Author: "Arnaout A. A." - Searchworks@Jio Institute Digital Library Search Results

4,712 results on '"Arnaout A. A."'

1. $\textit{lucie}$: An Improved Python Package for Loading Datasets from the UCI Machine Learning Repository

Author: Ge, Kenneth, Nguyen, Phuc, and Arnaout, Ramy
Subjects: Computer Science - Machine Learning, Computer Science - Information Retrieval
Abstract: The University of California--Irvine (UCI) Machine Learning (ML) Repository (UCIMLR) is consistently cited as one of the most popular dataset repositories, hosting hundreds of high-impact datasets. However, a significant portion, including 28.4% of the top 250, cannot be imported via the $\textit{ucimlrepo}$ package that is provided and recommended by the UCIMLR website. Instead, they are hosted as .zip files, containing nonstandard formats that are difficult to import without additional ad hoc processing. To address this issue, here we present $\textit{lucie}$ -- $\underline{l}oad$ $\underline{U}niversity$ $\underline{C}alifornia$ $\underline{I}rvine$ $\underline{e}xamples$ -- a utility that automatically determines the data format and imports many of these previously non-importable datasets, while preserving as much of a tabular data structure as possible. $\textit{lucie}$ was designed using the top 100 most popular datasets and benchmarked on the next 130, where it resulted in a success rate of 95.4% vs. 73.1% for $\textit{ucimlrepo}$. $\textit{lucie}$ is available as a Python package on PyPI with 98% code coverage., Comment: 5 pages, 3 figures
Published: 2024

2. Beyond Size and Class Balance: Alpha as a New Dataset Quality Metric for Deep Learning

Author: Couch, Josiah, Arnaout, Rima, and Arnaout, Ramy
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, J.3, I.2.6
Abstract: In deep learning, achieving high performance on image classification tasks requires diverse training sets. However, the current best practice$\unicode{x2013}$maximizing dataset size and class balance$\unicode{x2013}$does not guarantee dataset diversity. We hypothesized that, for a given model architecture, model performance can be improved by maximizing diversity more directly. To test this hypothesis, we introduce a comprehensive framework of diversity measures from ecology that generalizes familiar quantities like Shannon entropy by accounting for similarities among images. (Size and class balance emerge as special cases.) Analyzing thousands of subsets from seven medical datasets showed that the best correlates of performance were not size or class balance but $A$$\unicode{x2013}$"big alpha"$\unicode{x2013}$a set of generalized entropy measures interpreted as the effective number of image-class pairs in the dataset, after accounting for image similarities. One of these, $A_0$, explained 67% of the variance in balanced accuracy, vs. 54% for class balance and just 39% for size. The best pair of measures was size-plus-$A_1$ (79%), which outperformed size-plus-class-balance (74%). Subsets with the largest $A_0$ performed up to 16% better than those with the largest size (median improvement, 8%). We propose maximizing $A$ as a way to improve deep learning performance in medical imaging., Comment: 11 pages, 5 figures, 3 tables
Published: 2024

3. The Minimum Information about CLinical Artificial Intelligence Checklist for Generative Modeling Research (MI-CLAIM-GEN)

Author: Miao, Brenda Y., Chen, Irene Y., Williams, Christopher YK, Davidson, Jaysón, Garcia-Agundez, Augusto, Sun, Shenghuan, Zack, Travis, Saria, Suchi, Arnaout, Rima, Quer, Giorgio, Sadaei, Hossein J., Torkamani, Ali, Beaulieu-Jones, Brett, Yu, Bin, Gianfrancesco, Milena, Butte, Atul J., Norgeot, Beau, and Sushil, Madhumita
Subjects: Computer Science - Computation and Language, Computer Science - Computer Vision and Pattern Recognition
Abstract: Recent advances in generative models, including large language models (LLMs), vision language models (VLMs), and diffusion models, have accelerated the field of natural language and image processing in medicine and marked a significant paradigm shift in how biomedical models can be developed and deployed. While these models are highly adaptable to new tasks, scaling and evaluating their usage presents new challenges not addressed in previous frameworks. In particular, the ability of these models to produce useful outputs with little to no specialized training data ("zero-" or "few-shot" approaches), as well as the open-ended nature of their outputs, necessitate the development of new guidelines for robust reporting of clinical generative model research. In response to gaps in standards and best practices for the development of clinical AI tools identified by US Executive Order 141103 and several emerging national networks for clinical AI evaluation, we begin to formalize some of these guidelines by building on the original MI-CLAIM checklist. The new checklist, MI-CLAIM-GEN (Table 1), aims to address differences in training, evaluation, interpretability, and reproducibility of new generative models compared to non-generative ("predictive") AI models. This MI-CLAIM-GEN checklist also seeks to clarify cohort selection reporting with unstructured clinical data and adds additional items on alignment with ethical standards for clinical AI research.
Published: 2024

4. $\textit{greylock}$: A Python Package for Measuring The Composition of Complex Datasets

Author: Nguyen, Phuc, Arora, Rohit, Hill, Elliot D., Braun, Jasper, Morgan, Alexandra, Quintana, Liza M., Mazzoni, Gabrielle, Lee, Ghee Rye, Arnaout, Rima, and Arnaout, Ramy
Subjects: Quantitative Biology - Quantitative Methods
Abstract: Machine-learning datasets are typically characterized by measuring their size and class balance. However, there exists a richer and potentially more useful set of measures, termed diversity measures, that incorporate elements' frequencies and between-element similarities. Although these have been available in the R and Julia programming languages for other applications, they have not been as readily available in Python, which is widely used for machine learning, and are not easily applied to machine-learning-sized datasets without special coding considerations. To address these issues, we developed $\textit{greylock}$, a Python package that calculates diversity measures and is tailored to large datasets. $\textit{greylock}$ can calculate any of the frequency-sensitive measures of Hill's D-number framework, and going beyond Hill, their similarity-sensitive counterparts (Greylock is a mountain). $\textit{greylock}$ also outputs measures that compare datasets (beta diversities). We first briefly review the D-number framework, illustrating how it incorporates elements' frequencies and between-element similarities. We then describe $\textit{greylock}$'s key features and usage. We end with several examples - immunomics, metagenomics, computational pathology, and medical imaging - illustrating $\textit{greylock}$'s applicability across a range of dataset types and fields., Comment: 42 pages, many figures. Many thanks to Ralf Bundschuh for help with the submission process
Published: 2023

5. Intracranial outcomes following neurosurgical resection in patients with brain metastases secondary to HER2-positive breast cancer versus other subtypes

Author: Rashid, Narmeen S., Lamba, Nayan, Catalano, Paul J., Bi, Wenya Linda, Arnaout, Omar, Tanguturi, Shyam K., Rahman, Rifaquat, Haas-Kogan, Daphne A., Lin, Nancy U., Wen, Patrick Y., and Aizer, Ayal A.
Published: 2024
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6. Scaling Monte-Carlo-Based Inference on Antibody and TCR Repertoires

Author: Couch, Josiah, Arora, Rohit, Braun, Jasper, Kaplinsky, Joesph, Hill, Elliot, Li, Anthony, Altschul, Brett, and Arnaout, Ramy
Subjects: Quantitative Biology - Quantitative Methods, Quantitative Biology - Biomolecules, Quantitative Biology - Populations and Evolution
Abstract: Previously, it has been shown that maximum-entropy models of immune-repertoire sequence can be used to determine a person's vaccination status. However, this approach has the drawback of requiring a computationally intensive method to compute each model's partition function ($Z$), the normalization constant required for calculating the probability that the model will generate a given sequence. Specifically, the method required generating approximately $10^{10}$ sequences via Monte-Carlo simulations for each model. This is impractical for large numbers of models. Here we propose an alternative method that requires estimating $Z$ this way for only a few models: it then uses these expensive estimates to estimate $Z$ more efficiently for the remaining models. We demonstrate that this new method enables the generation of accurate estimates for 27 models using only three expensive estimates, thereby reducing the computational cost by an order of magnitude. Importantly, this gain in efficiency is achieved with only minimal impact on classification accuracy. Thus, this new method enables larger-scale investigations in computational immunology and represents a useful contribution to energy-based modeling more generally., Comment: 11 pages, 2 tables, 4 figures
Published: 2023

7. Emergent Coronary Thrombectomy for Acute Myocardial Infarction Immediately Following Craniotomy with Tumor Resection

Author: Ginder, Curtis R., Suero-Abreu, Giselle A., Ghumman, Saad S., Bergmark, Brian A., Arnaout, Omar, and Giugliano, Robert P.
Published: 2024
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8. Are foundation models efficient for medical image segmentation?

Author: Ferreira, Danielle and Arnaout, Rima
Subjects: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract: Foundation models are experiencing a surge in popularity. The Segment Anything model (SAM) asserts an ability to segment a wide spectrum of objects but required supervised training at unprecedented scale. We compared SAM's performance (against clinical ground truth) and resources (labeling time, compute) to a modality-specific, label-free self-supervised learning (SSL) method on 25 measurements for 100 cardiac ultrasounds. SAM performed poorly and required significantly more labeling and computing resources, demonstrating worse efficiency than SSL., Comment: 14 pages, 2 figures, 2 tables
Published: 2023

9. Novel Techniques in Imaging Congenital Heart Disease: JACC Scientific Statement.

Author: Sachdeva, Ritu, Armstrong, Aimee, Arnaout, Rima, Grosse-Wortmann, Lars, Han, B, Mertens, Luc, Moore, Ryan, Olivieri, Laura, Parthiban, Anitha, and Powell, Andrew
Subjects: angiography, artificial intelligence, cardiac computed tomography, cardiac magnetic resonance, digital twin technology, echocardiography, Humans, Artificial Intelligence, Heart Defects, Congenital, Echocardiography, Cardiac Imaging Techniques, Magnetic Resonance Imaging
Abstract: Recent years have witnessed exponential growth in cardiac imaging technologies, allowing better visualization of complex cardiac anatomy and improved assessment of physiology. These advances have become increasingly important as more complex surgical and catheter-based procedures are evolving to address the needs of a growing congenital heart disease population. This state-of-the-art review presents advances in echocardiography, cardiac magnetic resonance, cardiac computed tomography, invasive angiography, 3-dimensional modeling, and digital twin technology. The paper also highlights the integration of artificial intelligence with imaging technology. While some techniques are in their infancy and need further refinement, others have found their way into clinical workflow at well-resourced centers. Studies to evaluate the clinical value and cost-effectiveness of these techniques are needed. For techniques that enhance the value of care for congenital heart disease patients, resources will need to be allocated for education and training to promote widespread implementation.
Published: 2024

10. Data-driven insights can transform women’s reproductive health

Author: Oskotsky, Tomiko T, Yin, Ophelia, Khan, Umair, Arnaout, Leen, and Sirota, Marina
Subjects: Reproductive Medicine, Biomedical and Clinical Sciences, Health Sciences, Contraception/Reproduction, Networking and Information Technology R&D (NITRD), Preterm, Low Birth Weight and Health of the Newborn, Pediatric, Prevention, Perinatal Period - Conditions Originating in Perinatal Period, Infant Mortality, Generic health relevance, Reproductive health and childbirth, Good Health and Well Being, Reproductive biology, Reproductive disorders
Abstract: This perspective explores the transformative potential of data-driven insights to understand and address women's reproductive health conditions. Historically, clinical studies often excluded women, hindering comprehensive research into conditions such as adverse pregnancy outcomes and endometriosis. Recent advances in technology (e.g., next-generation sequencing techniques, electronic medical records (EMRs), computational power) provide unprecedented opportunities for research in women's reproductive health. Studies of molecular data, including large-scale meta-analyses, provide valuable insights into conditions like preterm birth and preeclampsia. Moreover, EMRs and other clinical data sources enable researchers to study populations of individuals, uncovering trends and associations in women's reproductive health conditions. Despite these advancements, challenges such as data completeness, accuracy, and representation persist. We emphasize the importance of holistic approaches, greater inclusion, and refining and expanding on how we leverage data and computational integrative approaches for discoveries so that we can benefit not only women's reproductive health but overall human health.
Published: 2024

11. Learning epistatic polygenic phenotypes with Boolean interactions.

Author: Arnaout, Rima, Brown, Ben, Priest, James, Yu, Bin, Behr, Merle, Kumbier, Karl, Cordova-Palomera, Aldo, Aguirre, Matthew, Ronen, Omer, Ye, Chengzhong, Ashley, Euan, and Butte, Atul
Subjects: Humans, Epistasis, Genetic, Genome-Wide Association Study, Phenotype, Multifactorial Inheritance, Logistic Models, Polymorphism, Single Nucleotide
Abstract: Detecting epistatic drivers of human phenotypes is a considerable challenge. Traditional approaches use regression to sequentially test multiplicative interaction terms involving pairs of genetic variants. For higher-order interactions and genome-wide large-scale data, this strategy is computationally intractable. Moreover, multiplicative terms used in regression modeling may not capture the form of biological interactions. Building on the Predictability, Computability, Stability (PCS) framework, we introduce the epiTree pipeline to extract higher-order interactions from genomic data using tree-based models. The epiTree pipeline first selects a set of variants derived from tissue-specific estimates of gene expression. Next, it uses iterative random forests (iRF) to search training data for candidate Boolean interactions (pairwise and higher-order). We derive significance tests for interactions, based on a stabilized likelihood ratio test, by simulating Boolean tree-structured null (no epistasis) and alternative (epistasis) distributions on hold-out test data. Finally, our pipeline computes PCS epistasis p-values that probabilisticly quantify improvement in prediction accuracy via bootstrap sampling on the test set. We validate the epiTree pipeline in two case studies using data from the UK Biobank: predicting red hair and multiple sclerosis (MS). In the case of predicting red hair, epiTree recovers known epistatic interactions surrounding MC1R and novel interactions, representing non-linearities not captured by logistic regression models. In the case of predicting MS, a more complex phenotype than red hair, epiTree rankings prioritize novel interactions surrounding HLA-DRB1, a variant previously associated with MS in several populations. Taken together, these results highlight the potential for epiTree rankings to help reduce the design space for follow up experiments.
Published: 2024

12. Efficacy and safety of pulsed‐field versus conventional thermal ablation for atrial fibrillation: A systematic review and meta‐analysis

Author: Ahmed Mazen Amin, Abubakar Nazir, Mohamed T. Abuelazm, Ahmed A. Ibrahim, Hossam Elbenawi, Aya Aboutaleb, Mohamed Ellabban, Moumen Arnaout, Mustafa Turkmani, Basel Abdelazeem, and Annabelle S. Volgman
Subjects: ablation, atrial fibrillation, pulsed‐field, Diseases of the circulatory (Cardiovascular) system, RC666-701
Abstract: Abstract Background Pulsed‐field ablation (PFA) has emerged as an innovative alternative to radiofrequency (RF) and cryoablation because it selectively targets myocardial tissue. Thus, we aim to estimate the efficacy and safety of PFA versus thermal ablation for atrial fibrillation (AF) ablation. Methods A systematic review and meta‐analysis were retrieved from PubMed, WOS, SCOPUS, EMBASE, and CENTRAL through September 2023. We used RevMan V. 5.4 to pool dichotomous data using risk ratio (RR) and continuous data using mean difference (MD) with a 95% confidence interval (CI). PROSPERO ID: CRD42023480321 Results We included 17 studies with a total of 2255 patients. PFA was significantly associated with a decreased incidence of AF recurrence (RR: 0.66 with 95% CI [0.51, 0.87], p = .003). However, there was no significant difference between PFA and thermal ablation in arrhythmia recurrence (RR: 0.92 with 95% CI [0.74, 1.46], p = .42). PFA was significantly associated with decreased total procedure time (MD: −15.15 with 95% CI [−20.23, −10.07], p
Published: 2024
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13. Platelet integrin αIIbβ3 plays a key role in a venous thrombogenesis mouse model

Author: Brian D. Adair, Conroy O. Field, José L. Alonso, Jian-Ping Xiong, Shi-Xian Deng, Hyun Sook Ahn, Eivgeni Mashin, Clary B. Clish, Johannes van Agthoven, Mark Yeager, Youzhong Guo, David A. Tess, Donald W. Landry, Mortimer Poncz, and M. Amin Arnaout
Subjects: Science
Abstract: Abstract Venous thrombosis (VT) is a common vascular disease associated with reduced survival and a high recurrence rate. VT is initiated by the accumulation of platelets and neutrophils at sites of endothelial cell activation. A role for platelet αIIbβ3 in VT is not established, a task complicated by the increased bleeding risk caused by partial agonists such as tirofiban. Here, we show that m-tirofiban, a modified version of tirofiban, does not agonize αIIbβ3 based on lack of neoepitope expression and the cryo-EM structure of m-tirofiban/full-length αIIbβ3 complex. m-tirofiban abolishes agonist-induced platelet aggregation while preserving clot retraction ex vivo and, unlike tirofiban, it suppresses venous thrombogenesis in a mouse model without increasing bleeding. These findings establish a key role for αIIbβ3 in VT initiation and suggest that m-tirofiban and compounds with a similar structurally-defined mechanism of action merit consideration as potential thromboprophylaxis agents in patients at high risk for VT and hemorrhage.
Published: 2024
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14. Management of patients with multiple brain metastases

Author: Mohamed M. Arnaout, Samer Hoz, Angle Lee, and Mahmoud Taha
Subjects: Brain metastasis, Chemotherapy, Multiple metastases, Radiotherapy, Outcome, Surgery, RD1-811, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract: Abstract Metastases in the brain are a devastating and common cancer with a poor prognosis. Physicians, on the other hand, may help their patients by suspecting, recognizing, and treating them correctly. It is predicted that between 8 and 10% of cancer patients may develop brain metastases, resulting in roughly 200,000 new cases of brain metastases per year. Single and multiple metastases can share clinical, diagnostic, and therapeutic features. In the majority of brain metastasis, cancer cells move through the blood to the brain. The cerebellum is responsible for 15% of instances; whereas, the brainstem is responsible for 5%. The severity and speed with which the symptoms appear might vary substantially. In addition to the history and physical examination, CT and MRI with contrast give a safe, more sensitive diagnosis. The treatment is primarily palliative, although vigorous therapy in carefully selected patients can prolong the median survival time to about a year. Generally, the prognosis is guarded. Medical treatment includes corticosteroids and antiepileptic. Whole-brain irradiation, SRS, and chemotherapeutic agents are the most common adjuvant therapies. The neurosurgical approach to the management of such lesions has been greatly improved over the last few decades. Hereby, an updated review for the management of multiple brain metastasis.
Published: 2024
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15. Automated ventricular segmentation and shunt failure detection using convolutional neural networks

Author: Kevin T. Huang, Jack McNulty, Helweh Hussein, Neil Klinger, Melissa M. J. Chua, Patrick R. Ng, Joshua Chalif, Neel H. Mehta, and Omar Arnaout
Subjects: Adult hydrocephalus, Ventricular shunt, Machine learning, Computer vision, Medicine, Science
Abstract: Abstract While ventricular shunts are the main treatment for adult hydrocephalus, shunt malfunction remains a common problem that can be challenging to diagnose. Computer vision-derived algorithms present a potential solution. We designed a feasibility study to see if such an algorithm could automatically predict ventriculomegaly indicative of shunt failure in a real-life adult hydrocephalus population. We retrospectively identified a consecutive series of adult shunted hydrocephalus patients over an eight-year period. Associated computed tomography scans were extracted and each scan was reviewed by two investigators. A machine learning algorithm was trained to identify the lateral and third ventricles, and then applied to test scans. Results were compared to human performance using Sørensen–Dice coefficients, calculated total ventricular volumes, and ventriculomegaly as documented in the electronic medical record. 5610 axial images from 191 patients were included for final analysis, with 52 segments (13.6% of total data) reserved for testing. Algorithmic performance on the test group averaged a Dice score of 0.809 ± 0.094. Calculated total ventricular volumes did not differ significantly between computer-derived volumes and volumes marked by either the first reviewer or second reviewer (p > 0.05). Algorithm detection of ventriculomegaly was correct in all test cases and this correlated with correct prediction of need for shunt revision in 92.3% of test cases. Though development challenges remain, it is feasible to create automated algorithms that detect ventriculomegaly in adult hydrocephalus shunt malfunction with high reliability and accuracy.
Published: 2024
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16. Visceral leishmaniasis complicated with hemophagocytic lymphohistiocytosis and resistant to amphotericin B: a case report

Author: Muhamad Zakaria Brimo Alsaman, Fares Abu Sultan, Yazan Ramadan, Khaled Arnaout, Mohamad Shahrour, Bilal Barakat, and Abeer Dayeh
Subjects: Visceral leishmaniasis, Hemophagocytic lymphohistiocytosis, Case report, Medicine
Abstract: Abstract Introduction Hemophagocytic lymphohistiocytosis characterized by hemophagocytosis leading to uncontrolled inflammation; the most common etiology in secondary cases of hemophagocytic lymphohistiocytosis is viral infections, especially Epstein–Barr virus. Visceral leishmaniasis is a vectorborne protozoal disease caused by Leishmania donovani complex. It is common in tropical and subtropical regions, with 50,000–90,000 new cases annually. Case presentation A 15-month-old Arab female was admitted to our hospital with 15 days of fever and decreased weight. On clinical examination, she had a markedly enlarged liver and spleen that were palpable 4 cm and 6 cm below the costal margin, respectively. The peripheral blood smear showed hypochromic microcytic anemia, poikilocytosis, reactive lymphocytosis, and mild thrombocytopenia. Bone marrow aspiration did not show malignancy or any other pathological findings. The patient was put on antibiotic therapy without improvement. Repeated bone marrow aspiration showed erythrophagocytosis; intracellular small round organisms looked like the amastigote form of Leishmania (Donovan bodies) with no evidence of malignancies. Her lab values showed ferritin greater than 500 ug/L, pancytopenia, and hypertriglyceridemia. The patient was diagnosed with hemophagocytic lymphohistiocytosis secondary to visceral leishmaniasis. Conclusion Hemophagocytic lymphohistiocytosis secondary to visceral leishmaniasis is an extensively rare phenomenon in the medical literature that causes challenges in diagnosis and management. Steroids should be used wisely to not cover the symptoms of infections or malignancy, and amphotericin B resistance should be kept in mind in unresponsive Leishmania cases.
Published: 2024
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17. ChatGPT Helped Me Write This Talk Title, but Can It Read an Echocardiogram?

Author: Arnaout, Rima
Subjects: Artificial intelligence, Echocardiography, Feigenbaum lecture, Foundation models, Machine learning, Humans, Echocardiography, Machine Learning
Abstract: While multidisciplinary collaboration in echocardiography is not new, machine learning has the potential to further improve it. In this transcript of the ASE 2023 Annual Feigenbaum lecture, advancements in foundation models are discussed, including their advantages, current disadvantages, and future potential for echocardiography.
Published: 2023

18. Proceedings of the NHLBI Workshop on Artificial Intelligence in Cardiovascular Imaging: Translation to Patient Care.

Author: Dey, Damini, Arnaout, Rima, Antani, Sameer, Badano, Aldo, Jacques, Louis, Li, Huiqing, Leiner, Tim, Margerrison, Edward, Samala, Ravi, Sengupta, Partho, Shah, Sanjiv, Slomka, Piotr, Williams, Michelle, Bandettini, W, and Sachdev, Vandana
Subjects: AI algorithms, artificial intelligence, cardiovascular imaging, data science, deep learning, machine learning, United States, Humans, Artificial Intelligence, National Heart, Lung, and Blood Institute (U.S.), Predictive Value of Tests, Cardiovascular System, Patient Care
Abstract: Artificial intelligence (AI) promises to revolutionize many fields, but its clinical implementation in cardiovascular imaging is still rare despite increasing research. We sought to facilitate discussion across several fields and across the lifecycle of research, development, validation, and implementation to identify challenges and opportunities to further translation of AI in cardiovascular imaging. Furthermore, it seemed apparent that a multidisciplinary effort across institutions would be essential to overcome these challenges. This paper summarizes the proceedings of the National Heart, Lung, and Blood Institute-led workshop, creating consensus around needs and opportunities for institutions at several levels to support and advance research in this field and support future translation.
Published: 2023

19. Platelet integrin αIIbβ3 plays a key role in a venous thrombogenesis mouse model

Author: Adair, Brian D., Field, Conroy O., Alonso, José L., Xiong, Jian-Ping, Deng, Shi-Xian, Ahn, Hyun Sook, Mashin, Eivgeni, Clish, Clary B., van Agthoven, Johannes, Yeager, Mark, Guo, Youzhong, Tess, David A., Landry, Donald W., Poncz, Mortimer, and Arnaout, M. Amin
Published: 2024
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20. Automated ventricular segmentation and shunt failure detection using convolutional neural networks

Author: Huang, Kevin T., McNulty, Jack, Hussein, Helweh, Klinger, Neil, Chua, Melissa M. J., Ng, Patrick R., Chalif, Joshua, Mehta, Neel H., and Arnaout, Omar
Published: 2024
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21. Management of patients with multiple brain metastases

Author: Arnaout, Mohamed M., Hoz, Samer, Lee, Angle, and Taha, Mahmoud
Published: 2024
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22. Visceral leishmaniasis complicated with hemophagocytic lymphohistiocytosis and resistant to amphotericin B: a case report

Author: Brimo Alsaman, Muhamad Zakaria, Abu Sultan, Fares, Ramadan, Yazan, Arnaout, Khaled, Shahrour, Mohamad, Barakat, Bilal, and Dayeh, Abeer
Published: 2024
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23. Optimizing HAR Systems: Comparative Analysis of Enhanced SVM and k-NN Classifiers

Author: Shdefat, Ahmed Younes, Mostafa, Nour, Al-Arnaout, Zakwan, Kotb, Yehia, and Alabed, Samer
Published: 2024
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24. Clinical decision support systems to optimize adherence to anticoagulant guidelines in patients with atrial fibrillation: a systematic review and meta-analysis of randomized controlled trials

Author: Amin, Ahmed Mazen, Ghaly, Ramy, Abuelazm, Mohamed T., Ibrahim, Ahmed A., Tanashat, Mohammad, Arnaout, Moumen, Altobaishat, Obieda, Elshahat, Ahmed, Abdelazeem, Basel, and Balla, Sudarshan
Published: 2024
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25. Association of socioeconomic deprivation with outcomes in critically ill adult patients: an observational prospective multicenter cohort study

Author: Benaïs, Morgan, Duprey, Matthieu, Federici, Laura, Arnaout, Michel, Mora, Pierre, Amouretti, Marc, Bourgeon-Ghittori, Irma, Gaudry, Stéphane, Garçon, Pierre, Reuter, Danielle, Geri, Guillaume, Megarbane, Bruno, Lebut, Jordane, Mekontso-Dessap, Armand, Ricard, Jean-Damien, da Silva, Daniel, and de Montmollin, Etienne
Published: 2024
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26. Elevated risk of recurrence and retreatment for silent pituitary adenomas

Author: Gupta, Saksham, Hoffman, Samantha E., Mehta, Neel H., Hauser, Blake, Altshuler, Marcelle, Bernstock, Joshua D., Smith, Timothy R., Arnaout, Omar, and Laws, Edward R.
Published: 2024
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27. The COVID-19 pandemic and its effects on follow-up of patients with early breast cancer: A patient survey

Author: Beltran-Bless, Ana-Alicia, Larocque, Gail, Brackstone, Muriel, Arnaout, Angel, Caudrelier, Jean-Michel, Boone, Denise, Fallah, Parvaneh, Ng, Terry, Cross, Peter, Alqahtani, Nasser, Hilton, John, Vandermeer, Lisa, Pond, Gregory, and Clemons, Mark
Published: 2024
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28. ENRICHing medical imaging training sets enables more efficient machine learning.

Author: Chinn, Erin, Arora, Rohit, Arnaout, Ramy, and Arnaout, Rima
Subjects: data efficiency, data quality, deep learning, information theory, instance selection, medical imaging, Humans, Diagnostic Imaging, Machine Learning, Radiography, Palliative Care, Image Processing, Computer-Assisted
Abstract: OBJECTIVE: Deep learning (DL) has been applied in proofs of concept across biomedical imaging, including across modalities and medical specialties. Labeled data are critical to training and testing DL models, but human expert labelers are limited. In addition, DL traditionally requires copious training data, which is computationally expensive to process and iterate over. Consequently, it is useful to prioritize using those images that are most likely to improve a models performance, a practice known as instance selection. The challenge is determining how best to prioritize. It is natural to prefer straightforward, robust, quantitative metrics as the basis for prioritization for instance selection. However, in current practice, such metrics are not tailored to, and almost never used for, image datasets. MATERIALS AND METHODS: To address this problem, we introduce ENRICH-Eliminate Noise and Redundancy for Imaging Challenges-a customizable method that prioritizes images based on how much diversity each image adds to the training set. RESULTS: First, we show that medical datasets are special in that in general each image adds less diversity than in nonmedical datasets. Next, we demonstrate that ENRICH achieves nearly maximal performance on classification and segmentation tasks on several medical image datasets using only a fraction of the available images and without up-front data labeling. ENRICH outperforms random image selection, the negative control. Finally, we show that ENRICH can also be used to identify errors and outliers in imaging datasets. CONCLUSIONS: ENRICH is a simple, computationally efficient method for prioritizing images for expert labeling and use in DL.
Published: 2023

29. Can large language models generate salient negative statements?

Author: Arnaout, Hiba and Razniewski, Simon
Subjects: Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract: We examine the ability of large language models (LLMs) to generate salient (interesting) negative statements about real-world entities; an emerging research topic of the last few years. We probe the LLMs using zero- and k-shot unconstrained probes, and compare with traditional methods for negation generation, i.e., pattern-based textual extractions and knowledge-graph-based inferences, as well as crowdsourced gold statements. We measure the correctness and salience of the generated lists about subjects from different domains. Our evaluation shows that guided probes do in fact improve the quality of generated negatives, compared to the zero-shot variant. Nevertheless, using both prompts, LLMs still struggle with the notion of factuality of negatives, frequently generating many ambiguous statements, or statements with negative keywords but a positive meaning., Comment: For data, see https://www.mpi-inf.mpg.de/fileadmin/inf/d5/research/negation_in_KBs/data.csv
Published: 2023

30. Completeness, Recall, and Negation in Open-World Knowledge Bases: A Survey

Author: Razniewski, Simon, Arnaout, Hiba, Ghosh, Shrestha, and Suchanek, Fabian
Subjects: Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Databases, Computer Science - Digital Libraries
Abstract: General-purpose knowledge bases (KBs) are a cornerstone of knowledge-centric AI. Many of them are constructed pragmatically from Web sources, and are thus far from complete. This poses challenges for the consumption as well as the curation of their content. While several surveys target the problem of completing incomplete KBs, the first problem is arguably to know whether and where the KB is incomplete in the first place, and to which degree. In this survey we discuss how knowledge about completeness, recall, and negation in KBs can be expressed, extracted, and inferred. We cover (i) the logical foundations of knowledge representation and querying under partial closed-world semantics; (ii) the estimation of this information via statistical patterns; (iii) the extraction of information about recall from KBs and text; (iv) the identification of interesting negative statements; and (v) relaxed notions of relative recall. This survey is targeted at two types of audiences: (1) practitioners who are interested in tracking KB quality, focusing extraction efforts, and building quality-aware downstream applications; and (2) data management, knowledge base and semantic web researchers who wish to understand the state of the art of knowledge bases beyond the open-world assumption. Consequently, our survey presents both fundamental methodologies and their working, and gives practice-oriented recommendations on how to choose between different approaches for a problem at hand., Comment: 42 pages, 8 figures, 5 tables
Published: 2023

31. Evaluation of GPT-3.5 and GPT-4 for supporting real-world information needs in healthcare delivery

Author: Dash, Debadutta, Thapa, Rahul, Banda, Juan M., Swaminathan, Akshay, Cheatham, Morgan, Kashyap, Mehr, Kotecha, Nikesh, Chen, Jonathan H., Gombar, Saurabh, Downing, Lance, Pedreira, Rachel, Goh, Ethan, Arnaout, Angel, Morris, Garret Kenn, Magon, Honor, Lungren, Matthew P, Horvitz, Eric, and Shah, Nigam H.
Subjects: Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Information Retrieval
Abstract: Despite growing interest in using large language models (LLMs) in healthcare, current explorations do not assess the real-world utility and safety of LLMs in clinical settings. Our objective was to determine whether two LLMs can serve information needs submitted by physicians as questions to an informatics consultation service in a safe and concordant manner. Sixty six questions from an informatics consult service were submitted to GPT-3.5 and GPT-4 via simple prompts. 12 physicians assessed the LLM responses' possibility of patient harm and concordance with existing reports from an informatics consultation service. Physician assessments were summarized based on majority vote. For no questions did a majority of physicians deem either LLM response as harmful. For GPT-3.5, responses to 8 questions were concordant with the informatics consult report, 20 discordant, and 9 were unable to be assessed. There were 29 responses with no majority on "Agree", "Disagree", and "Unable to assess". For GPT-4, responses to 13 questions were concordant, 15 discordant, and 3 were unable to be assessed. There were 35 responses with no majority. Responses from both LLMs were largely devoid of overt harm, but less than 20% of the responses agreed with an answer from an informatics consultation service, responses contained hallucinated references, and physicians were divided on what constitutes harm. These results suggest that while general purpose LLMs are able to provide safe and credible responses, they often do not meet the specific information need of a given question. A definitive evaluation of the usefulness of LLMs in healthcare settings will likely require additional research on prompt engineering, calibration, and custom-tailoring of general purpose models., Comment: 27 pages including supplemental information
Published: 2023

32. Wiki-based Communities of Interest: Demographics and Outliers

Author: Arnaout, Hiba, Razniewski, Simon, and Pan, Jeff Z.
Subjects: Computer Science - Social and Information Networks, Computer Science - Computers and Society
Abstract: In this paper, we release data about demographic information and outliers of communities of interest. Identified from Wiki-based sources, mainly Wikidata, the data covers 7.5k communities, such as members of the White House Coronavirus Task Force, and 345k subjects, e.g., Deborah Birx. We describe the statistical inference methodology adopted to mine such data. We release subject-centric and group-centric datasets in JSON format, as well as a browsing interface. Finally, we forsee three areas this research can have an impact on: in social sciences research, it provides a resource for demographic analyses; in web-scale collaborative encyclopedias, it serves as an edit recommender to fill knowledge gaps; and in web search, it offers lists of salient statements about queried subjects for higher user engagement., Comment: Accepted to ICWSM 2023. For demo, see https://wikiknowledge.onrender.com/demographics/ and for dataset see https://doi.org/10.5281/zenodo.7410436
Published: 2023

33. Optimizing HAR Systems: Comparative Analysis of Enhanced SVM and k-NN Classifiers

Author: Ahmed Younes Shdefat, Nour Mostafa, Zakwan Al-Arnaout, Yehia Kotb, and Samer Alabed
Subjects: HAR, IoT, Pervasive, Machine learning classifiers, k-NN, SVM, Electronic computers. Computer science, QA75.5-76.95
Abstract: Abstract This research addresses the accuracy issues in IoT-based human activity recognition (HAR) applications, essential for health monitoring, elderly care, gait analysis, security, and Industry 5.0. This study uses 12 machine learning approaches, split equally between support vector machine (SVM) and k-nearest neighbor (k-NN) models. Data from 102 individuals, aged 18–43, were used to train and test these models. The researchers aimed to detect twelve daily activities, such as sitting, walking, and cycling. Results showed k-NN models achieved slightly higher accuracy (97.08%) compared to SVM models (95.88%), though SVM had faster processing times. The improved machine learning approaches proved effective in accurately classifying daily activities, with k-NN models outperforming SVM models marginally. The paper provides significant contributions to the field of HAR by enhancing the performance of SVM and k-NN classifiers, optimizing them for higher accuracy and faster processing. Through robust testing with samples of real-world data, the study provides a detailed comparative analysis that highlights strengths and weaknesses of each classifier model, specifically within IoT-based systems. This work not only advances the theoretical understanding and practical applications of HAR systems in areas, such as healthcare and industrial automation, but also sets the stage for future research that could explore hybrid models or further enhancements, consequently improving the efficiency and functionality of IoT devices based on activity recognition.
Published: 2024
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34. Access to and quality of elective care: a prospective cohort study using hernia surgery as a tracer condition in 83 countries

Author: 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, 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Özata, ıbrahim Halil, Kandil, Ibrahim, Ahmed, Ibrahim Mutwakil Gamal, Tagreda, Ibrahim, Garzali, Ibrahim Umar, Nadi, Ibtihal, Ogundele, Ibukunolu, Pius Ogolekwu, Idoko, Shariff, Idriss, Jakaityte, Ieva, Abdullah, Ifra, Babajimi-Joseph, Igbagbolere, Guirguis, Ihab, Orji, Iheanyi, Okoro, Iheanyichukwu, Ahmed, Ihsan, Obianyo, Ijeoma, Njere, Ike, Ugwueke, Ikechukwu, Nzenwa, Ikemsinachi C, Zerrouq, Ikram, Clementi, Ilaria, Neri, Ilaria, Yavuz, Ilayda Esma, Hoxhaj, Ilda, Fakhradiyev, Ildar, R Fakhradiyev, Ildar, Gomatos, Ilias, Fidoshev, Iliya, Gouazar, Iltimass, Alnimer, Imad, Boudis, Imad Eddine, Boumakhlouf, Imad Eddine, Abdalla, Iman, Herrero, Imanol, Osman, Imoro, Nasser, Inas, Singh, Indra, P S, Induchoodan, Borges Da Costa, Inês, Colaço, Ines, Lourenço, Inês Mariana, Mogárrio, Inês, Königsrainer, Ingmar, Ahmed, Intezar, Hamdun, Intisar Hisham Said, Tanase, Ioan, Bokos, Ioannis, Contis, Ioannis, Demiris, Ioannis, Gerogiannis, Ioannis, Gogoulis, Ioannis, Karatsolis, Ioannis, Katsaros, 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Rugambwa, Jean Paul, Jordaan, Jean- Jacques, Razafindrahita, Jeannie Ba, Do, Jee Eun, Senavirathna, Jeewantha, Kalyanapu, Jeffrey A, Chan, Jeffrey, Ofori, Jeffrey, Otote, Jeffrey, Akiely, Jehad, Cassiano Neves, Jelena, Gillingham, Jemima, Herrera Batres, Jenifer Olga María, Ezeh, Jennifer, Okei, Jennifer, Vu, Jennifer, Guevara, Jenny, Siu, Jenny, Rolinger, Jens, Skaff, Jeolle, Uys, Jernelle, Makama, Jerry Godfrey, Barklimore, Jessica, Dei-Asamoa, Jessica, Jahjah, Jessica, Jones, Jessica, Ng, Jessica, Ricardo, Jéssica, Schembri Higgans, Jessica, You, Jessica, Annan, Jessie, Aguilar, Jesus Antonio, Pizarro Lozano, Jesús, Lim, Jia Hui, Zhu, Jiang, Wang, Jianxue, Abbasy, Jibran, Ugwu, Jideofor, Rathod, Jignesh, Albalushi, Jihan, Najajra, Jinan, Fu, Jing Hui, Tefay, Joan Sia Tan, Domingues, Joana Cristina, Simoes, Joana FF, Figueiredo, Joana, Fontaínhas, Joana, Frazão, Joana, Marques Antunes, Joana, Simoes, Joana, Marx, Joanna Naemi, Dias-Ferreira, João, Fernandes, João, Mendes, João, Pinho, João, Ribeiro, João, Vaz, João, Dominguez, Joaquina, Abutu John, Job, Chen, Joe Jun Qiao, Enaholo, Joel, Alaji, Joelle, Miller, Johanna, Binder, Johannes, Daleku, John, Katogiritis, John, Khoury, John, Anyorigiya, John Noah, Reilly, John, Abebrese, John Tabiri, Ahale, Johnny Lordson, Tabora-Zepeda, Johnny, Barrutia Leonardo, Jon, Demma, Jonathan Abraham, Hong, Jonathan, Chejfec-Ciociano, Jonathan Matias, Oppong, Jonathan, Quansah, Jonathan, Sutcliffe, Jonathan, Taiwo, Jones, Nyamekye - Baidoo, Joojo, Prat-Ortells, Jordi, Dongmo, Jordy, Kleeff, Jorg, Jimenez Flores, Jorge Alberto, Pellecer Cano, Jorge Bernardo, Chóliz, Jorge, Giubi Bobeda, Jorge Eduardo, Escartin, Jorge, Febré, Jorge, Gass, Jörn-Markus, Calvache, Jose A, Guzman Barba, Jose Aldo, Gutiérrez Gómez, José Alonso, Calvache, Jose Andres, Acevedo Parrales, José Danilo, Orozco Navarro, José Esteban, Viñas, José Federico, Gomez, José, Goncalves-Nobre, Jose Guilherme, Valenzuela, José Ignacio, Garcia Galocha, Jose Luis, Rodicio Miravalles, Jose Luis, Faylona, Jose Macario, Alvarez Hernandez, Jose Manuel, De Francisco Rios, Jose Mario, Martin Fernandez, Jose, Michel, José, Carlos, José Miguel, Mondino, Jose, Vieira De Sousa, José Pedro, Asturias Luna, José Rodrigo, Hernández, Jose Rodrigo, García, José Sebastian, Galea, Joseph, Hwang, Joseph, Leong, Joseph, Yorke, Joseph, Psaila, Josephine, Walshaw, Josephine, Ampadu, Joshua, Linker, Joshua, Ng-Kamstra, Joshua, Olorunfunmi, Joshua, Mavrek, Josip, Miskovic, Josip, Petric, Josipa, Avakoudjo, Josue, Al Karmi, Joud, Fahed, Joud, Suleiman Hadidi, Joudd, Alkharish, Joul, Juloski, Jovan, Vaitekūnas, Jovydas, Wang, Joyce, Flores Prado, Juan Armando, Beltrán De Heredia, Juan, Garcia Vera, Juan Carlos, Molina Marin, Juan David, Gonzalez Aboytes, Juan Eduardo, Ulloa Robles, Juan Jose, Blas Laina, Juan Luis, Carranza Rosales, Juan Manuel, Morales, Juan, Naranjo Fernández, Juan Ramón, Al Daradkah, Jude, Aseme, Jude, Ede, Jude, Kahn, Judith, Lindert, Judith, Deeb, Judy, Mühlhäusser, Julia, Sidorovskaia, Julia, Carrizo, Julian, Napoli, Juliana, Pereira-Macedo, Juliana, Ribeiro, Juliana, De Deken, Julie, Nnoli, Juliet, Paredes, Julieta, Zevallos-Quiroz, Julio Cesar, Gonzalez Garcia, Julio, Adjei, Julius, Olaogun, Julius, Kutkevičius, Juozas, Metzger, Jürg, Košir, Jurij Aleš, Kosir, Jurij, Seyi-Olajide, Justina, Zajac, Justyna Karolina, Dhima, Jyoti, Dhiman, Jyoti, Shreyas, K, Türker, Kadir Yağız, Lee, Kai, Khutsishvili, Kakhi, Yu, Kam Keung Auston, Das, Kamala, Özdoğan, Kamil, Reddy, Kamireddy Madana Raghava, Kabulov, Kamran, Nahar, Kamrun, Rajaratnam, Kanapathippillai, Wijesinghe, Kanchana, Sevnaran, Kapil, Khalil, Karam, Singh, Karanbir, Fayed, Kareem, Booyse, Karien, Geiger-Timm, Karin, Maharaj, Karl, Bhutia, Karma, Richter, Karolina, Komorowska, Katarzyna, Urbańska, Katarzyna, Waddell, Kate, Kambouri, Katerina, Tata, Katerina, Richetti, Katharina, Griffin, Katherine, McKenzie, Katherine, Tambudze, Katherine, Hall, Katheryn, Chu, Kathryn, Theodoropoulou, Katina, Kröning, Katrin, Bozada-Gutiérrez, Katya, Raghuwanshi, Kaustubh, J B, Kavitha, Ahmed Ibrahim, Kefah, Bwala, Kefas John, Iles, Kelly, Jasso García, Kelvin, Oğur, Kemal, Fretwell, Kenneth, Oluchukwu, Kenneth, Onyekachi, Kenneth, Voon, Kenneth, Oderoha, Kevin Ewomazino, Nieto Yrigoin, Kevin, Quispe De La Roca, Kewin, Anaam, Khadega, Bensmain, Khadija, Shwail, Khadija, Akter, Khadiza, Sawaftah, Khaled Ahmad, Shamiyah, Khalid Adnan, Bhatti, Khalid, Mohammed, Khalid Miftah Ghuraybeel, Ayyoub, Khalil, Cholah, Khalil, Rahman, Khandakar Rezwanur, Abdulrahman, Khawla, Al-Shami, Khayry, Hayat, Khizar, Al Hinai, Khulood, Taghavi, Kiarash, Shodunke, Kikelomo, Abo Zaal, Kinaz, Ewool, Kingsley, Gaikwad, Kiran, Shemyatovsky, Kirill, Dey, Kirsty Elaine, Mishra, Kirti Kumar, Rathod, Kirtikumar J, Senanayake, Kithsiri Janakantha, Noguchi, Kiyonari, Kordeni, Kleoniki C., Dembele, Klétigui Samuel, Spassov, Kolyo, Khalid, Komal, Petrenko, Konstantin, Bekiaridou, Konstantina, Rekouna, Konstantina, Athanassiou, Konstantinos, Avgerinos, Konstantinos, Bouchagier, Konstantinos, Bouliaris, Konstantinos, Bramis, Konstantinos, Iliakopoulos, Konstantinos, Kontzoglou, Konstantinos, Ntatsis, Konstantinos, Pavlopoulos, Konstantinos, Perivoliotis, Konstantinos, Polychronopoulos, Konstantinos, Giannakopoulos, Konstantinos S., Zakkas, Konstantinos, Zapsalis, Konstantinos, Koumarelas, Konstantinos-Eleftherios, Mori, Krinal, Ooi, Krisada, Ascencio Diaz, Kriscia Vanessa, Jasaitis, Kristijonas, Nieuwenhuys, Kristin, Atherton, Kristy, Smith, Kristy, Sharma, Kruti, Jaffry, Kumail, Lehmann, Kuno, Ayad, Kusay, Luthra, Kush Kumar, Nanayakkara, Kushan, Agbedinu, Kwabena, Oduro-Boateng, Kwaku Boakye, Boakye-Acheampong, Kwame, Sim, Kwang Kiat, Yalley, Kwekuma, Collins, Kyeremeh, Muscat, Kyle, Hofmann, Kyra, Lambri, Kyriaki, Bananis, Kyriakos, Nassim, Kyrillos, Allan, Lachlan, Codina Corrons, Laia, Hijazein, Laith, Moolla, Laith, Sweidan, Laith, Rai, Lajpat, Devi, Lakshmi, Fountoulis, Lampros, Andreski, Lara, Bonello, Lara, Rodrigues Madeira, Lara, Tafesh, Lara, Yasin, Lara, Martinez, Laura, Fortuna, Laura, Peña Balboa, Laura Gabriela, García-Zambrano, Laura, Bolaños, Laura Isabela, Jiménez, Laura, Lely, Laura, Ortega Lechuga, Laura, Rossi, Laura, Rubin, Laura, Ruiz-Villa, Laura, Sequi, Laura, Sahan, Laura-Ann, Johnstone, Lauren, Monteiro, Lauren, Abdullahi, Lawal Barau, Jovcheski, Lazo, Siragusa, Leandro, Kang, Leannedra, Elgebaly, Leenah, Mansour, Lefteris, Robine-Durnell, Lélia, Bray, Lemuel Davies, Mohammed, Lena, Licari, Leo, Onyebulu, Leonard, Chieng, Leonard Ron, Solaini, Leonardo, Chardalias, Leonidas, Fountarlis, Leonidas Ilias, Van Den Hil, Leontine, Tapia Moral, Leopoldo, Petashvili, Leri, Heeren, Letícia, Laface, Letizia, Niyidukunda, Lewis, Barnard, Liam, Torres Bavestrello, Lidice Veronica, Hasan, Lilav, Dyab, Lima, Venclauskas, Linas, Beukes, Linda, Gabellini, Linda, Liepa, Linda, Zeng, Lingjuan, Dubs, Linus, Rampersad, Lisa, Catozzi, Livio, Castro, Lizeth, Milic, Ljiljana, Shawesh, Lobna, Anyanwu, Lofty-John, Khalifa, Lojina, Cheng, Lok Pong, Manoj Joshua, Lokavarapu, Shammas, Lora, Fuentes Rivera Lau, Lorena, Rodríguez Gómez, Lorena, Capello, Lorenzo, Epis, Lorenzo, Fatucchi, Lorenzo Maria, Rende, Lorenzo, Ripamonti, Lorenzo, Ackam, Louis, Moniz, Lourenço, Bains, Lovenish, Alriyami, Lubna, Alokshi, Lubnah, Martinek, Lubomir, Tulinsky, Lubomir, Samison, Luc, Lo Piccolo, Luca Benedetto, Cardinali, Luca, Nespoli, Luca Carlo, Casingena, Luca, Degrate, Luca, Bonomo, Luca Domenico, Morelli, Luca, Ottaviani, Luca, Resca, Luca, Scaravilli, Luca, Schiavone, Luca, Theuil, Luca, Casalduero, Lucas, Lee, Lucas D., Granero, Lucas, Caram, Lucas Jose, Barni, Lucia, Cidade Costa, Luciana, Paredes, Luciana, Poggi, Luciano, Marquez, Lucila, Garciandia, Lucio, Taglietti, Lucio, Pramod, Ludiya, Stella, Ludovica, Carbone, Ludovico, Bracciano, Luigi, Cobellis, Luigi, Conti, Luigi, Fedele, Luigi, Ragazzini, Luigi, Simonelli, Luigi, Verre, Luigi, Flores Chávez, Luis Adrián, Rea Bocanegra, Luis Alberto, Pelayo Orozco, Luis Ángel, Asensio Gomez, Luis, Bravo, Luis, Flores Chávez, Luis Manuel, Poggi, Luis, Cabezudo, Luis Ramón, Sante Serna, Luis, Tallon-Aguilar, Luis, Talé-Rosales, Luis-Fernando, Chantada, Luisa, Moreira, Luísa, Hasan, Lujain, M Mheidat, Lujain, Eisner, Lukas, Gantner, Lukas, Havranek, Lukas, Majerčák, Lukáš, Pánči, Lukáš, Nawacki, Łukasz, Adagrah Aniakwo, Luke, Bromley, Luke, Green, Luke, Paterson, Luke, Peters, Luke, Abdur-Rahman, Lukman, Omomeji, Lukman, Amosu, Lukmon, Kolongi, Lungushinga Christopher, Bin Aizan, Luqman Naim, Imchen, Lurong, Chang, Lydia, Mukamazera, Lydie, Katsiaras, Lykoyrgos, Al-Boukhari, Lynn, Chong, Lynn, Karydakis, Lysandros, Peris, M. Roosindu, Cancelas, Mª Dolores, Kariem, Maahir, Amoako-Boateng, Mabel, Alshamikh, Mabroukah, Ishak, Mabruok, Narayanan, Machingal, Matyja, Maciej, Walędziak, Maciej, Zounon, Mack-Arthur, Trindade, Madalena, Shahid, Madeeha, Kelly, Madeleine, Bajwa, Madiha, Bowles, Madison, Roussos, Madison, Sampaio-Alves, Mafalda, Manfredini, Magda, Kołomańska, Magdalena, Redynk, Magdalena, Farooq, Maham, Ali, Mahamudu Ayamba, Rodha, Mahaveer Singh, Alsalawi, Mahdi, Lodha, Mahendra, Sethi, Mahesh Kumar, Khalil, Mahfoud, Singh, Mahima, Abdualqader, Mahmmoud yehia, Abdelhafez, Mahmood, Sersarah, Mahmood, Abdelreheem, Mahmoud, Abu Al Amrain, Mahmoud, Barbarawi, Mahmoud, Diab, Mahmoud, Eissa, Mahmoud, ElFiky, Mahmoud, Elmesalmi, Mahmoud, Elmousili, Mahmoud, Fahmy, Mahmoud, Faisal, Mahmoud, Jabal, Mahmoud, Saad, Mahmoud M., Mahafdah, Mahmoud, Marei, Mahmoud Marei Abdelaziz, Mostafa, Mahmoud, Reda, Mahmoud, Zahran, Mahmoud, Suleman, Mahmowd, Genç, Mahmut Salih, Haghighat Ghahfarokhi, Mahta, Abubakar, Maimuna, Haimona, Mairarangi, Camuera, Maite, H. Oweidat, Majd, Mansour, Majd, Kalabić, Majda, MohammedAli, Majdeddin, Abu Daoud, Majdi, Almahjoub, Majdolin, Bilfaqirah, Majed, Yigah, Makafui, Akkawe, Malak, Dery, Malcolm, West, Malcolm, Rius, Malena, Nandasena, Malith, Aljarawn, Mallak, Masaaod, Mallak, Manangi, Mallikarjuna, Windsouri, Mamadou, Battili, Mamata, Rakotonaivo, Mamisoa Judicaël, Benghazi, Manaji, Al-Dhaheri, Manal, Almaqrahi, Manal, Katif, Manal, Alshami, Manar, Ibrahim, Manar, Ramazanov, Manas, Kaur, Mandeep, Agrawal, Manish Kumar, Pathak, Manish, Aggarwal, Manisha, Chandrasegaram, Manju, Anandan, Manoj, K S D, Manoj Kishan Lal, Kumar, Manoj, Sarda, Manoj, K B, Manojkumar, Jokubauskas, Mantas, Moreno Gonzales, Manuel Arturo, Cruz, Manuel, Diez Alonso, Manuel, Garcia, Manuel, Perez, Manuel, Mosquera Paz, Manuel Santiago, Cammelli, Manuela, Mercurio, Manuela, Singh, Manvinder, Taamreh, Manwa, Canals Sin, Mªpilar, Carbonell Pradas, Mar, Nunes, Mara, Sawaftah, Marah Ahmad, Khalil, Maram, Schneider, Marcel André, Steffani, Marcella, Pisano, Marcello, Doniquian, Marcelo Alejandro, Martínez Bareiro, Marcelo Nicolás, Pagani, Marcelo, Scortechini, Marcelo, Carvalho, Márcia, Sánchez Audelo, Marco Antonio, Brizzolari, Marco, Ceresoli, Marco, De Prizio, Marco, Amisano, Marco Francesco, Gramellini, Marco, Marcianò, Marco, Mariani, Marco, Milone, Marco, Montagna, Marco, Piccino, Marco, Santos, Marco, Dyer, Marcus, Kang, Marcus, Mahadi, Mardhiana, Zawadzki, Marek, Yinusa, Margaret, Bernardo, Margarida, Nunes Luís, Margarida, Gambelli, Margherita, Flint, Margot, Aggelidou, Maria, Bequis, Maria Agustina, Sotiriou, Maria Aikaterini, Ñañez, Maria Alejandra, Tahir, Maria Ambreen, Gascón Domínguez, María Ángeles, Asimakidou, Maria, Mourato, Maria Beatriz, Blanco, Maria, Christou, Maria, Coronas Soucheiron, Maria, Pignanelli, Maria Costanza, Kalaydjian, María Cristina, Martí-Ejarque, Maria del Mar, Olmedo Reinoso, María Del Mar, Cebollero, María Del Pilar, Derattani, Maria, Gama Caldeira, Maria Do Carmo, Drogouti, Maria, Gonzalez-Gonzalez, Maria Elena, Muñoz Fernández, María Elena, Muriel, María Emilia, Duran, María Eugenia, Zarate Casas, María Fernanda, Florou, Maria, Piacentini, Maria Gaia, Seixo, Maria Inês, Bellini, Maria Irene, Peña Soria, Maria Jesús, Martínez, María José, Medina, Maria José, Rueda Medécigo, María José, Karakeke, Maria, Kausar, Maria, Kyriazi, Maria, Marqueta De Salas, Maria, Modolo, Maria Marta, Mateu, Maria, Mavri, Maria, El Ghoul Miliotou, Maria Melina, Moreno Gijon, Maria, Papadoliopoulou, Maria, Proclamà, Maria Pia, Picciochi, Maria, Reis, Maria, Rennis, Maria, D'Anna, Maria Rosa, Ruffoli, Maria, Ruiz Soriano, María, Sánchez Ramirez, Maria, Sánchez-Rubio, María, Schüler, Maria, Sotiropoulou, Maria, Barrio Renteria, Maria Teresa, Tsopozidi, Maria, Vasconcelos, Maria, Facchino, Maria Vittoria, Wikar, Maria, Aguilera-Arevalo, Maria-Lorena, Abdu, Mariam, Asarbakhsh, Mariam, Elmiesiry, Mariam, Ashong, Marian Esi, Gomes, Mariana, Capuano, Marianna, Lill, Marianne, Nieto Galvan, Mariano, Osso, Mariasara, Olivos, Maricarmen, Sacdalan, Marie Dione, Weber, Marie-Christin, Gonzalez, Marijose De Cristo, Ferreira, Marília, Infantes Ormad, Marina, Kouliou, Marina Nektaria, Ransome, Marina, Vicario Bravo, Marina, Cuesta Argos, Mario, Faletar, Mario, Giuffrida, Mario, Lainez Escribano, Mario, Pacilli, Mario, Pannullo, Mario, Rodriguez-Lopez, Mario, Trejo-Avila, Mario, Merrakos, Marios, Tello Jimenez, Marisol, Kalisvaart, Marit, Quaunine, Marium, Perera, Mariyan, Harris, Mark, Youssef, Mark Melad Thabet, Peter, Mark, Philipp, Mark, Portelli, Mark, Jesudason, Mark Ranjan, Sammut, Mark, Tomlinson, Mark, Zammit Vincenti, Mark, Despotidis, Markos, Albertsmeier, Markus, Quante, Markus, Löffler, Markus W., Gurung, Markush, Gumsheimer, Marlena, Benamrouche, Maroua, Chaccour, Maroun, Ahmed, Marriam, Farooqui, Marryam Riaz, Angelini, Marta, Ortega Escudero, Marta, Martin Gimenez, Marta Pilar, Reia, Marta, Scricciolo, Marta, Caramantin Obando, Martha Maria, Gosselink, Martijn, 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, Mosleh, Neebal, Aruldas, Neerav, Kapur, Neeti, Paul Sigamony, Negine Paul, Karthikeyan, Neha, Tabatabaian, Nelia, Affram, Nelson, Agboadoh, Nelson, Tran, Nelson, Lalovic, Nenad, Agugua-Obianyo, Nene, Kullab, Nermen, Lahmer, Nesrin, Raiq, Neven, Soldo, Neven, Spiteri, Neville, Chen, Nevin, Al Rabadi, Nezar, Christian, Ngarambe, Cabrel, Ngongang Ngansop, Leonelle Lore, Nguefack Noudem, Furbetta, Niccolò, Ensor, Nicholas, Tsantikos, Nick, Seeger, Nico, Cillara, Nicola, Eardley, Nicola, Petrarota, Nicola, Tartaglia, Nicola, Feijoo, Nicolas, Finocchiaro, Nicole, Koter, Nicole, De Santis, Nicolò, Fabbri, Nicolò, Schiavon, Nicolò, Tamini, Nicolò, Sharma, Nidhi, Sánchez Fuentes, Nieves, Solomon, Nigus, Bugdayci, Nihat, Lekiashvili, Nika, Gupta, Nikhil, Narain, Nikhil, Uttam, Nikita, Kłos, Nikola, Ververidis, Nikolaos Adrianos, Arkadopoulos, Nikolaos, Dimitrokallis, Nikolaos, Garmpis, Nikolaos, Gouvas, Nikolaos, Kalampokis, Nikolaos, Kopanakis, Nikolaos, Kydonakis, Nikolaos, Machairas, 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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Omar, Yousef, Alhammoud, Yousef, Allaoua, Yousef, Tanas, Yousef, El Okazy, Youssef, Kerolous, Youssef, Mohamed, Youssef, Al-Junaidi, Youssuf, Lakdawala, Yousuf, Lu, Yuann, Asano, Yukio, Altinel, Yuksel, Aktimur, Yunus Emre, Fishman, Yuri, Kudryavcev, Yurii, Tandoğan, Yusuf Iskender, Razafimandimby, Yves Michaël, Adofo-Asamoah, Yvonne, Şenol, Zafer, Odeh, Zahra, Benamrouche, Zahra Rayane, Alkhaier, Zahraa, Al-Sheikh Ali, Zaid, Zaher, Zain Aldin, Alnajjar, Zaina, Al-Balushi, Zainab, Alkhuzaie, Zainab, Khraim, Zakaria, Toutounji, Zakaria, Djama, Zakarya, Farraj, Zamil, Shah, Zamir Ahmad, Khan, Zara, Khan, Zarnigar, Abo Alarous, Zayn Alabden, Asaad, Zaynab, Demetrashvili, Zaza, Lorenc, Zbigniew, Brekalo, Zdrinko, Hannouneh, Zein Alabdin, Robertson, Zelda, Bady, Zeyad, Durna, Zeynep, Wong, Zhen Yu, Fozo, Zien Alabdin, Klib, Zienab, Chin, Ziling, Tamou, Zime Farid, Johnson, Zina, Shabello, Ziyad, Orzeszko, Zofia, Aljalabi, Zohal, Al-Azher El-Hamel, Zohour, Karampotaki, Zoi Rafaila, Valera Sanchez, Zoraida, Matkovic, Zoran, Azhar, Zubair, Qureshi, Zubaria, Correa López, Zulema Margarita, Balciscueta, Zutoia, Musilová, Zuzana, Katusic, Zvonimir, Osama, Zyad, and Dauksa, Zygimantas
Published: 2024
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35. The efficacy and safety of using prophylactic abdominal drainage after laparoscopic cholecystectomy: A randomized control trial

Author: Ahmad Y. Arnaout, Lama Kadoura, Ruqaya Masri, Yaman Nerabani, Abd Alazeez Atli, Khaled Arnaout, Ibrahim Arnaout, Adel Bsata, Hasan Kayali, Nehad Mahli, Ahmad Al‐Haj, Kusay Ayoub, Ammar Niazi, Ahmad Ghazal, and Aleppo University Hospital Team
Subjects: laparoscopic cholecystectomy, prophylactic drainage, randomized control trial, Medicine
Abstract: Abstract Background The use of prophylactic drainage after laparoscopic cholecystectomy has been a routine practice for many years. However, the debate surrounding using it stems from conflicting evidence regarding its potential benefits and risks. Methods Patients who underwent laparoscopic cholecystectomy from February 1, 2022, to November 30, 2022, at Aleppo University Hospital were enrolled according to our previously registered protocol (NCT05267860). Results This study included 232 patients (117 in the drainage group [DG], and 115 in the non‐drainage group [NDG]). There was no statistical difference in the patients' main characteristics, comorbidities, and laboratory findings. The duration of the surgical operation in NDG (mean = 44.92, SD = 1.85) was shorter than in DG (mean 55.14, SD = 2.14), with (p = 0.039) statistically significant, which indicates that the use of the drainage led to a prolongation of the surgical operation. The total number of complicated cases reached 22 (9.48%) cases (DG = 9 vs. NDG = 13, p = 0.348) as follows: bleeding (n = 1) (DG = 1 vs. NDG = 0; p = 0.320), bile leak with no established bile duct injury (n = 1) (DG = 1 vs. NDG = 0; p = 0.320), wound infection (n = 12) (DG = 4 vs. NDG = 8; p = 0.443), urinary tract infection (n = 3) (DG = 0 vs. NDG = 3; p = 0.079), prolonged shoulder pain (n = 2) (DG = 0 vs. NDG = 2; p = 0.152), and acute pancreatitis (n = 1) (DG = 1 vs. NDG = 0; p = 0.144). Conclusion Based on the results of our study, the use of prophylactic drainage was safe, but ineffective, as it did not improve the outcomes statistically significantly or worsen them, which is consistent with previous studies highlighting the need for personalized patient care in this setting.
Published: 2024
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36. Future Evolutions of Fronthauling Architectures over Passive Optical Networks.

Author: Safana Alzoubi, Sandra Arnaout, Md. Arifur Rahman, and Roberto Gaudino
Published: 2024
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37. Dynamic Resource Allocation for Converged Optical and Radio Architecture in 6G Networks.

Author: Sandra Arnaout, Md. Arifur Rahman, Md. Munjure Mowla, Slawomir Hausman, and Piotr Korbel
Published: 2024
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38. FASETS: Discovering Faceted Sets of Entities.

Author: Koninika Pal, Hiba Arnaout, Simon Razniewski, and Gerhard Weikum
Published: 2024
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39. A Case of Scapholunate Instability Treated by Arthroscopic Dorsal Capsuloligamentous Repair (ADCLR)

Author: Arnaout, Ahlam, Mathoulin, Christophe, and Yao, Jeffrey, editor
Published: 2024
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40. Management of CNS Tumors (General Principles)

Author: Mubarak, Mohamed J., Al-Dhahir, Mohammed A., Salih, Hayder R., Ismail, Mustafa, Arnaout, Mohamed M., Hoz, Samer S., Hoz, Samer S., editor, Atallah, Oday, editor, Ma, Li, editor, Aljuboori, Zaid, editor, Sharma, Mayur, editor, Ismail, Mustafa, editor, and Delawan, Maliya, editor
Published: 2024
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41. Label-free segmentation from cardiac ultrasound using self-supervised learning

Author: Ferreira, Danielle L., Salaymang, Zaynaf, and Arnaout, Rima
Subjects: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract: Segmentation and measurement of cardiac chambers is critical in cardiac ultrasound but is laborious and poorly reproducible. Neural networks can assist, but supervised approaches require the same laborious manual annotations. We built a pipeline for self-supervised (no manual labels) segmentation combining computer vision, clinical domain knowledge, and deep learning. We trained on 450 echocardiograms (93,000 images) and tested on 8,393 echocardiograms (4,476,266 images; mean 61 years, 51% female), using the resulting segmentations to calculate biometrics. We also tested against external images from an additional 10,030 patients with available manual tracings of the left ventricle. r2 between clinically measured and pipeline-predicted measurements were similar to reported inter-clinician variation and comparable to supervised learning across several different measurements (r2 0.56-0.84). Average accuracy for detecting abnormal chamber size and function was 0.85 (range 0.71-0.97) compared to clinical measurements. A subset of test echocardiograms (n=553) had corresponding cardiac MRIs, where MRI is the gold standard. Correlation between pipeline and MRI measurements was similar to that between clinical echocardiogram and MRI. Finally, the pipeline accurately segments the left ventricle with an average Dice score of 0.89 (95% CI [0.89]) in the external, manually labeled dataset. Our results demonstrate a manual-label free, clinically valid, and highly scalable method for segmentation from ultrasound, a noisy but globally important imaging modality., Comment: 37 pages, 3 Tables, 7 Figures
Published: 2022

42. Domain-guided data augmentation for deep learning on medical imaging

Author: Athalye, Chinmayee and Arnaout, Rima
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract: While domain-specific data augmentation can be useful in training neural networks for medical imaging tasks, such techniques have not been widely used to date. Here, we test whether domain-specific data augmentation is useful for medical imaging using a well-benchmarked task: view classification on fetal ultrasound FETAL-125 and OB-125 datasets. We found that using a context-preserving cut-paste strategy, we could create valid training data as measured by performance of the resulting trained model on the benchmark test dataset. When used in an online fashion, models trained on this data performed similarly to those trained using traditional data augmentation (FETAL-125 F-score 85.33+/-0.24 vs 86.89+/-0.60, p-value 0.0139; OB-125 F-score 74.60+/-0.11 vs 72.43+/-0.62, p-value 0.0039). Furthermore, the ability to perform augmentations during training time, as well as the ability to apply chosen augmentations equally across data classes, are important considerations in designing a bespoke data augmentation. Finally, we provide open-source code to facilitate running bespoke data augmentations in an online fashion. Taken together, this work expands the ability to design and apply domain-guided data augmentations for medical imaging tasks., Comment: 18 pages, 6 Tables, 3 Figures
Published: 2022
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43. Clinical decision support systems to optimize adherence to anticoagulant guidelines in patients with atrial fibrillation: a systematic review and meta-analysis of randomized controlled trials

Author: Ahmed Mazen Amin, Ramy Ghaly, Mohamed T. Abuelazm, Ahmed A. Ibrahim, Mohammad Tanashat, Moumen Arnaout, Obieda Altobaishat, Ahmed Elshahat, Basel Abdelazeem, and Sudarshan Balla
Subjects: Atrial fibrillation, Oral anticoagulation, Electronic notifications, Electronic alerts., Diseases of the blood and blood-forming organs, RC633-647.5
Abstract: Abstract Background Clinical decision support systems (CDSS) have been utilized as a low-cost intervention to improve healthcare process measures. Thus, we aim to estimate CDSS efficacy to optimize adherence to oral anticoagulant guidelines in eligible patients with atrial fibrillation (AF). Methods A systematic review and meta-analysis of randomized controlled trials (RCTs) retrieved from PubMed, WOS, SCOPUS, EMBASE, and CENTRAL through August 2023. We used RevMan V. 5.4 to pool dichotomous data using risk ratio (RR) with a 95% confidence interval (CI). PROSPERO ID: CRD42023471806. Results We included nine RCTs with a total of 25,573 patients. There was no significant difference, with the use of CDSS compared to routine care, in the number of patients prescribed anticoagulants (RR: 1.06, 95% CI [0.98, 1.14], P = 0.16), the number of patients prescribed antiplatelets (RR: 1.01 with 95% CI [0.97, 1.06], P = 0.59), all-cause mortality (RR: 1.19, 95% CI [0.31, 4.50], P = 0.80), major bleeding (RR: 0.84, 95% CI [0.21, 3.45], P = 0.81), and clinically relevant non-major bleeding (RR: 1.05, 95% CI [0.52, 2.16], P = 0.88). However, CDSS was significantly associated with reduced incidence of myocardial infarction (RR: 0.18, 95% CI [0.06, 0.54], P = 0.002) and cerebral or systemic embolic event (RR: 0.11, 95% CI [0.01, 0.83], P = 0.03). Conclusion We report no significant difference with the use of CDSS compared to routine care in anticoagulant or antiplatelet prescription in eligible patients with AF. CDSS was associated with a reduced incidence of myocardial infarction and cerebral or systemic embolic events.
Published: 2024
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44. Hypoglycemia Unawareness and Recurrent Severe Hypoglycemia in an Individual With Type 1 Diabetes Mellitus on Insulin

Author: Carlos Escudero, MD, MSc, Alaa Husain, MD, FRCPC, and Amel Arnaout, MD, FRCPC
Subjects: type 1 diabetes mellitus, hypoglycemia, hypoglycemia unawareness, insulin, multidisciplinary care, Diseases of the endocrine glands. Clinical endocrinology, RC648-665
Abstract: Background/Objective: Hypoglycemia unawareness is a complication of recurrent hypoglycemia that can complicate diabetes management and impact quality of life. We present the case of an individual with type 1 diabetes with hypoglycemia unawareness and recurrent severe hypoglycemia requiring emergency intervention. Case Report: A 55-year-old man with type 1 diabetes was referred for hypoglycemia unawareness and recurrent hypoglycemia with seizures. Over the prior 4 years he had >400 paramedic responses with 56 hospitalizations. Blood glucose levels ranged between 0.7 and 2.4 mmol/L during these episodes and presenting Hemoglobin A1c (HbA1c) was 4.6% (28 mmol/mol). He was taking insulin glargine daily and aspart with meals via insulin pens with no alternative etiology for his hypoglycemia was identified. The patient expressed difficulty with self-management, social instability, and limited appointment attendance. He was provided a continuous glucose monitor, educational support, and glycemic targets were broadened. After 6 months, HbA1c was 4.6% (28 mmol/mol) and he had 65 paramedic responses. A multidisciplinary team was organized for biweekly follow-up, community outreach, remote technological support, and psychological counseling. After 2 years, the patient had 2 emergency responses and HbA1c was 7.2% (55.2 mmol/mol). Discussion: Permissive hyperglycemia, educational interventions, and continuous glucose monitoring are validated strategies for prevention of hypoglycemia. Limiting hypoglycemia is crucial to restore hypoglycemia awareness, and in severe cases may require high intensity follow-up, community outreach, and psychosocial support. Conclusion: Hypoglycemia unawareness can complicate hypoglycemia prevention. Severe refractory cases are often multifaceted and may warrant a multidisciplinary approach to identify and target patient-specific needs.
Published: 2024
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45. Association of socioeconomic deprivation with outcomes in critically ill adult patients: an observational prospective multicenter cohort study

Author: Morgan Benaïs, Matthieu Duprey, Laura Federici, Michel Arnaout, Pierre Mora, Marc Amouretti, Irma Bourgeon-Ghittori, Stéphane Gaudry, Pierre Garçon, Danielle Reuter, Guillaume Geri, Bruno Megarbane, Jordane Lebut, Armand Mekontso-Dessap, Jean-Damien Ricard, Daniel da Silva, and Etienne de Montmollin
Subjects: Social deprivation, Socioeconomic factor, Socioeconomic status, Critical illness, Intensive care units, Medical emergencies. Critical care. Intensive care. First aid, RC86-88.9
Abstract: Abstract Background The influence of socioeconomic deprivation on health inequalities is established, but its effect on critically ill patients remains unclear, due to inconsistent definitions in previous studies. Methods Prospective multicenter cohort study conducted from March to June 2018 in eight ICUs in the Greater Paris area. All admitted patients aged ≥ 18 years were enrolled. Socioeconomic phenotypes were identified using hierarchical clustering, based on education, health insurance, income, and housing. Association of phenotypes with 180-day mortality was assessed using Cox proportional hazards models. Results A total of 1,748 patients were included. Median age was 62.9 [47.4–74.5] years, 654 (37.4%) patients were female, and median SOFA score was 3 [1–6]. Study population was clustered in five phenotypes with increasing socioeconomic deprivation. Patients from phenotype A (n = 958/1,748, 54.8%) were without socioeconomic deprivation, patients from phenotype B (n = 273/1,748, 15.6%) had only lower education levels, phenotype C patients (n = 117/1,748, 6.7%) had a cumulative burden of 1[1–2] deprivations and all had housing deprivation, phenotype D patients had 2 [1–2] deprivations, all of them with income deprivation, and phenotype E patients (n = 93/1,748, 5.3%) included patients with 3 [2–4] deprivations and included all patients with health insurance deprivation. Patients from phenotypes D and E were younger, had fewer comorbidities, more alcohol and opiate use, and were more frequently admitted due to self-harm diagnoses. Patients from phenotype C (predominant housing deprivation), were more frequently admitted with diagnoses related to chronic respiratory diseases and received more non-invasive positive pressure ventilation. Following adjustment for age, sex, alcohol and opiate use, socioeconomic phenotypes were not associated with increased 180-day mortality: phenotype A (reference); phenotype B (hazard ratio [HR], 0.85; 95% confidence interval CI 0.65–1.12); phenotype C (HR, 0.56; 95% CI 0.34–0.93); phenotype D (HR, 1.09; 95% CI 0.78–1.51); phenotype E (HR, 1.20; 95% CI 0.73–1.96). Conclusions In a universal health care system, the most deprived socioeconomic phenotypes were not associated with increased 180-day mortality. The most disadvantaged populations exhibit distinct characteristics and medical conditions that may be addressed through targeted public health interventions.
Published: 2024
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46. Emergent Coronary Thrombectomy for Acute Myocardial Infarction Immediately Following Craniotomy with Tumor Resection

Author: Curtis R. Ginder, Giselle A. Suero-Abreu, Saad S. Ghumman, Brian A. Bergmark, Omar Arnaout, and Robert P. Giugliano
Subjects: Perioperative ACS, Aspiration thrombectomy, Staged PCI, CYP2C19 genotyping, Platelet aggregation, Diseases of the circulatory (Cardiovascular) system, RC666-701
Abstract: Abstract The management of perioperative acute myocardial infarction (AMI) following oncologic neurosurgery requires balancing competing risks of myocardial ischemia and postoperative bleeding. There are limited human data to establish the safest timing of antiplatelet or anticoagulation therapy following neurosurgical procedures. For patients with malignancy experiencing AMI in the acute postoperative period, staged percutaneous coronary intervention (PCI) with upfront coronary aspiration thrombectomy followed by delayed completion PCI may offer an opportunity for myocardial salvage while minimizing postoperative bleeding risks. CYP2C19 genotyping and platelet aggregation studies can help confirm adequate platelet inhibition once antiplatelet therapy is resumed.
Published: 2024
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47. UnCommonSense: Informative Negative Knowledge about Everyday Concepts

Author: Arnaout, Hiba, Razniewski, Simon, Weikum, Gerhard, and Pan, Jeff Z.
Subjects: Computer Science - Artificial Intelligence, Computer Science - Information Retrieval
Abstract: Commonsense knowledge about everyday concepts is an important asset for AI applications, such as question answering and chatbots. Recently, we have seen an increasing interest in the construction of structured commonsense knowledge bases (CSKBs). An important part of human commonsense is about properties that do not apply to concepts, yet existing CSKBs only store positive statements. Moreover, since CSKBs operate under the open-world assumption, absent statements are considered to have unknown truth rather than being invalid. This paper presents the UNCOMMONSENSE framework for materializing informative negative commonsense statements. Given a target concept, comparable concepts are identified in the CSKB, for which a local closed-world assumption is postulated. This way, positive statements about comparable concepts that are absent for the target concept become seeds for negative statement candidates. The large set of candidates is then scrutinized, pruned and ranked by informativeness. Intrinsic and extrinsic evaluations show that our method significantly outperforms the state-of-the-art. A large dataset of informative negations is released as a resource for future research.
Published: 2022
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48. Domain-guided data augmentation for deep learning on medical imaging.

Author: Athalye, Chinmayee and Arnaout, Rima
Subjects: Humans, Radiography, Ultrasonography, Prenatal, Pregnancy, Female, Deep Learning, Neural Networks, Computer, Biomedical Imaging, Clinical Research, General Science & Technology
Abstract: While domain-specific data augmentation can be useful in training neural networks for medical imaging tasks, such techniques have not been widely used to date. Our objective was to test whether domain-specific data augmentation is useful for medical imaging using a well-benchmarked task: view classification on fetal ultrasound FETAL-125 and OB-125 datasets. We found that using a context-preserving cut-paste strategy, we could create valid training data as measured by performance of the resulting trained model on the benchmark test dataset. When used in an online fashion, models trained on this hybrid data performed similarly to those trained using traditional data augmentation (FETAL-125 F-score 85.33 ± 0.24 vs 86.89 ± 0.60, p-value 0.014; OB-125 F-score 74.60 ± 0.11 vs 72.43 ± 0.62, p-value 0.004). Furthermore, the ability to perform augmentations during training time, as well as the ability to apply chosen augmentations equally across data classes, are important considerations in designing a bespoke data augmentation. Finally, we provide open-source code to facilitate running bespoke data augmentations in an online fashion. Taken together, this work expands the ability to design and apply domain-guided data augmentations for medical imaging tasks.
Published: 2023

49. Artificial intelligence in neurosurgery—a focus on neuro-oncology

Author: Boaro, A., primary and Arnaout, O., additional
Published: 2024
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50. List of contributors

Author: Ahn, Joseph C., primary, Anand, Shankara, additional, Arnaout, O., additional, Bandyopadhyay, Anuja, additional, Bartholomew, Erin, additional, Bates, David W., additional, Bender, Sarah M.L., additional, Bhullar, Puneet K., additional, Bhutani, Tina, additional, Boaro, A., additional, Boyer, Edward W., additional, Brown, Ethan D.L., additional, Carreiro, Stephanie, additional, Chahla, Jorge, additional, Chandran, Viji Pulikkel, additional, Cheng, Ching-Yu, additional, Cheung, Carol Y., additional, Choudhary, Anirudh, additional, Choudhury, Avishek, additional, Chung, Mimi, additional, Ciurtin, Coziana, additional, Comfere, Nneka I., additional, Corriveau-Lecavalier, Nick, additional, Côté, Mélina, additional, del Alamo, Diego, additional, Dias, Roger D., additional, Dönnes, Pierre, additional, Duong, Dat, additional, Ebnali, Mahdi, additional, El Sherbini, Adham, additional, ElZarrad, M. Khair, additional, Fakhouri, Tala H., additional, Fakhoury, Marc, additional, Fein, Joshua A., additional, Fischer, Uwe M., additional, Glicksberg, Benjamin S., additional, Goldstein, Cathy, additional, Gonem, Sherif, additional, Green, Darren V.S., additional, Gupta, Raghav, additional, Hakimi, Marwa, additional, Halamka, John D., additional, Han, Christina S., additional, Hannah-Shmouni, Fady, additional, Harrer, Stefan, additional, Hazime, Ali Amer, additional, Howard, Michael A., additional, Hui, Herbert Y.H., additional, Ildardashty, Alexander, additional, J.F. Shaikh, Hashim, additional, Jeliazkov, Jeliazko R., additional, Jones, David T., additional, Juhn, Young J., additional, Jury, Elizabeth C., additional, Kandaswamy, Swaminathan, additional, Kang, Yanna, additional, Kann, Benjamin H., additional, Kaplin, Scott, additional, Karpiak, Joel, additional, Kassir, Elias, additional, Kaur, Harsimran, additional, Khan, Sohil, additional, Khatib, Reem, additional, Kherabi, Yousra, additional, Knake, Lindsey A., additional, Krittanawong, Chayakrit, additional, Kuhn, Veronica C., additional, Kunze, Kyle, additional, Lamarche, Benoît, additional, Laplante, Simon, additional, Lehmann, Lisa Soleymani, additional, Li, Dawei, additional, Liao, Wilson, additional, Likitlersuang, Jirapat, additional, Liu, Qi, additional, Madani, Amin, additional, Malik, Momin M., additional, Mann, Matthias, additional, Masood, Sameer, additional, Mathur, Piyush, additional, McManus, Sean, additional, Menard, Jeffrey, additional, Mivalt, Filip, additional, Murphree, Dennis, additional, Myers, Thomas G., additional, Narayan, Sanjiv M., additional, Natarajan, Vivek, additional, Pandav, Krunal, additional, Parwani, Anil V., additional, Pedraza Bermeo, Adriana Marcela, additional, Peiffer-Smadja, Nathan, additional, Peng, Junjie, additional, Peng, Lily, additional, Peters, Margot S., additional, Petrick, Nicholas, additional, Polce, Evan, additional, Poojari, Pooja Gopal, additional, Price, W. Nicholson, additional, Raghavan, Lavanya, additional, Rajan, Asha K., additional, Ran, An Ran, additional, Rangu, Sowmith, additional, Rashid, Muhammed, additional, Reddy, Charitha D., additional, Rider, Nicholas L., additional, Rigatti, Marc, additional, Rivers, Michael, additional, Robinson, George, additional, Rogers, Albert J., additional, Rogerson, Colin M., additional, Ryu, Euijung, additional, Sahiner, Berkman, additional, Sarnaik, Kunaal, additional, Shafi, Saba, additional, Shah, Vijay H., additional, Shapovalov, Maxim V., additional, Sharma, Samin K, additional, Shekhar, Skand, additional, Shin, Harold, additional, Shouval, Roni, additional, Smith, Kenneth, additional, Sokumbi, Olayemi, additional, Solomon, Benjamin D., additional, Somani, Sulaiman S., additional, Sternke, Matt C., additional, Strauss, Maximillian T., additional, Syrowatka, Ania, additional, Tang, W. H. Wilson, additional, Teven, Chad M., additional, Tewari, Ashutosh Kumar, additional, Thunga, Girish, additional, Urena, Estefania, additional, Verma, Ashish, additional, Vietas, Jay, additional, Waikel, Rebekah L., additional, Wi, Chung-Il, additional, Winter, Meredith C., additional, Wong, Melissa S., additional, Wong, Tien Yin, additional, Wu, Chao-Ping, additional, Yeroushalmi, Samuel, additional, Zenati, Marco A., additional, Zeng, Wen-Feng, additional, and Zheng, Yingfeng, additional
Published: 2024
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