Mostafa, Almaletti, Motasem, Saifi, Motaz, Tsuruta, Motohisa, Agbadebo, Mouhamed, Arnaout, Moumen, Ojo, Moyosoluwa, Sharma, Mrthyunjayani, Azzuz, Muad Mohamed Ramadan, Haj Hussein, Mudar, Ameen, Muftiat Omolola, Nkoronko, Mugisha, Fitri, Muhamad, Abdullah, Muhammad, Bashir, Muhammad Ahmad, Sadiq, Muhammad Ahmad, Ahmed, Muhammad, Ali, Muhammad, Al-Yusuf, Muhammad Ali, Haider, Muhammad Aliyan, Alshaar, Muhammad, Mirza, Muhammad Bilal, Che Yaacob, Muhammad, Daniyan, Muhammad, Elzayat, Muhammad, Butt, Muhammad Faizan, Sadiq, Muhammad Hamza, Chishti, Muhammad Haris, Ismail, Muhammad Izzat Shufphi, Kashif, Muhammad, Khalid, Muhammad, Mansoor Iqbal, Muhammad, Bin Khalid, Muhammad Moaz, Rafique, Muhammad Nouman, Raheem, Muhammad, Khalid, Muhammad Saud, Usama, Muhammad, Usman Malik, Muhammad, Saqib, Muhammad Waleed, Elhadi, Muhammed, Ateş, Muhammed Ikbal, Demirpolat, Muhammed Taha, Ergenç, Muhammer, Bello, Muideen, Kulimbet, Mukhtar, Nyirenda, Mulinda, Taeme, Mulugeta, Denini, Muna, Aziz, Munazzah, Yahaya, Mundashiru, Eugene, Muneza, Amadu, Munira, Chauhan, Munish, Acar, Murat, Teressa, Murti, Dauphin, Muryango, Arafat, Musa, Althomali, Musab, Rayzah, Musaed, Husain, Musharraf, Tolani, Musliu Adetola, Abu Jayyab, Mustafa, Al-Attraqchi, Mustafa, Kara, Mustafa Alper, Seker, Mustafa Ege, Gün, Mustafa, Hamed, Mustafa, Mehanny, Mustafa, Oncel, Mustafa, Sabri Massadi, Mustafa, Uyanik, Mustafa Safa, Uzunoglu, Mustafa Yener, Abdullahi, Mustapha, Saidani, Mustapha, Nofal, Mutaz, San, Myat, Ishak, Myrna, Keramida, Myrto D., Nik Burhanuddin, N.M. Syahrin, Talat, Nabila, Shehata, Nada, Alasbahi, Nadeen, Marcos, Nádia, Al Amri, Nadiah, Absy, Nadine, Parker, Naeelah, Lindi, Naeimah, Omer, Nafisa, Papadogianni, Nafsika, Rawal, Nagendra, Ismaiel, Nahar, Ben Hasan, Najat, Albahloul, Najla, Gokhare Viswanath, Nakul, Srinivas, Namitha, Christian, Nana Ama, Obuobi, Nana Kwame Asare, Beharry, Naomi, Yousefzadeh Kandevani, Naser, Alnamari, Nashwan, Oyelowo, Nasir, Oloko, Nasirudeen, Alzerwi, Nasser, Mosbah, Nasser, Shulman, Nastassia, Andrade, Natacha, Boumas, Natacha, Cobeño Tamayo, Natalia, Barrera Lopez, Natalia Guadalupe, Palamara, Natalia, Blencowe, Natalie, Marzouqa, Natalie, Blencowe, Natalie S, Salgadoe, Natalie, Techapongsatorn, Nathakorn, Paez, Nathaly, Ekwo, Nathan, Saini, Navdeep, Malik, Naveed Akhtar, Sharma, Naveen, Kumar, Navin, Chaudhry, Navneet Kumar, Karimbocus, Nawaaz, Rasool, Nazeerah, Lodhi, Nazia, Kavak, Nazli, Ildephonse, Ndayiragije, Diomede, Nduwimana, Bayleyegn, Nebiyou S., S.Bayleyegn, Nebiyou, Jović, Nediljko, 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Nikolaos, Memos, Nikolaos, Michalopoulos, Nikolaos, Mathioudakis, Nikolaos Nestor, Roukounakis, Nikolaos, Stamos, Nikolaos, Tasis, Nikolaos, Tsakiridis, Nikolaos, Vlachakos, Nikolaos, Zampitis, Nikolaos, Kouzakos, Nikolas, Börner, Nikolaus, Dimitriou, Nikoletta, Dafnios, Nikos, Banerjee, Niladri, Klammer, Nils, Maharjan, Nima, Rabai, Nimah, Oscar, Nimpaye, Harrison, Nina, Kheyrbek, Nina, Kansakar, Nirajan, Bhat, Nisar, Krishnappa, Nischal, Pahuja, Nisha, Paul Ambrose, Nitin, Renovat, Niyongabo, Musawa, Nizar, Nwanne, Njideka, Steve, Nkurunziza, Chidumije, Nnaemeka, Nwafulume, Nnaemeka, Celestine, Nnamani, Duru, Nnamdi, López Bernal, Noelia Esthela, Laquatra, Noemi, Pasqua, Noemi, Awwad, Noor Al-Deen, Ali, Nora, Hielscher, Nora, Trabulsi, Nora, Hüser, Norbert, Muñoz Montes, Norberto, Eshak, Norma-Sol, Rajab, Nosaiba, Lahnaoui, Nouhaila, Ouachou, Nouhaila, Ali, Nour, Dadoush, Nour, Fazili, Noureen, Theophile, Ntahompagaze, Ntanzi, Ntuthuko, Bibila, Nubia Samuel, Alwaer, Nuhay, Naabo, Nuhu, Jiagge, Nuna, Freitas Oliveira, Nuno Miguel, Pratas, Nuno, Morricone, Nunzia, Franco, Nunzio Francesco, Binti Yusri, Nur Aima, Ramli, Nur Natasya, Abd Ghani, Nur Suriyana, Umar, Nura, Aly, Nuran Khaled, Lakhanov, Nurgali, Al-Hroub, Nurhan, Kiziltoprak, Nurhilal, Chavarrias, Nuria, Maulenov, Nurlan, Akinbami, Nurudeen, Abdulraheem, Nurudeen Toyin, Karunaratne, Nuwanthika, Jeannette, Nyiransabiyera, Altobaishat, Obieda, Egwuonwu, Ochonma Amobi, Enciu, Octavian, Cohen-Arazi, Oded, Pheiffer, Odette, Okoye, Odili Aloysius, Omoike, Ofure, čančar, Ognjen, Ekwunife, Okechukwu Hyginus, Emuze, Okundayo, Eljizoly, Ola Abdallah, Al Hamdani, Ola, Emadeldeen, Ola, Morgan, Ola, Olajumoke, Oladapo, Popoola, Olakunle, Ajiboye, Olalekan, Olasehinde, Olalekan, Jolayemi, Olamide, Babatunde, Olanrewaju, Balogun, Olanrewaju, Olajide, Olawumi, Lisin, Oleg, Bellou, Olga, Jurić, Oliver, Ladlow, Oliver, Whitehurst, Oliver, Camilleri, Olivia, Montaño Angeles, Olivia, Pryer, Olivia, Ray, Olivia, Gabom, Olivier, Ndizeye, Olivier, Ojewuyi, Olufemi, Oluyemi, Olukayode, Elebute, Olumide, Ogunsua, Oluseyi, Fasiku, Olushola Kayode, Fasoro, Olusola, Fatudimu, Oluwafemi, Osunlusi, Oluwafemi, Owolanke, Oluwagbenga, Oso, Oluwarotimi, Alaba, Oluwaseun, Ojo, Oluwaseun, Oluseye, Oluwaseun, Oyinloye, Oluwaseun, Al Alyani, Omaima, Okrah, Omane, Agha, Omar, Al-Fahel, Omar, Almomani, Omar, Alruwaili, Omar, Ibarra, Omar, Ibrahim, Omar, Khalil, Omar, Kouli, Omar, Lima Azurdia, Omar, Mansour, Omar, Mohamed Mokbel, Omar, Omar, Omar, Bedak, Omer, Ozkan, Omer Faruk, Yalkın, Omer, Mohamed, Omnia, Ayandipo, Omobolaji, Faboya, Omolara, Williams, Omolara Modupe, Irowa, Omorodion, Attawodi, Onuche, Salako, Opeyemi, Ioannidis, Orestis, Agcaoglu, Orhan, Martin Sole, Oriol, Webster, Orla, Abouhiekal, Osama, Al Shaqran, Osama, Bahsas-Zaky, Osama, Nubi, Osama Gamal, Hany, Osama, Matar, Osama, Mahafdah, Osama R., Sarhan, Osama, Osagie, Osasumwen, Soublett Rivas, Óscar Alexander, Gülcicek, Osman Bilgin, Cuenca Torres, Osmar Manuel, Carpineto Samorani, Osvaldo, Arsalan, Othman, Coyoy-Gaitán, Otto, Nouhail, Oumaima, Boujidi, Oumama, Dicko, Ousmane A A, Ahmad, Owais, Christianah, Oyegbola, Küpçüoğlu, Özge, Zanbak Mutlu, Özgecan Pırıl, Chadha, P, Prabhu, P Santosh, Carmignani, Pablo, Arteaga Asensio, Pablo Cesar, Del Val Ruiz, Pablo, Royo Dachary, Pablo, Sancho Pardo, Pablo, Vásquez, Pablo, Sukhvibul, Pakkapol, Assouto, Pamphile, Evangelou, Panagiota, Dorovinis, Panagiotis, Kapsampelis, Panagiotis, Kokoropoulos, Panagiotis, Kostoglou, Panagiotis, Manikis, Panagiotis, Sakarellos, Panagiotis, Lykoudis, Panagis, Kumar, Pankaj, Patel, Panna, Paraskeva, Panoraia, Vassiliu, Panteleimon, Antonakis, Pantelis, De Nardi, Paola, Marongiu, Paola, Kamenova, Paolina, Venturelli, Paolina, Belotti, Paolo, Ubiali, Paolo, Grivas, Paraskevas, Stamopoulos, Paraskevas, Chatzikomnitsa, Paraskevi, Trakosari, Paraskevi, Tekam Wadje, Parfait Stéphane, Manjunath, Parikshith, Gupta, Pariza, Dhamija, Parth, Haque, Parvez, Haque, 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2024
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46. Impact of packaging atmosphere, oregano essential oil, and storage temperature on cold-adapted Salmonella Enteritidis and Salmonella Typhimurium on ready-to-eat smoked turkey

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Mahgoub, Samir A., Qattan, Shaza Y.A., AlMalki, Fatemah, Kamal, Mahmoud, Alqurashi, Amal F., Almuraee, Areej A., Alhassani, Walaa E., Abu-Hiamed, Hind A., Almarkhan, Wafa D., Alsanei, Woroud A., Alfassam, Haifa E., Rudayni, Hassan A., Allam, Ahmed A., Moustafa, Mahmoud, Alshaharni, Mohammed O., and Taha, Ayman E.

Published
2024
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