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105 results on '"Keles, Elif"'

1. Towards Synergistic Deep Learning Models for Volumetric Cirrhotic Liver Segmentation in MRIs

Author

Gorade, Vandan, Susladkar, Onkar, Durak, Gorkem, Keles, Elif, Aktas, Ertugrul, Cebeci, Timurhan, Medetalibeyoglu, Alpay, Ladner, Daniela, Jha, Debesh, and Bagci, Ulas

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

Liver cirrhosis, a leading cause of global mortality, requires precise segmentation of ROIs for effective disease monitoring and treatment planning. Existing segmentation models often fail to capture complex feature interactions and generalize across diverse datasets. To address these limitations, we propose a novel synergistic theory that leverages complementary latent spaces for enhanced feature interaction modeling. Our proposed architecture, nnSynergyNet3D integrates continuous and discrete latent spaces for 3D volumes and features auto-configured training. This approach captures both fine-grained and coarse features, enabling effective modeling of intricate feature interactions. We empirically validated nnSynergyNet3D on a private dataset of 628 high-resolution T1 abdominal MRI scans from 339 patients. Our model outperformed the baseline nnUNet3D by approximately 2%. Additionally, zero-shot testing on healthy liver CT scans from the public LiTS dataset demonstrated superior cross-modal generalization capabilities. These results highlight the potential of synergistic latent space models to improve segmentation accuracy and robustness, thereby enhancing clinical workflows by ensuring consistency across CT and MRI modalities.

Published
2024

2. Large-Scale Multi-Center CT and MRI Segmentation of Pancreas with Deep Learning

Author

Zhang, Zheyuan, Keles, Elif, Durak, Gorkem, Taktak, Yavuz, Susladkar, Onkar, Gorade, Vandan, Jha, Debesh, Ormeci, Asli C., Medetalibeyoglu, Alpay, Yao, Lanhong, Wang, Bin, Isler, Ilkin Sevgi, Peng, Linkai, Pan, Hongyi, Vendrami, Camila Lopes, Bourhani, Amir, Velichko, Yury, Gong, Boqing, Spampinato, Concetto, Pyrros, Ayis, Tiwari, Pallavi, Klatte, Derk C. F., Engels, Megan, Hoogenboom, Sanne, Bolan, Candice W., Agarunov, Emil, Harfouch, Nassier, Huang, Chenchan, Bruno, Marco J., Schoots, Ivo, Keswani, Rajesh N., Miller, Frank H., Gonda, Tamas, Yazici, Cemal, Tirkes, Temel, Turkbey, Baris, Wallace, Michael B., and Bagci, Ulas

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Automated volumetric segmentation of the pancreas on cross-sectional imaging is needed for diagnosis and follow-up of pancreatic diseases. While CT-based pancreatic segmentation is more established, MRI-based segmentation methods are understudied, largely due to a lack of publicly available datasets, benchmarking research efforts, and domain-specific deep learning methods. In this retrospective study, we collected a large dataset (767 scans from 499 participants) of T1-weighted (T1W) and T2-weighted (T2W) abdominal MRI series from five centers between March 2004 and November 2022. We also collected CT scans of 1,350 patients from publicly available sources for benchmarking purposes. We developed a new pancreas segmentation method, called PanSegNet, combining the strengths of nnUNet and a Transformer network with a new linear attention module enabling volumetric computation. We tested PanSegNet's accuracy in cross-modality (a total of 2,117 scans) and cross-center settings with Dice and Hausdorff distance (HD95) evaluation metrics. We used Cohen's kappa statistics for intra and inter-rater agreement evaluation and paired t-tests for volume and Dice comparisons, respectively. For segmentation accuracy, we achieved Dice coefficients of 88.3% (std: 7.2%, at case level) with CT, 85.0% (std: 7.9%) with T1W MRI, and 86.3% (std: 6.4%) with T2W MRI. There was a high correlation for pancreas volume prediction with R^2 of 0.91, 0.84, and 0.85 for CT, T1W, and T2W, respectively. We found moderate inter-observer (0.624 and 0.638 for T1W and T2W MRI, respectively) and high intra-observer agreement scores. All MRI data is made available at https://osf.io/kysnj/. Our source code is available at https://github.com/NUBagciLab/PaNSegNet., Comment: under review version

Published
2024

3. Explainable Transformer Prototypes for Medical Diagnoses

Author

Demir, Ugur, Jha, Debesh, Zhang, Zheyuan, Keles, Elif, Allen, Bradley, Katsaggelos, Aggelos K., and Bagci, Ulas

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Deployments of artificial intelligence in medical diagnostics mandate not just accuracy and efficacy but also trust, emphasizing the need for explainability in machine decisions. The recent trend in automated medical image diagnostics leans towards the deployment of Transformer-based architectures, credited to their impressive capabilities. Since the self-attention feature of transformers contributes towards identifying crucial regions during the classification process, they enhance the trustability of the methods. However, the complex intricacies of these attention mechanisms may fall short of effectively pinpointing the regions of interest directly influencing AI decisions. Our research endeavors to innovate a unique attention block that underscores the correlation between 'regions' rather than 'pixels'. To address this challenge, we introduce an innovative system grounded in prototype learning, featuring an advanced self-attention mechanism that goes beyond conventional ad-hoc visual explanation techniques by offering comprehensible visual insights. A combined quantitative and qualitative methodological approach was used to demonstrate the effectiveness of the proposed method on the large-scale NIH chest X-ray dataset. Experimental results showed that our proposed method offers a promising direction for explainability, which can lead to the development of more trustable systems, which can facilitate easier and rapid adoption of such technology into routine clinics. The code is available at www.github.com/NUBagcilab/r2r_proto.

Published
2024

4. EMIT-Diff: Enhancing Medical Image Segmentation via Text-Guided Diffusion Model

Author

Zhang, Zheyuan, Yao, Lanhong, Wang, Bin, Jha, Debesh, Keles, Elif, Medetalibeyoglu, Alpay, and Bagci, Ulas

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Large-scale, big-variant, and high-quality data are crucial for developing robust and successful deep-learning models for medical applications since they potentially enable better generalization performance and avoid overfitting. However, the scarcity of high-quality labeled data always presents significant challenges. This paper proposes a novel approach to address this challenge by developing controllable diffusion models for medical image synthesis, called EMIT-Diff. We leverage recent diffusion probabilistic models to generate realistic and diverse synthetic medical image data that preserve the essential characteristics of the original medical images by incorporating edge information of objects to guide the synthesis process. In our approach, we ensure that the synthesized samples adhere to medically relevant constraints and preserve the underlying structure of imaging data. Due to the random sampling process by the diffusion model, we can generate an arbitrary number of synthetic images with diverse appearances. To validate the effectiveness of our proposed method, we conduct an extensive set of medical image segmentation experiments on multiple datasets, including Ultrasound breast (+13.87%), CT spleen (+0.38%), and MRI prostate (+7.78%), achieving significant improvements over the baseline segmentation methods. For the first time, to our best knowledge, the promising results demonstrate the effectiveness of our EMIT-Diff for medical image segmentation tasks and show the feasibility of introducing a first-ever text-guided diffusion model for general medical image segmentation tasks. With carefully designed ablation experiments, we investigate the influence of various data augmentation ratios, hyper-parameter settings, patch size for generating random merging mask settings, and combined influence with different network architectures., Comment: 15 pages

Published
2023

5. Radiomics Boosts Deep Learning Model for IPMN Classification

Author

Yao, Lanhong, Zhang, Zheyuan, Demir, Ugur, Keles, Elif, Vendrami, Camila, Agarunov, Emil, Bolan, Candice, Schoots, Ivo, Bruno, Marc, Keswani, Rajesh, Miller, Frank, Gonda, Tamas, Yazici, Cemal, Tirkes, Temel, Wallace, Michael, Spampinato, Concetto, and Bagci, Ulas

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Intraductal Papillary Mucinous Neoplasm (IPMN) cysts are pre-malignant pancreas lesions, and they can progress into pancreatic cancer. Therefore, detecting and stratifying their risk level is of ultimate importance for effective treatment planning and disease control. However, this is a highly challenging task because of the diverse and irregular shape, texture, and size of the IPMN cysts as well as the pancreas. In this study, we propose a novel computer-aided diagnosis pipeline for IPMN risk classification from multi-contrast MRI scans. Our proposed analysis framework includes an efficient volumetric self-adapting segmentation strategy for pancreas delineation, followed by a newly designed deep learning-based classification scheme with a radiomics-based predictive approach. We test our proposed decision-fusion model in multi-center data sets of 246 multi-contrast MRI scans and obtain superior performance to the state of the art (SOTA) in this field. Our ablation studies demonstrate the significance of both radiomics and deep learning modules for achieving the new SOTA performance compared to international guidelines and published studies (81.9\% vs 61.3\% in accuracy). Our findings have important implications for clinical decision-making. In a series of rigorous experiments on multi-center data sets (246 MRI scans from five centers), we achieved unprecedented performance (81.9\% accuracy)., Comment: 10 pages, MICCAI MLMI 2023

Published
2023

6. GazeGNN: A Gaze-Guided Graph Neural Network for Chest X-ray Classification

Author

Wang, Bin, Pan, Hongyi, Aboah, Armstrong, Zhang, Zheyuan, Keles, Elif, Torigian, Drew, Turkbey, Baris, Krupinski, Elizabeth, Udupa, Jayaram, and Bagci, Ulas

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Eye tracking research is important in computer vision because it can help us understand how humans interact with the visual world. Specifically for high-risk applications, such as in medical imaging, eye tracking can help us to comprehend how radiologists and other medical professionals search, analyze, and interpret images for diagnostic and clinical purposes. Hence, the application of eye tracking techniques in disease classification has become increasingly popular in recent years. Contemporary works usually transform gaze information collected by eye tracking devices into visual attention maps (VAMs) to supervise the learning process. However, this is a time-consuming preprocessing step, which stops us from applying eye tracking to radiologists' daily work. To solve this problem, we propose a novel gaze-guided graph neural network (GNN), GazeGNN, to leverage raw eye-gaze data without being converted into VAMs. In GazeGNN, to directly integrate eye gaze into image classification, we create a unified representation graph that models both images and gaze pattern information. With this benefit, we develop a real-time, real-world, end-to-end disease classification algorithm for the first time in the literature. This achievement demonstrates the practicality and feasibility of integrating real-time eye tracking techniques into the daily work of radiologists. To our best knowledge, GazeGNN is the first work that adopts GNN to integrate image and eye-gaze data. Our experiments on the public chest X-ray dataset show that our proposed method exhibits the best classification performance compared to existing methods. The code is available at https://github.com/ukaukaaaa/GazeGNN., Comment: WACV 2024

Published
2023

7. Ensuring Trustworthy Medical Artificial Intelligence through Ethical and Philosophical Principles

Author

Jha, Debesh, Rauniyar, Ashish, Srivastava, Abhiskek, Hagos, Desta Haileselassie, Tomar, Nikhil Kumar, Sharma, Vanshali, Keles, Elif, Zhang, Zheyuan, Demir, Ugur, Topcu, Ahmet, Yazidi, Anis, Håakegård, Jan Erik, and Bagci, Ulas

Subjects
Computer Science - Artificial Intelligence
Abstract

Artificial intelligence (AI) methods hold immense potential to revolutionize numerous medical care by enhancing the experience of medical experts and patients. AI-based computer-assisted diagnosis and treatment tools can democratize healthcare by matching the clinical level or surpassing clinical experts. As a result, advanced healthcare services can be affordable to all populations, irrespective of demographics, race, or socioeconomic background. The democratization of such AI tools can reduce the cost of care, optimize resource allocation, and improve the quality of care. In contrast to humans, AI can uncover complex relations in the data from a large set of inputs and even lead to new evidence-based knowledge in medicine. However, integrating AI into healthcare raises several ethical and philosophical concerns, such as bias, transparency, autonomy, responsibility, and accountability. Here, we emphasize recent advances in AI-assisted medical image analysis, existing standards, and the significance of comprehending ethical issues and best practices for clinical settings. We cover the technical and ethical challenges and implications of deploying AI in hospitals and public organizations. We also discuss key measures and techniques to address ethical challenges, data scarcity, racial bias, lack of transparency, and algorithmic bias and provide recommendations and future directions.

Published
2023

8. Selecting the Best Optimizers for Deep Learning based Medical Image Segmentation

Author

Mortazi, Aliasghar, Cicek, Vedat, Keles, Elif, and Bagci, Ulas

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

The goal of this work is to identify the best optimizers for deep learning in the context of cardiac image segmentation and to provide guidance on how to design segmentation networks with effective optimization strategies. Adaptive learning helps with fast convergence by starting with a larger learning rate (LR) and gradually decreasing it. Momentum optimizers are particularly effective at quickly optimizing neural networks within the accelerated schemes category. By revealing the potential interplay between these two types of algorithms (LR and momentum optimizers or momentum rate (MR) in short), in this article, we explore the two variants of SGD algorithms in a single setting. We suggest using cyclic learning as the base optimizer and integrating optimal values of learning rate and momentum rate. We investigated the relationship of LR and MR under an important problem of medical image segmentation of cardiac structures from MRI and CT scans. We conducted experiments using the cardiac imaging dataset from the ACDC challenge of MICCAI 2017, and four different architectures shown to be successful for cardiac image segmentation problems. Our comprehensive evaluations demonstrated that the proposed optimizer achieved better results (over a 2\% improvement in the dice metric) than other optimizers in deep learning literature with similar or lower computational cost in both single and multi-object segmentation settings. We hypothesized that combination of accelerated and adaptive optimization methods can have a drastic effect in medical image segmentation performances. To this end, we proposed a new cyclic optimization method (\textit{CLMR}) to address the efficiency and accuracy problems in deep learning based medical image segmentation. The proposed strategy yielded better generalization in comparison to adaptive optimizers.

Published
2023

9. The Past, Current, and Future of Neonatal Intensive Care Units with Artificial Intelligence

Author

Keles, Elif and Bagci, Ulas

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

Machine learning and deep learning are two subsets of artificial intelligence that involve teaching computers to learn and make decisions from any sort of data. Most recent developments in artificial intelligence are coming from deep learning, which has proven revolutionary in almost all fields, from computer vision to health sciences. The effects of deep learning in medicine have changed the conventional ways of clinical application significantly. Although some sub-fields of medicine, such as pediatrics, have been relatively slow in receiving the critical benefits of deep learning, related research in pediatrics has started to accumulate to a significant level, too. Hence, in this paper, we review recently developed machine learning and deep learning-based solutions for neonatology applications. We systematically evaluate the roles of both classical machine learning and deep learning in neonatology applications, define the methodologies, including algorithmic developments, and describe the remaining challenges in the assessment of neonatal diseases by using PRISMA 2020 guidelines. To date, the primary areas of focus in neonatology regarding AI applications have included survival analysis, neuroimaging, analysis of vital parameters and biosignals, and retinopathy of prematurity diagnosis. We have categorically summarized 106 research articles from 1996 to 2022 and discussed their pros and cons, respectively. In this systematic review, we aimed to further enhance the comprehensiveness of the study. We also discuss possible directions for new AI models and the future of neonatology with the rising power of AI, suggesting roadmaps for the integration of AI into neonatal intensive care units., Comment: 90 pages, review article

Published
2023

10. A Critical Appraisal of Data Augmentation Methods for Imaging-Based Medical Diagnosis Applications

Author

Pattilachan, Tara M., Demir, Ugur, Keles, Elif, Jha, Debesh, Klatte, Derk, Engels, Megan, Hoogenboom, Sanne, Bolan, Candice, Wallace, Michael, and Bagci, Ulas

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Current data augmentation techniques and transformations are well suited for improving the size and quality of natural image datasets but are not yet optimized for medical imaging. We hypothesize that sub-optimal data augmentations can easily distort or occlude medical images, leading to false positives or negatives during patient diagnosis, prediction, or therapy/surgery evaluation. In our experimental results, we found that utilizing commonly used intensity-based data augmentation distorts the MRI scans and leads to texture information loss, thus negatively affecting the overall performance of classification. Additionally, we observed that commonly used data augmentation methods cannot be used with a plug-and-play approach in medical imaging, and requires manual tuning and adjustment.

Published
2022

11. Radiomics Boosts Deep Learning Model for IPMN Classification

Author

Yao, Lanhong, Zhang, Zheyuan, Demir, Ugur, Keles, Elif, Vendrami, Camila, Agarunov, Emil, Bolan, Candice, Schoots, Ivo, Bruno, Marc, Keswani, Rajesh, Miller, Frank, Gonda, Tamas, Yazici, Cemal, Tirkes, Temel, Wallace, Michael, Spampinato, Concetto, Bagci, Ulas, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Cao, Xiaohuan, editor, Xu, Xuanang, editor, Rekik, Islem, editor, Cui, Zhiming, editor, and Ouyang, Xi, editor

Published
2024
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12. An Efficient Multi-Scale Fusion Network for 3D Organ at Risk (OAR) Segmentation

Author

Srivastava, Abhishek, Jha, Debesh, Keles, Elif, Aydogan, Bulent, Abazeed, Mohamed, and Bagci, Ulas

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Accurate segmentation of organs-at-risks (OARs) is a precursor for optimizing radiation therapy planning. Existing deep learning-based multi-scale fusion architectures have demonstrated a tremendous capacity for 2D medical image segmentation. The key to their success is aggregating global context and maintaining high resolution representations. However, when translated into 3D segmentation problems, existing multi-scale fusion architectures might underperform due to their heavy computation overhead and substantial data diet. To address this issue, we propose a new OAR segmentation framework, called OARFocalFuseNet, which fuses multi-scale features and employs focal modulation for capturing global-local context across multiple scales. Each resolution stream is enriched with features from different resolution scales, and multi-scale information is aggregated to model diverse contextual ranges. As a result, feature representations are further boosted. The comprehensive comparisons in our experimental setup with OAR segmentation as well as multi-organ segmentation show that our proposed OARFocalFuseNet outperforms the recent state-of-the-art methods on publicly available OpenKBP datasets and Synapse multi-organ segmentation. Both of the proposed methods (3D-MSF and OARFocalFuseNet) showed promising performance in terms of standard evaluation metrics. Our best performing method (OARFocalFuseNet) obtained a dice coefficient of 0.7995 and hausdorff distance of 5.1435 on OpenKBP datasets and dice coefficient of 0.8137 on Synapse multi-organ segmentation dataset., Comment: 4 pages, 2 figures, 2 tables

Published
2022

13. Transformer based Generative Adversarial Network for Liver Segmentation

Author

Demir, Ugur, Zhang, Zheyuan, Wang, Bin, Antalek, Matthew, Keles, Elif, Jha, Debesh, Borhani, Amir, Ladner, Daniela, and Bagci, Ulas

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Automated liver segmentation from radiology scans (CT, MRI) can improve surgery and therapy planning and follow-up assessment in addition to conventional use for diagnosis and prognosis. Although convolutional neural networks (CNNs) have become the standard image segmentation tasks, more recently this has started to change towards Transformers based architectures because Transformers are taking advantage of capturing long range dependence modeling capability in signals, so called attention mechanism. In this study, we propose a new segmentation approach using a hybrid approach combining the Transformer(s) with the Generative Adversarial Network (GAN) approach. The premise behind this choice is that the self-attention mechanism of the Transformers allows the network to aggregate the high dimensional feature and provide global information modeling. This mechanism provides better segmentation performance compared with traditional methods. Furthermore, we encode this generator into the GAN based architecture so that the discriminator network in the GAN can classify the credibility of the generated segmentation masks compared with the real masks coming from human (expert) annotations. This allows us to extract the high dimensional topology information in the mask for biomedical image segmentation and provide more reliable segmentation results. Our model achieved a high dice coefficient of 0.9433, recall of 0.9515, and precision of 0.9376 and outperformed other Transformer based approaches.

Published
2022

14. A Population Model of Time-Dependent Changes in Serum Creatinine in (Near)term Neonates with Hypoxic-Ischemic Encephalopathy During and After Therapeutic Hypothermia

Author

Krzyzanski, Wojciech, Wintermark, Pia, Annaert, Pieter, Groenendaal, Floris, Şahin, Suzan, Öncel, Mehmet Yekta, Armangil, Didem, Koc, Esin, Battin, Malcolm R., Gunn, Alistair J., Frymoyer, Adam, Chock, Valerie Y.-L., Keles, Elif, Mekahli, Djalila, van den Anker, John, Smits, Anne, and Allegaert, Karel

Published
2024
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16. Radiomics Boosts Deep Learning Model for IPMN Classification

Author

Yao, Lanhong, primary, Zhang, Zheyuan, additional, Demir, Ugur, additional, Keles, Elif, additional, Vendrami, Camila, additional, Agarunov, Emil, additional, Bolan, Candice, additional, Schoots, Ivo, additional, Bruno, Marc, additional, Keswani, Rajesh, additional, Miller, Frank, additional, Gonda, Tamas, additional, Yazici, Cemal, additional, Tirkes, Temel, additional, Wallace, Michael, additional, Spampinato, Concetto, additional, and Bagci, Ulas, additional

Published
2023
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17. Information Bottleneck Attribution for Visual Explanations of Diagnosis and Prognosis

Author

Demir, Ugur, Irmakci, Ismail, Keles, Elif, Topcu, Ahmet, Xu, Ziyue, Spampinato, Concetto, Jambawalikar, Sachin, Turkbey, Evrim, Turkbey, Baris, and Bagci, Ulas

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Visual explanation methods have an important role in the prognosis of the patients where the annotated data is limited or unavailable. There have been several attempts to use gradient-based attribution methods to localize pathology from medical scans without using segmentation labels. This research direction has been impeded by the lack of robustness and reliability. These methods are highly sensitive to the network parameters. In this study, we introduce a robust visual explanation method to address this problem for medical applications. We provide an innovative visual explanation algorithm for general purpose and as an example application, we demonstrate its effectiveness for quantifying lesions in the lungs caused by the Covid-19 with high accuracy and robustness without using dense segmentation labels. This approach overcomes the drawbacks of commonly used Grad-CAM and its extended versions. The premise behind our proposed strategy is that the information flow is minimized while ensuring the classifier prediction stays similar. Our findings indicate that the bottleneck condition provides a more stable severity estimation than the similar attribution methods.

Published
2021

21. Mortality from gastrointestinal congenital anomalies at 264 hospitals in 74 low-income, middle-income, and high-income countries: a multicentre, international, prospective cohort study

Author

Wright, Naomi Jane, Leather, Andrew J.M., Ade-Ajayi, Niyi, Sevdalis, Nick, Davies, Justine, Poenaru, Dan, Ameh, Emmanuel, Ademuyiwa, Adesoji, Lakhoo, Kokila, Smith, Emily Rose, Douiri, Abdel, Elstad, Maria, Sim, Marcus, Riboni, Cristiana, Martinez-Leo, Bruno, Akhbari, Melika, Tabiri, Stephen, Mitul, Ashrarur, Aziz, Dayang Anita Abdul, Fachin, Camila, Niyukuri, Alliance, Arshad, Muhammad, Ibrahim, Fowzia, Moitt, Natalie, Doheim, Mohamed Fahmy, Thompson, Hannah, Ubhi, Harmony, Williams, Isabelle, Hashim, Sophia, Philipo, Godfrey Sama, Herrera, Laura, Yunus, Aayenah, Vervoort, Dominique, Parker, Samuel, Benaskeur, Yousra-Imane, Alser, Osaid H., Adofo-Ansong, Nana, Alhamid, Ahmad, Salem, Hosni khairy, Saleh, Mahmoud, Elrais, Safa Abdal, Abukhalaf, Sadi, Shinondo, Patricia, Nour, Ibrahim, Aydin, Emrah, Vaitkiene, Agota, Naranjo, Kelly, Dube, Andile Maqhawe, Ngwenya, Sodumisa, Yacoub, Mina A., Kwasau, Henang, Hyman, Gabriella, Elghazaly, Shrouk Mahmoud, Al-Slaibi, Ibrahim, Hisham, Intisar, Franco, Helena, Arbab, Hana, Samad, Lubna, Soomro, Aqil, Chaudhry, Muhammad Amjad, Karim, Safina, Khattak, Muhammad Adnan Khan, Nah, Shireen Anne, Dimatatac, Doris Mae, Choo, Candy SC, Maistry, Niveshni, Mitul, Ashrarur Rahman, Hasan, Samiul, Karim, Sabbir, Yousuf, Hina, Qureshi, Taimur, Nour, Ibrahim Rabi, Al-Taher, Raed Nael, Sarhan, Osama Abdul Kareem, Garcia-Aparicio, Luis, Prat, Jordi, Blazquez-Gomez, Eva, Tarrado, Xavier, Iriondo, Martí, Bragagnini, Paolo, Rite, Segundo, Hagander, Lars, Svensson, Emma, Owusu, Sheila, Abdul-Mumin, Alhassan, Bagbio, Dominic, Ismavel, Vijay Anand, Miriam, Ann, T, Shajin, Anaya Dominguez, Marlene, Ivanov, Monica, Serban, Andreea Madalina, Derbew, Miliard, Elfiky, Mahmoud, Olivos Perez, Maricarmen, Abrunhosa Matias, Marcia, Arnaud, Alexis P, Negida, Ahmed, King, Sebastian, Fazli, Mohamad Rafi, Hamidi, Nadia, Touabti, Souhem, Chipalavela, Rossana Francisco, Lobos, Pablo, Jones, Brendan, Ljuhar, Damir, Singer, Georg, Cordonnier, Annelien, Jáuregui, Lorena, Zvizdic, Zlatan, Wong, Janice, St-Louis, Etienne, Shu, Qiang, Lui, Yang, Correa, Catalina, Pos, Lucie, Alcántara, Elvyn, Féliz, Erick, Zea-Salazar, Luis Enrique, Ali, Liza, Peycelon, Matthieu, Anatole, Nzanzu Kipata, Jallow, Cherno S., Lindert, Judith, Ghosh, Dhruv, Adhiwidjaja, Cathline Freya, Tabari, Ahmad Khaleghnejad, Lotfollahzadeh, Saran, Mussein, Haidar Mohammad, Vatta, Fabrizio, Pasqua, Noemi, Kihiko, David, Gohil, Hetal, Nour, Ibrahim R., Elhadi, Muhammed, Almada, Suad Ahmed, Verkauskas, Gilvydas, Risteski, Toni, Peñarrieta Daher, Alejandro, Outani, Oumaima, Hamill, James, Lawal, Taiwo, Mulu, Jack, Yapo, Benjamin, Saldaña, Lily, Espineda, Beda, Toczewski, Krystian, Tuyishime, Eugene, Ndayishimiye, Isaac, Raboe, Enaam, Hammond, Philip, Walker, Gregor, Djordjevic, Ivona, Chitnis, Milind, Son, Joonhyuk, Lee, Sanghoon, Hussien, Muaad, Malik, Sawazen, Ismail, Enas Musa, Boonthai, Ampaipan, Dahman, Nesrine Ben Hadj, Hall, Nigel, Castedo Camacho, Fabiola Ruth, Sobrero, Helena, Butler, Marilyn, Makhmud, Aliev, Novotny, Nathan, Hammouri, Ahmad G., Al-Rayyes, Maisara, Bvulani, Bruce, Muraveji, Qais, Murzaie, Muhammad Yousuf, Sherzad, Ajmal, Haidari, Sayed Aman, Monawar, Abdul Baqi, Samadi, Dr. Ahmad Zia, Thiessen, Jesh, Venant, Ntakarutimana, Hospital, Sonia Inamuco, Jérémie, Niyonkuru, Mbonicura, Jean Claude, Vianney, Butoyi Jean Marie, Tadesse, Amezene, Negash, Samuel, Roberts, Charles A., Jabang, John N., Bah, Abdoulie, Camamra, Kajali, Correa, Armandou, Sowe, Babucarr, Gai, A., Jaiteh, Musa, Raymond, Kwizera Jean, Mvukiyehe, Jean Paul, Itangishaka, Innocent, Kayibanda, Emmanuel, Manirambona, Emery, Lule, Joseph, Costas-Chavarri, Ainhoa, Shyaka Gashugi, Ian, Ndata, Albert, Gasana, Georges, Nezerwa, Yves Castar, Simeon, Turatsinze, Muragijimana, Jean De Dieu, Rashid, Sakina, Msuya, David, Elisante, Joseph, Solanki, Meghna, Manjira, Emmanuel, Lodhia, Jay, Jusabani, Mubashir, Tarmohamed, Murad, Koipapi, Sengua, Souhem, Touabti, Sara, Nabti, Sihem, Brahimi, Dania, Bouguermouh, Toufik, Iaiche Achour, Mounira, Baghdadi Nour el islam, Habiba, Alouani, Aragão, Liliana, Gonçalves, Victor, Lino Urquizo, Marcelo Mauricio, Varela, Maria Florencia, Mercado, Pedro, Horacio, Bonavia, Damiani, Andrea, Mac, Carlos, Putruele, Daniel, Liljesthrom, Karen, Bernaus, Marianela, Jauri, Cesar, Cripovich, Alejandrina, Bianchin, Ezequiel, Puig, Maria Gabriela, Andreussi, Lorna, Iracelay, Susana, Marcos, Dolores, Herrera, Carina, Palacios, Nelly, Avile, Romina, Serezo, Belen, Montoya, Debora, Cepeda, Rodrigo, Vaquila, Justo, Veronica, Sofficci, Pardo, Liliana, Valeria, Pelussi, Julio, Lapalma, Martin, Aranda Diego, Lucio, Palazzi, Gabriel, Comba, Marianella, Depetrini, Calderón Arancibia, José Alfredo, Huespe, Enrique, Losa, Gabriela Natalia, Arancibia Gutiérrez, Elsa, Scherl, Humberto, Gonzalez, Daniel Emilio, Baistrocchi, Valentina, Silva, Yanina, Galdeano, Marcelo, Medard, Pablo, Sueiras, Ines, Romero Manteola, Enrique, Defago, Victor Hugo, Mieres, Carlos, Alberto, Carlos, Cornelli, Fabio, Molina, Marcelo, Ravetta, Pablo, Patiño Gonzalez, Celeste Carolina, Dallegre, Maria Belen, Szklarz, Maria Tatiana, Leyba, Marcos Federico, Rivarola, Nahuel Ignacio, Charras, Maria Delia, Morales, Adriana, Caseb, Paloma, Toselli, Luzia, Millán, Carolina, Junes, Maria del Carmen, Di Siervi, Oscar, Gilardi, Jose, Simon, Soledad, Contreras, Carla Sofia, Rojas, Nair, Arnoletto, Lucia Beatriz, Blain, Otilia Eva, Bravo, Mauro Nicolas, Sanchez, Nancy, Herrera Pesara, Luciana Martina, Moreno, Maria Eugenia, Sferco, Carlos Ariel, Huq, Umama, Ferdousi, Tamanna, Al-Mamun, Abdullah, Sultana, Sadia, Mahmud, Refoyez, Mahmud, Khalid, Sayeed, Fatema, Svirsky, Alexander, Sempertegui, Denisse, Negrete, Amalia, Teran, Araceli, Sadagurschi, Mariana, Popovic, Nusret, Karavdic, Kenan, Milisic, Emir, Jonuzi, Asmir, Mesic, Amira, Terzic, Sabina, Dendusic, Nejra, Biber, Elna, Sehic, Anesa, Zvizdic, Nada, Letic, Emina, Saracevic, Adna, Hamidovic, Ajla, Selak, Nejra, Horozic, Dzan, Hukic, Lamija, Muhic, Amila, Vanis, Nedim, Sokolovic, Emir, Sabic, Adnan, Becker, Karin, Novochadlo Klüppel, Elis, dos Santos Dias, André Iván Bradley, Agulham, Miguel Angelo, Bischoff, Cristiano, Sabbatini, Stella, Fernandes de Souza, Rachel, Souza Machado, Ana Beatriz, Werneck Raposo, Juliana, da Silva Augusto, Maria Lucia, Martins, Bianca M.R., de Souza Santos Ferreira, Mariana, Fernandes de Oliveira, Darli, Silva dos Santos, Carla, Ribeiro de Fernández y Alcázar, Fernanda, Alves Dutra da Silva, Érika, Furtado, Mariana, Tamada, Horácio, Silva Ferreira dos Santos, Marília, Lopes de Almeida, Thayná, Oliveira de Andrade, Susy, Gurgel do Amaral, Antonio Cipriano, Jr, Sartori Giovanoni, Lais, de Deus Passos Leles, Kamila, Corrêa Costa, Eduardo, Feldens, Leticia, Ferraz Schopf, Luciano, Soares de Fraga, José Carlos, Colombo de Holanda, Felipe, Brolin Santis Isolan, Paola Maria, Loyola Ferreira, Julia, Bruxel, Carla Luisa, Lopes Teixeira Ferdinando, Danielle, Zottis Barcelos, Fabricio, Baseggio, Natalia, Knorr Brenner, Nicole, Trindade Deyl, Rafael, Dure, Carolina, Nunes Kist, Iuri, Bueno Mazzuca, Rafael, Bueno Motter, Sarah, Ramos, Yna, Suzana Trein, Cristine, Rezende Rosa, Bianca, de Assis Silva, Murilo, Menin, Flavio Augusto, Semensato Carloni, Isabela Cristina, Norberto da Silva, Juliana Antinarelli, Gomes, Adriano Luis, Girão Tauffer, Mariana, Bassan Gonçalves, Paulo César, Nogueira Marques, Geraldo Magela, Moriya, Eliane, Labonia, Carla, Carrasco, Ana Lucia, Furtado Meyer, Karine, Farion-Aguiar, Luiz, Amado, Fernando, Antunes, Amanda, Silva, Elisângela, Telles, Leila, Almeida, Giovana, Belmino Gadelha, Aluísio Augusto, de Azevedo Belesa, Flavia, Gonçalves da Cunha, Jr, Acimar, Souza Barros, Beatriz, Zanellato, Josiane Bernartt, Guimarães, Patricia, Silva, Karina Ilheu da, Ribas, Bianca, Reuter, Cristina, Casado, Francis Tanise, Correa Leite, Mila Torii, Testoni, Daniela, Guinsburg, Ruth, de Campos Vieira Abib, Simone, Khodor Cury, Edson, Dornellas do Nascimento, Suely, Almeida Aguiar, Arthur, Melo Gallindo, Rodrigo, Gonçalves Borges, Carolina, Liu, Yang, Duote, Cai, Wang, Jinhu, Gao, Zhigang, Liang, Liang, Luo, Wenjuan, Zhao, Xiaoxia, Chen, Rui, Wang, Peng, Han, Yijiang, Huang, Ting, Donglai, Hu, Xiaodong, Guo, Junjie, Chen, Zhu, Libin, Wu, Guowei, Bao, Xiaozhou, Li, Haijing, Lv, Junying, Li, Zhongrong, Yong, Feng, Gao, Zhou Chong, Bai, Qiang, Tang, Weibing, Xie, Hua, Motee, Jethishka, Zhu, Jianming, Wen, Gang, Ruan, Weiwei, Li, Shungen, Chen, Lulu, Huang, Shungen, Lv, Zhibao, Lu, Jinjing, Huang, Liuming, Yu, Mengnan, Dajia, Wang, Bai, Yu Zuo, Rincon, Luis Carlos, Mancera, Juliana, Alzate Gallego, Edgar, Torres-Canchala, Laura, Silva Beltrán, Nathalia, Osorio Fory, Ghordana, Castaño Avila, Daniela, Forero Ladino, Angelica Maria, Gomez, Juanita, Jaramillo, Martha, Morales, Otto, Sanchez, Beatriz, Tinoco Guzmán, Nestor Julien, Castañeda Espinosa, Sergio, Prieto Vargas, Osbaldo, Pardo, Lina Maria, Toral, Eliana, Cáceres Aucatoma, Freud, Hinostroza, Daniel, Valencia, Santiago, Salinas, Vicente, Landivar Cino, Enrique, Ponce Fajardo, Gabriela Yulissa, Astudillo, Miguel, Garcia, Virginia, Muñoz, Guillermo, Verduga, Leonardo, Verduga, Ivan, Murillo, Ericka, Bucaram, Elena, Guayelema, Marisol, Marmol, Monica, Sanchez, Janina, Vergara, Carolina, Mena, Adriana, Velaña, Junior, Salazar, Karla, Lara, Sandra, Chiriboga, Elena, Silva, Julian, Gad, Dalia, Samy, Doaa, Elsadek, Menan Ahmed, Mohammed, Hanan Mahmoud, Abouheba, Mohamed, Ali, Karim Osamy, Rashwan, Hayssam, Fawzy, Omar Moustafa, Kamel, Tarek mohamed, Nemer, Rawan, Hassan, Mohamed Abada, Falah, Eyad Hassan, Abdelhady, Dina Sobhy, Zain, Mostafa, Ibrahim, Eman Abouzeid Abouzeid, Elsiraffy, Omar Ossama, Aboelela, Ahmed, Farag, Eman mohamed, Oshiba, Ahmed Mohamed, Emam, Omar Sameh, Attia, Alaa Mobarak, Laymouna, Moustafa A., Ghorab, Islam Abdelmonem, Mohammed, Mansour Mkayed, Soliman, Nourhan Akram, Ghaly, Khaled Abd elrahman, Sadek, Kareem, Elsherbiny, Mohamed, Saleh, Amr, Sheir, Hesham, Wafa, Tamer, Elmenam, Mohamed Abd, Abdelmaksoud, Sherif, Reda, Ahmed, Mansour, Islam, Elzohiri, Mohamed, Waseem, Basma, Elewaily, Mohamed, El-Ghazaly, Mohammed, Elhattab, Ahmad, Shalaby, Amr, Elsaied, Adham, Adawy, Ahmad, Sadek, Mirna, Ahmed, Mahmoud Abdelfattah, Herdan, Mohamed Omar, Elassall, Gena Mohamed Hamed, Mohammed, Azhar Arabi, Takrouney, Mohammed Hamada, Essa, Tarek Mohamed, Mahmoud, Ahmed Mokhtar, Saad, Alshaimaa M., Fouly, Mariam Albatoul Nageh, Ibrahim, Mahmoud abdelshakour, Nageh, Mohammad, Saad, Mahmoud M., Badr, Helmy, Fouda, Mohamed Fayez, Nofal, Ahmed Hassan, Almohamady, Hisham, Arafa, Mohamed Ahmed, Amad, Mohamed, Mansour, Mohamed Awad, O'Connor, Jennifer, O'Connor, Zachary, Anatole, Nzanzu, Nkunzimana, Elysé, Machemedze, Solomon, Dieudonné, Lemfuka, Appeadu-Mensah, William, Anyomih, Theophilus Teddy Kojo, Alhassan, Priscilla, Abantanga, Francis A., Michael, Vishal, Mary Koshy, Roshine, Raj, Ankit, Kumar, Vijay, PT, Sundeep, Prabhu, P Santosh, Vosoughi, Armin, Al-Mayoof, Ali Farooq, Fadhle, Muhamed Jassim, Joda, Ali Egab, Algabri, Hayder Nadhim Obaid, Abdelhamid, Sultan S., Al-Momani, Hashem M., Amarin, Marzouq, Zaghlol, Louay Y., Alsaadi, Nijmeh Nasser, Qwaider, Yasmeen Z., Qutishat, Hibah, Aliwisat, Ahmad Hasan, Arabiat, Esraa, Bsisu, Isam, Murshidi, Raghad M., Jabaiti, Mohammad S., Bataineh, Ziad A., Abuhayyeh, Husam Aldean, Quran, Thekraiat M. Al, Za'nouneh, Faris J. Abu, Alebbini, Mohanad Mutasem, Qudah, Hamzah Abullah, Hussein, Omar Ghazi, Murad, Amir M.I., Amarin, Justin Z., Suradi, Haya H., Alzraikat, Sayel H., Omari, Rand Y., Matour, Bashar M., Al-Halbouni, Layana, Zurikat, Rajai O., Yanis, Ahmad H., Hussein, Sara Al, Shoubaki, Ali, Ghanem, Waleed H., David, Kuria, Chitiavi, Soita Wycliffe, Mose, Moraa, Mugo, Robert, Ndungu, James, Mwai, Timothy, Shahbal, Swaleh, Malik, Janan, Chauhan, Nirav, Syovata, Francisa, Ochieng, Kevin, Omendo Liyenzero, Polycarp, Hussain, Syeda Ra'ana, Mugambi, Stanley, Ochieng, Roseline, Elkhazmi, Ebtesam Othman Abdulsalam, Khaled, Ala, Albozidi, Aya, Enbaya, Manal Ben, Elgammudi, Mala, Soula, Enas, Khalel, Wegden ibrahim almabrouk, Elhajjaji, Yasmine Ali, Alwaggaa, Nouriyah Ali, Ghayth, Sumayyah, Zreeg, Dafer abdulhakim .S., Tantush, Sara Abobaker, Bibas, Fatma, Layas, Tesneem, Sharif, Randa Alamen M, Aljadidi, Wesal Omar F. Saied, Tarek, Ahmed, Ahmed, Hazem, Essamilghi, Kamila Almabrouk Mohammed, Alfoghi, Mabroka, Abuhlega, Ma'aly A., Arrmali, Saddam, Abduljawad, Fatima Mousa, Alosta, Hasan Mustafa, Abuajaila, Abdulsalam, Abdelmutalib, Fakereldeen, Bashir, Fatma, Almengar, Inas, Annajjar, Mohammad hasan, Deyab, Abdelaziz, Elzowawi, Fathi, Krayem, Yousef, Drah, Weam, Meftah, Asma, Mohammed, Abobaker, Arrmalli, Lina Ali, Aljaboo, Hajir, Elayeb, Abdallah, Altomi, Mohamed, Altaweel, Ahmed, Tumi, Mohamed, Bazozi, Hana Milad, Shaklawoon, Aisha, Alglaib, Mohammed Meftah, Elkaloush, Abdullahn Abdousalam, Trainba, Sara, Swessi, Hisham, Alnaeri, Ali, Shnishah, Aya Essam, Mustufa, Hamassat, Gargum, Sondas Ali, Tarniba, Sara Ali, Shalluf, Hawa Ahmed, Shokri, Hajer Ali, Sarkaz, Taher L., Tababa, Osama, Elhadi, Ahmed, Naunova, Vesna Cvetanovska, Jovcheski, Laze, Kamilovski, Marjan, Gavrilovska-Brzanov, Aleksandra, Latiff, Zarina Abdul, Pauzi, Siti Farhan Moh, Osman, Marjmin, Lim, Felicia, Bakar, Ainal Huda Abu, Zaman, Azrina SK, Ishak, Shareena, Teo, Rufinah, Qi, Dr. Tammy Teoh Han, Othman, Mohd Yusran Bin, Zahari, Dato' Dr Zakaria bin, Hassan, Zulfitri bin Md, Shan, Cheah Hui, Lechmiannandan, Abhirrami, Tamaddun, Hafatin Fairos bt, Adanan, Mohd Fitri Shukri bin Mohamed, Abdullah, Mohd Yusof bin, Junyi, Wang, Nor, Mohd. Tarmizi Mohd, Noor, Wan Ruzaimie, Hassan, Mohd Razin bin, Dalek, Noor Fa'izatul Rahil Ambok, Hashim, Hidayah Hayati binti, Zarwawi, Ahmad Zulhisyam bin, Vellusamy, V Muthualhagi M, Yuen, Quah Soong, Kannessan, Hemasutha a/p, Ramli, Najua binti, Bujarimin, Ahmad Shafiee bin, Anntinea, Jessmine, Dass, Anthony, Khalid, Hazlina Mohd., Hanifah, Nur Atiqah binti Mohd, Jyun, Keily Wong Yue, Razak, Rahilah binti Abd, Naim, Nur Atifah binti Mohd, Hamzah, Siti Nur Aien binti Hamid, Vidal, Cristian R. Zalles, Bracho Blanchet, Eduardo, Dávila Perez, Roberto, Fernandez Portilla, Emilio, Villegas Silva, Raúl, Ibarra, Daniel, Calderon Moore, Antonio, Carrasco-Ortega, Cesar, Noguez Castillo, Monica, Herappe Mellado, Dorihela, Yanowsky Reyes, Guillermo, Gonzalez Cortez, Luis Fernando, Santana Ortiz, Rafael, Orozco Perez, Jamie, Corona C.Rivera, Jorge Román, Cardenas Ruiz Velasco, Juan Jose, Quiles Corona, Moises, Peña Padilla, Christian, Bobadilla Morales, Lucina, Corona Rivera, Alfredo, Rios Flores, Izabel Maryalexandra, Aranda Sánchez, Cristian Irela, Ambriz-González, Gabriela, Martínez Hernández Magro, Nestor, León Frutos, Francisco Javier, Cárdenas Barón, José de Jesús, González Ojeda, Alejandro, Yarza Fernández, Jessica, Porras, Juan Domingo, Aguirre-Lopez, Pastor, Sánchez Paredes, Vicente, Montalvo Marin, Arturo, Diaz Gomez, Jose Manuel, Caamal, Lorenzo Juvencio, Bulnes Mendizabal, David, Sanchez Valladares, Pablo, Garcia Martinez, Humberto, Adesanya, Opeoluwa, Olanrewaju, Moses, 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Alkareem, Raghad mohammad abdu, Lahlooh, Raghad abdullateef, Halabi, Yasmeen Adly, Baker, Wisam, Almusleh, Tasneem Fathi Hasan, Tahyneh, Abdulraheem Adnan Abdulraheem, Atatri, Yazid yousef mahmoud, Jamie, Najlaa Abu, Massry, Nasrallah Ashraf Al, Lubbad, Walaa, A.Nemer, Ayoub, Alser, Mohammed, Salha, Aya Azmi Shehda, Alnahhal, Khaled, Elmzyyen, Aya Mahmoud, Ghabayen, Amir Talat Sheda, Alamrain, Abdulwhhab Ayman Abu, Al-Shwaikh, Samar H., Elshaer, Omar Adly, Shaheen, Nureddin, Fares, Jehad, Dalloul, Hisham, Qawwash, Anas, Jayyab, Mustafa abu, Ashour, Dina Ayman, Shaheen, Ahmad Ashraf, Naim, Samy Rafat Ramadan, Shiha, Eman Abu, Dammagh, Nagham Mohammed Al, Almadhoun, Walaa, Al-Salhi, Ashraf Ayman, Hammato, Abdalkarim Yhya, Salim, Jamal Mohammed, Hasanain, Doaa Khalil, Alwadia, Soha Marwan Salem, Nassar, Ismail, Al-Attar, Hala M., Alshaikhkhalil, Haya Abdulnasser Ali, Jamie, Yasmin Mohammed Khalil Abu, Ashour, Yara shareef, Alijla, Sharif S., Tallaa, Mohamed Anwer El, Abuattaya, Adham 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David, Khamag, Omar, Rengura, Cecilia, Siyotula, Thozama, Jooma, Uzair, Delft, Dirk von, Arnold, Marion, Mangray, Hansraj, Harilal, Shamaman, Madziba, Sanele, Wijekoon, Naveen, Gamage, Tharanga, Bright, Benedict Paul, Abdulrahman, Alaa, Mohammed, Ola Ahmed Abdulmjeed, Salah, Mohammed, Ajwa, Ahmad Elian Abu, Morjan, Mohammed, Batal, Mohammad Mohannad, Faks, Vivian, Mouti, Mohamad Bassel, Assi, Ahmadfateh, Al-Mouakeh, Ahmad, Tarabishi, Ahmad Sankari, Aljarad, Ziad, Alhamid, Aos, Khorana, Jiraporn, Poocharoen, Wannisa, Liukitithara, Sirima, Sriniworn, Anan, Nuntasunti, Wasun, Ngerncham, Monawat, Phannua, Ratiyaporn, Thaiwatcharamas, Kanokrat, Tanming, Patchareeporn, Sahnoun, Lassaad, Kchiche, Nahla, Abdelmoumen, Roua, Eroğlu, Egemen, Ozen, Mehmet Ali, Cömert, Hatice Sonay Yalçın, İmamoğlu, Mustafa, Sarıhan, Haluk, Kader, Şebnem, Mutlu, Mehmet, Aslan, Yakup, Beşir, Ahmet, Geze, Şükran, Çekiç, Bahanur, Yalcinkaya, Ali, Sönmez, Kaan, Karabulut, Ramazan, Türkyılmaz, Zafer, Şeref, Kıvanç, Altın, Merve, Aykut, Merve, Akan, M.Eren, Erdem, Melisa, Ergenekon, Ebru, Türkyılmaz, Canan, Keleş, Elif, Canözer, Ali, Yeniay, Aslı Öztürk, Eren, Elif, Cesur, İlknur Banlı, Özçelik, Zerrin, Kurt, Gökmen, Mert, Mustafa Kurthan, Kaya, Hatice, Çelik, Müge, Karakus, Suleyman Cuneyt, Erturk, Nazile, Suzen, Alev, Hakan, Nilay, Akova, Fatih, Pasaoglu, Mehmet, Eshkabilov, Shukurali, Yuldashev, Rustam Z., Abdunomonovich, Dekhkonboev Avazjon, Muslimovich, Aliev Makhmudjan, Patel, Azad, Kapihya, Chisengo, Ensar, Nicholas, Nataraja, Ramesh M, Sivasubramaniam, Mithila, Jones, Matthew, Teague, Warwick, Tanny, Sharman Tan, Thomas, Gordon, Roberts, Kiera, Venkatraman, Soundappan Sannappa, Till, Holger, Pigeolet, Manon, Dassonville, Martine, Shikha, Anas, Win, Win Sabai Phyu, Ahmad, Zahidah Adlynee Haji, Meloche-Dumas, Léamarie, Caouette-Laberge, Louise, St-Vil, Dickens, Aspirot, Ann, Piché, Nelson, Joharifard, Shahrzad, Safa, Nadia, Laberge, Jean-Martin, Emil, Sherif, Puligandla, Pramod, Shaw, Kenneth, Wissanji, Hussein, Duggan, Eileen, Guadagno, Elena, Puentes, Maria Consuelo, Leal, Paola Osses, Mendez Benavente, Carolina, Rygl, Michal, Trojanová, Barbora, Berková, Klára, Racková, Tereza, Planka, Ladislav, Škvařil, Jan, Štichhauer, Radek, Sabti, Shahad, Macdonald, Alex, Bouhadiba, Nordeen, Kufeji, Dorothy, Pardy, Caroline, Mccluney, Simon, Keshtgar, Alireza, Roberts, Rebecca, Rhodes, Hannah, Burns, Kate, Garrett-Cox, Robin, Ford, Kat, Cornwall, Hannah, Ravi, Krithi, Arthur, Felicity, Losty, Paul, Lander, Tony, Jester, Ingo, Arul, Suren, Gee, Oliver, Soccorso, Giampiero, Singh, Michael, Pachl, Max, Martin, Benjamin, Alzubair, Afnan, Kelay, Arun, Sutcliffe, Jonathan, Middleton, Thomas, Thomas, Amy Hughes, Kurian, Merina, Cameron, Fraser, Sivaraj, Jayaram, Thomas, Mark C, Rex, Dean, Jones, Ceri, Bradshaw, Kate, Bonnard, Arnaud, Delforge, Xavier, Duchesne, Camille, Gall, Caroline Le, Defert, Coralie, Laraqui Hossini, Samia, Guerin, Florent, Hery, Géraldine, Fouquet-Languillat, Virginie, Kohaut, Jules, Broch, Aline, Blanc, Thomas, Harper, Luke, Delefortrie, Thomas, Ballouhey, Quentin, Fourcade, Laurent, Grosos, Céline, Parmentier, Benoit, Levard, Guillaume, Grella, Maria Giovanna, Renaux Petel, Mariette, Grynberg, Lucie, Abbo, Olivier, Mouttalib, Sofia, Juricic, Mélodie, Scalabre, Aurelien, Haraux, Elodie, Rissmann, Anke, Krause, Hardy, Goebel, Peter, Patzer, Ludwig, Rolle, Udo, Schmedding, Andrea, Antunez-Mora, Alexandra, Tillig, Bernd, Bismarck, Sylvester von, Barbosa, Patricia Reis, Knorr, Christian, Stark, Domitille, Brunero, Marco, Avolio, Luigi, Manni, Francesco, Molinelli, Matilde, Guazzotti, Marinella, Raffaele, Alessandro, Romano, Piero Giovanni, Cavaiuolo, Silvia, Parigi, Gian Battista, Juhasz, Laszlo, Rieth, Anna, Strumila, Arunas, Dagilytė, Rūta, Liubsys, Arunas, Gurskas, Pranas, Malcius, Dalius, Mikneviciute, Agne, Vinskaite, Asta, Barauskas, Vidmantas, Vierboom, Liam, Hall, Timothy, Beasley, Spencer, Goddard, Lucy, Stringer, Mark, Weeratunga, Naveen, Adams, Stephen, Cama, Jitoko, Wong, Marilyn, Jayaratnam, Sridharan, Kukkady, Askar, Samarakkody, Udaya, Gerus, Sylwester, Patkowski, Dariusz, Wolny, Agnieszka, Koszutski, Tomasz, Tobor, Szymon, Osowicka, Marta, Czauderna, Piotr, Wyrzykowski, Dariusz, Garnier, Hanna, Anzelewicz, Stefan, Marta, Osowicka, Knurowska, Agata, Weiszewsk, Alicja, Grabowski, Andrzej, Korlacki, Wojciech, Pasierbek, Michal, Wolak, Przemyslaw, Piotrowska, Aneta, Roszkiewicz, Anna, Kalicińsk, Piotr, Trypens, Agata, Kowalewsk, Grzegorz, Sigalet, David, Alsaied, Amer, Ali, Mansour, Alsaggaf, Ameen, Ghallab, Alaa, Owiwi, Yazeed, Zeinelabdeen, Ali, Fayez, Mohamed, Atta, Ahmed, Zidan, Mazen, Radwan, Asaad saleh, Shalaby, Hanin, Abdelbaqi, Reem, Alattas, Khalid, Kano, Yar, Sindi, Omar, Alshehri, Abdullah, Altokhais, Tariq, Alturki, Fahad, Almosaibli, Mohammad, Krisanova, Dasha, Abbas, Wisam, Yang, Hee-Beom, Kim, Hyun-Young, Youn, Joong Kee, Chung, Jae Hee, Cho, Seok Hyeon, Hwang, In ji, Lee, Ju yeon, Song, Eung song, Arboleda, Jenny, Ruiz de Temiño Bravo, Mercedes, Siles Hinojosa, Alexander, García, Miriam, Casal Beloy, Isabel, Oliu San Miguel, Detlef, Molina Vazquez, Maria Elena, Alonso, Verónica, Sanchez, Alberto, Gomez, Oscar, Carrillo, Isabel, Wester, Tomas, Mesas Burgos, Carmen, Salö, Martin, Omling, Erik, Rudolfson, Niclas, Granéli, Christina, Arnadóttir, Helena, Grottling, Emma, Abrahamsson, Kate, Gatzinsky, Vladimir, Dellenmark Blom, Michaela, Borbonet, Daniel, Puglia, Paul, Jimenez Morejon, Vinicio, Acuna, Gaston, Moraes, Mario, Chan, Jonathan, Brahmamdam, Pavan, Tom, Alan, Sherer, Karen, Gonzales, Brandy, Cunningham, Aaron, Krishnaswami, Sanjay, Baertschiger, Reto, Leech, Mary, Williams, Regan, Camp, Lauren, Gosain, Ankush, Mora, Maria, Lyttle, Bailey D., Chang, Jeremy, McColl Makepeace, Lydia, Fowler, Kathryn L, Mansfield, Sara, Hodgman, Erica, Amaechi, Chukwubinyelum, Beres, Alana, Pernik, Mark N., Dosselman, Luke J., Almasri, Murad, Jain, Sunil, Modi, Varun, Fernandez Ferrer, Marianelly, Coon, John, Gonzalez, Joann, Honhar, Medhavi, Ruzgar, Nensi, Coghill, Griffin, Ullrich, Sarah, Cheung, Maija, Løfberg, Katrine, Greenberg, Jodie, Davenport, Kate, Gadepalli, Samir, Fox, Sarah, Johnson, Stephanie, Pilkington, Mercedes, Hamilton, April, Lin, Nicole, Sola, Juan, Yao, Yang, Davis, Jenna Kylene, Langer, Monica, Vacek, Jonathan, Abdullah, Fizan, Khlevner, Julie, Middlesworth, William, Levitt, Marc, Ahmad, Hira, Siddiqui, Sabina M, Bowder, Alex, Derks, Terry, Amoabin, Afua Amoabin, Pinar, Brooke, Owusu-Sekyere, Frank, Saousen, Benmanseur, Naidoo, Rasika, Karamustafic, Azra, Oliveira, Danielle Paula de, Motter, Sarah Bueno, Andrade, Jerhy, Šafus, Antonín, Langley, Jason, Wilke, Alexandra, Deya, Corazone, Murtadi, Habib Mansour, Berzanskis, Mindaugas, Calistus, Nwachukwu, Ajiboye, Olalekan S., Felix, Michael, Olabisi, Osagie O, Erçin, Seçil, Muradi, Teymursha, Burks, Stephen S., Lerma, Sergio, Jacobson, Jillian, Calancea, Calin, Valerio-Vazquez, Rafael, Sikwete, Guigui, Sekyere, Owusu, Mbonisweni, Akhona, Syed, Shahnoor, Hyeon, Cho Seok, Pajouhandeh, Fatemeh, and Kunfah, Sheba Mary Pognaa

Published
2021
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23. A Population Model of Time-Dependent Changes in Serum Creatinine in (Near)term Neonates with Hypoxic-Ischemic Encephalopathy During and After Therapeutic Hypothermia

Author

MS Neonatologie, Brain, Child Health, Regenerative Medicine and Stem Cells, Krzyzanski, Wojciech, Wintermark, Pia, Annaert, Pieter, Groenendaal, Floris, Şahin, Suzan, Öncel, Mehmet Yekta, Armangil, Didem, Koc, Esin, Battin, Malcolm R, Gunn, Alistair J, Frymoyer, Adam, Chock, Valerie Y-L, Keles, Elif, Mekahli, Djalila, van den Anker, John, Smits, Anne, Allegaert, Karel, MS Neonatologie, Brain, Child Health, Regenerative Medicine and Stem Cells, Krzyzanski, Wojciech, Wintermark, Pia, Annaert, Pieter, Groenendaal, Floris, Şahin, Suzan, Öncel, Mehmet Yekta, Armangil, Didem, Koc, Esin, Battin, Malcolm R, Gunn, Alistair J, Frymoyer, Adam, Chock, Valerie Y-L, Keles, Elif, Mekahli, Djalila, van den Anker, John, Smits, Anne, and Allegaert, Karel

Published
2024

25. A Population Model of Time-Dependent Changes in Serum Creatinine in (Near)term Neonates with Hypoxic-Ischemic Encephalopathy During and After Therapeutic Hypothermia

Author

Krzyzanski, Wojciech, primary, Wintermark, Pia, additional, Annaert, Pieter, additional, Groenendaal, Floris, additional, Şahin, Suzan, additional, Öncel, Mehmet Yekta, additional, Armangil, Didem, additional, Koc, Esin, additional, Battin, Malcolm R., additional, Gunn, Alistair J., additional, Frymoyer, Adam, additional, Chock, Valerie Y.-L., additional, Keles, Elif, additional, Mekahli, Djalila, additional, van den Anker, John, additional, Smits, Anne, additional, and Allegaert, Karel, additional

Published
2023
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30. A Population Model of Time-Dependent Changes in Serum Creatinine in (Near)term Neonates with Hypoxic-Ischemic Encephalopathy During and After Therapeutic Hypothermia

Author

Krzyzanski, Wojciech, Wintermark, Pia, Annaert, Pieter, Groenendaal, Floris, Şahin, Suzan, Öncel, Mehmet Yekta, Armangil, Didem, Koc, Esin, Battin, Malcolm R., Gunn, Alistair J., Frymoyer, Adam, Chock, Valerie Y.L., Keles, Elif, Mekahli, Djalila, van den Anker, John, Smits, Anne, Allegaert, Karel, Krzyzanski, Wojciech, Wintermark, Pia, Annaert, Pieter, Groenendaal, Floris, Şahin, Suzan, Öncel, Mehmet Yekta, Armangil, Didem, Koc, Esin, Battin, Malcolm R., Gunn, Alistair J., Frymoyer, Adam, Chock, Valerie Y.L., Keles, Elif, Mekahli, Djalila, van den Anker, John, Smits, Anne, and Allegaert, Karel

Abstract

The objective was to apply a population model to describe the time course and variability of serum creatinine (sCr) in (near)term neonates with moderate to severe encephalopathy during and after therapeutic hypothermia (TH). The data consisted of sCr observations up to 10 days of postnatal age in neonates who underwent TH during the first 3 days after birth. Available covariates were birth weight (BWT), gestational age (GA), survival, and acute kidney injury (AKI). A previously published population model of sCr kinetics in neonates served as the base model. This model predicted not only sCr but also the glomerular filtration rate normalized by its value at birth (GFR/GFR0). The model was used to compare the TH neonates with a reference full term non-asphyxiated population of neonates. The estimates of the model parameters had good precision and showed high between subject variability. AKI influenced most of the estimated parameters denoting a strong impact on sCr kinetics and GFR. BWT and GA were not significant covariates. TH transiently increased sCr in TH neonates over the first days compared to the reference group. Asphyxia impacted not only GFR, but also the sCr synthesis rate. We also observed that AKI neonates exhibit a delayed onset of postnatal GFR increase and have a higher sCr synthesis rate compared to no-AKI patients. Our findings show that the use of sCr as marker of renal function in asphyxiated neonates treated with TH to guide dose selection for renally cleared drugs is challenging, while we captured the postnatal sCr patterns in this specific population. Graphical Abstract: [Figure not available: see fulltext.].

Published
2023

33. Proceedings of the 13th International Newborn Brain Conference: Neuroprotection strategies in the neonate

Author

Abou Mehrem, Ayman, Al Awad, Essa, Anninck, Kim, Au-Young, Stephanie, Aydinol, Nesil, Bartmann, Peter, Benders, Manon, Benlamri, Amina, Bolderheij, Leah, Celik, Yalcin, Chan, Natalie, Chau, Cecil, Chau, Vann, Chen, Xiaodi, Chetcuti Ganado, Claudia, Coetser, Annaleen, Cools, Filip, Da Rocha, Giselle, Deigner, Hans-Peter, Dereymaeker, Anneleen, Deshmukh, Laxmikant, Domonoske, Rose, Dossani, Salma, Dsouza, Jeanne M, El Gamal, Mayy, Eshemokhai, Peace, Esser, Michael, Fiedrich, Elsa, Franz, Axel, Ghosh, Anirban, Groenendaal, Floris, Grunau, Ruth E, Venkata, Sujith Kumar Reddy Gurram, Hamitoglu, Serif, Hellström-Westas, Lena, Irvine, Leigh, Jansen, Katrien, Javadyan, Artem, Jenkin, Graham, Kamanga, Noela, Kaur, Navneet, Keles, Elif, Keller, Matthias, Kelly, Edmond, Kesting, Samantha Jane, Kgwadi, Dikeledi, Kim, Boram, Kohl, Matthias, Kowal, Derek, Kricitober, Jennifer Damoi, Leijser, Lara, LePine, Mark, Lim, Yow-Pin, Lodha, Abhay, Londhe, Atul, Ly, Linh, Maes, Eva, Malhotra, Atul, Marlow, Neil, Mathew, Joseph L, McDonald, Courtney, McLean, Mia, Metcalfe, Cathy, Meyer, Ron, Miller, Steven P, Miller, Suzanne, Mogajane, Tshiamo, Mohammad, Khorshid, Momin, Sarfaraz, Montpetit, John, Mukiza, Nelson, Murthy, Prashanth, Scott, James N, Nakibuuka, Victoria, Nakwa, Firdose, Naulaers, Gunnar, Noort, Jennessa, Ntuli, Nandi, Ondongo-Ezhet, Claude, Paul, Renee, Pepper, Michael, Plum, Achim, Rombough, Bryan, Saugstad, Ola, Scotland, Jillian, Scott, James, Seake, Karabo, Sebunya, Robert, Selvanathan, Thiviya, Sepeng, Letlhogonolo, Simsek, Huseyin, Steins-Rang, Carola, Stonestreet, Barbara, Tang, Selphee, Taskin, Erdal, Thewissen, Liesbeth, Thomas, Sumesh, Thomas, Reenu, van Kwawegen, Alison, van Rensburg, Jeanne, Velaphi, Sithembiso, Wu, Yuqi, Yaman, Akan, Yapicioglu-Yildizdas, Hacer, Yawno, Tamara, Zaki, Pearl, Zein, Hussein, and Zhou, Lindsay

Subjects
Infant, Newborn, Brain, Humans, Head, Neuroprotection
Abstract

ispartof: J Neonatal Perinatal Med vol:15 issue:2 pages:427-439 ispartof: location:Netherlands status: published

Published
2022

34. Serum Creatinine Patterns in Neonates Treated with Therapeutic Hypothermia for Neonatal Encephalopathy

Author

Keles, Elif, Wintermark, Pia, Groenendaal, Floris, Borloo, Noor, Smits, Anne, Laenen, Annouschka, Mekahli, Djalila, Annaert, Pieter, Şahin, Suzan, Öncel, Mehmet Yekta, Chock, Valerie, Armangil, Didem, Koc, Esin, Battin, Malcolm R., Frymoyer, Adam, Allegaert, Karel, Keles, Elif, Wintermark, Pia, Groenendaal, Floris, Borloo, Noor, Smits, Anne, Laenen, Annouschka, Mekahli, Djalila, Annaert, Pieter, Şahin, Suzan, Öncel, Mehmet Yekta, Chock, Valerie, Armangil, Didem, Koc, Esin, Battin, Malcolm R., Frymoyer, Adam, and Allegaert, Karel

Abstract

Introduction: There is large variability in kidney function and injury in neonates with neonatal encephalopathy (NE) treated with therapeutic hypothermia (TH). Acute kidney injury (AKI) definitions that apply categorical approaches may lose valuable information about kidney function in individual patients. Centile serum creatinine (SCr) over postnatal age (PNA) may provide more valuable information in TH neonates. Methods: Data from seven TH neonates and one non-TH-treated, non-NE control cohorts were pooled in a retrospective study. SCr centiles over PNA, and AKI incidence (definition: SCr ↑≥0.3 mg/dL within 48 h, or ↑ ≥1.5 fold vs. the lowest prior SCr within 7 days) and mortality were calculated. Repeated measurement linear models were applied to SCr trends, modeling SCr on PNA, birth weight or gestational age (GA), using heterogeneous autoregressive residual covariance structure and maximum likelihood methods. Findings were compared to patterns in the control cohort. Results: Among 1,136 TH neonates, representing 4,724 SCr observations, SCr (10th-25th-50th-75th-90th-95th) PNA centiles (day 1-10) were generated. In TH neonates, the AKI incidence was 132/1,136 (11.6%), mortality 193/1,136 (17%). AKI neonates had a higher mortality (37.2-14.3%, p < 0.001). Median SCr patterns over PNA were significantly higher in nonsurvivors (p < 0.01) or AKI neonates (p < 0.001). In TH-treated neonates, PNA and GA or birth weight explained SCr variability. Patterns over PNA were significantly higher in TH neonates to controls (801 neonates, 2,779 SCr). Conclusions: SCr patterns in TH-treated NE neonates are specific. Knowing PNA-related patterns enable clinicians to better assess kidney function and tailor pharmacotherapy, fluids, or kidney supportive therapies.

Published
2022

35. Rescaling Creatinine Centiles in Neonates Treated with Therapeutic Hypothermia for Neonatal Encephalopathy

Author

Allegaert, Karel, Mekahli, Djalila, Wintermark, Pia, Groenendaal, Floris, Borloo, Noor, Laenen, Annouschka, Annaert, Pieter, Şahin, Suzan, Öncel, Mehmet Yekta, Chock, Valerie Y., Armangil, Didem, Koc, Esin, Battin, Malcolm R., Frymoyer, Adam, Keles, Elif, Smits, Anne, Allegaert, Karel, Mekahli, Djalila, Wintermark, Pia, Groenendaal, Floris, Borloo, Noor, Laenen, Annouschka, Annaert, Pieter, Şahin, Suzan, Öncel, Mehmet Yekta, Chock, Valerie Y., Armangil, Didem, Koc, Esin, Battin, Malcolm R., Frymoyer, Adam, Keles, Elif, and Smits, Anne

Published
2022

36. Rescaling Creatinine Centiles in Neonates Treated with Therapeutic Hypothermia for Neonatal Encephalopathy

Author

MS Neonatologie, Brain, Child Health, Regenerative Medicine and Stem Cells, Allegaert, Karel, Mekahli, Djalila, Wintermark, Pia, Groenendaal, Floris, Borloo, Noor, Laenen, Annouschka, Annaert, Pieter, Şahin, Suzan, Öncel, Mehmet Yekta, Chock, Valerie Y, Armangil, Didem, Koc, Esin, Battin, Malcolm R, Frymoyer, Adam, Keles, Elif, Smits, Anne, MS Neonatologie, Brain, Child Health, Regenerative Medicine and Stem Cells, Allegaert, Karel, Mekahli, Djalila, Wintermark, Pia, Groenendaal, Floris, Borloo, Noor, Laenen, Annouschka, Annaert, Pieter, Şahin, Suzan, Öncel, Mehmet Yekta, Chock, Valerie Y, Armangil, Didem, Koc, Esin, Battin, Malcolm R, Frymoyer, Adam, Keles, Elif, and Smits, Anne

Published
2022

37. Serum Creatinine Patterns in Neonates Treated with Therapeutic Hypothermia for Neonatal Encephalopathy

Author

MS Neonatologie, Brain, Child Health, Regenerative Medicine and Stem Cells, Keles, Elif, Wintermark, Pia, Groenendaal, Floris, Borloo, Noor, Smits, Anne, Laenen, Annouschka, Mekahli, Djalila, Annaert, Pieter, Şahin, Suzan, Öncel, Mehmet Yekta, Chock, Valerie, Armangil, Didem, Koc, Esin, Battin, Malcolm R, Frymoyer, Adam, Allegaert, Karel, MS Neonatologie, Brain, Child Health, Regenerative Medicine and Stem Cells, Keles, Elif, Wintermark, Pia, Groenendaal, Floris, Borloo, Noor, Smits, Anne, Laenen, Annouschka, Mekahli, Djalila, Annaert, Pieter, Şahin, Suzan, Öncel, Mehmet Yekta, Chock, Valerie, Armangil, Didem, Koc, Esin, Battin, Malcolm R, Frymoyer, Adam, and Allegaert, Karel

Published
2022

38. Proceedings of the 13th International Newborn Brain Conference: Neuroprotection strategies in the neonate

Author

MS Neonatologie, Brain, Child Health, Regenerative Medicine and Stem Cells, Abou Mehrem, Ayman, Al Awad, Essa, Anninck, Kim, Au-Young, Stephanie, Aydinol, Nesil, Bartmann, Peter, Benders, Manon, Benlamri, Amina, Bolderheij, Leah, Celik, Yalcin, Chan, Natalie, Chau, Cecil, Chau, Vann, Chen, Xiaodi, Chetcuti Ganado, Claudia, Coetser, Annaleen, Cools, Filip, Da Rocha, Giselle, Deigner, Hans Peter, Dereymaeker, Anneleen, Deshmukh, Laxmikant, Domonoske, Rose, Dossani, Salma, Dsouza, Jeanne M., El Gamal, Mayy, Eshemokhai, Peace, Esser, Michael, Fiedrich, Elsa, Franz, Axel, Ghosh, Anirban, Groenendaal, Floris, Grunau, Ruth E., Venkata, Sujith Kumar Reddy Gurram, Hamitoglu, Serif, Hellström-Westas, Lena, Irvine, Leigh, Jansen, Katrien, Javadyan, Artem, Jenkin, Graham, Kamanga, Noela, Kaur, Navneet, Keles, Elif, Keller, Matthias, Kelly, Edmond, Kesting, Samantha Jane, Kgwadi, Dikeledi, Kim, Boram, Kohl, Matthias, Kowal, Derek, Kricitober, Jennifer Damoi, Leijser, Lara, LePine, Mark, Lim, Yow Pin, Lodha, Abhay, Londhe, Atul, Ly, Linh, Maes, Eva, Malhotra, Atul, Marlow, Neil, Mathew, Joseph L., McDonald, Courtney, McLean, Mia, Metcalfe, Cathy, Meyer, Ron, Miller, Steven P., Miller, Suzanne, Mogajane, Tshiamo, Mohammad, Khorshid, Momin, Sarfaraz, Montpetit, John, Mukiza, Nelson, Murthy, Prashanth, Scott, James N., Nakibuuka, Victoria, Nakwa, Firdose, Naulaers, Gunnar, Noort, Jennessa, Ntuli, Nandi, Ondongo-Ezhet, Claude, Paul, Renee, Pepper, Michael, Plum, Achim, Rombough, Bryan, Saugstad, Ola, Scotland, Jillian, Scott, James, Seake, Karabo, Sebunya, Robert, Selvanathan, Thiviya, Sepeng, Letlhogonolo, Simsek, Huseyin, Steins-Rang, Carola, Stonestreet, Barbara, Tang, Selphee, Taskin, Erdal, Thewissen, Liesbeth, Thomas, Sumesh, Thomas, Reenu, van Kwawegen, Alison, van Rensburg, Jeanne, Velaphi, Sithembiso, Wu, Yuqi, Yaman, Akan, Yapicioglu-Yildizdas, Hacer, Yawno, Tamara, Zaki, Pearl, Zein, Hussein, Zhou, Lindsay, MS Neonatologie, Brain, Child Health, Regenerative Medicine and Stem Cells, Abou Mehrem, Ayman, Al Awad, Essa, Anninck, Kim, Au-Young, Stephanie, Aydinol, Nesil, Bartmann, Peter, Benders, Manon, Benlamri, Amina, Bolderheij, Leah, Celik, Yalcin, Chan, Natalie, Chau, Cecil, Chau, Vann, Chen, Xiaodi, Chetcuti Ganado, Claudia, Coetser, Annaleen, Cools, Filip, Da Rocha, Giselle, Deigner, Hans Peter, Dereymaeker, Anneleen, Deshmukh, Laxmikant, Domonoske, Rose, Dossani, Salma, Dsouza, Jeanne M., El Gamal, Mayy, Eshemokhai, Peace, Esser, Michael, Fiedrich, Elsa, Franz, Axel, Ghosh, Anirban, Groenendaal, Floris, Grunau, Ruth E., Venkata, Sujith Kumar Reddy Gurram, Hamitoglu, Serif, Hellström-Westas, Lena, Irvine, Leigh, Jansen, Katrien, Javadyan, Artem, Jenkin, Graham, Kamanga, Noela, Kaur, Navneet, Keles, Elif, Keller, Matthias, Kelly, Edmond, Kesting, Samantha Jane, Kgwadi, Dikeledi, Kim, Boram, Kohl, Matthias, Kowal, Derek, Kricitober, Jennifer Damoi, Leijser, Lara, LePine, Mark, Lim, Yow Pin, Lodha, Abhay, Londhe, Atul, Ly, Linh, Maes, Eva, Malhotra, Atul, Marlow, Neil, Mathew, Joseph L., McDonald, Courtney, McLean, Mia, Metcalfe, Cathy, Meyer, Ron, Miller, Steven P., Miller, Suzanne, Mogajane, Tshiamo, Mohammad, Khorshid, Momin, Sarfaraz, Montpetit, John, Mukiza, Nelson, Murthy, Prashanth, Scott, James N., Nakibuuka, Victoria, Nakwa, Firdose, Naulaers, Gunnar, Noort, Jennessa, Ntuli, Nandi, Ondongo-Ezhet, Claude, Paul, Renee, Pepper, Michael, Plum, Achim, Rombough, Bryan, Saugstad, Ola, Scotland, Jillian, Scott, James, Seake, Karabo, Sebunya, Robert, Selvanathan, Thiviya, Sepeng, Letlhogonolo, Simsek, Huseyin, Steins-Rang, Carola, Stonestreet, Barbara, Tang, Selphee, Taskin, Erdal, Thewissen, Liesbeth, Thomas, Sumesh, Thomas, Reenu, van Kwawegen, Alison, van Rensburg, Jeanne, Velaphi, Sithembiso, Wu, Yuqi, Yaman, Akan, Yapicioglu-Yildizdas, Hacer, Yawno, Tamara, Zaki, Pearl, Zein, Hussein, and Zhou, Lindsay

Published
2022

41. Serum Creatinine Patterns in Neonates Treated with Therapeutic Hypothermia for Neonatal Encephalopathy

Author

Keles, Elif, primary, Wintermark, Pia, additional, Groenendaal, Floris, additional, Borloo, Noor, additional, Smits, Anne, additional, Laenen, Annouschka, additional, Mekahli, Djalila, additional, Annaert, Pieter, additional, Şahin, Suzan, additional, Öncel, Mehmet Yekta, additional, Chock, Valerie, additional, Armangil, Didem, additional, Koc, Esin, additional, Battin, Malcolm R., additional, Frymoyer, Adam, additional, and Allegaert, Karel, additional

Published
2022
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42. Rescaling Creatinine Centiles in Neonates Treated with Therapeutic Hypothermia for Neonatal Encephalopathy

Author

Allegaert, Karel, primary, Mekahli, Djalila, additional, Wintermark, Pia, additional, Groenendaal, Floris, additional, Borloo, Noor, additional, Laenen, Annouschka, additional, Annaert, Pieter, additional, Şahin, Suzan, additional, Öncel, Mehmet Yekta, additional, Chock, Valerie Y., additional, Armangil, Didem, additional, Koc, Esin, additional, Battin, Malcolm R., additional, Frymoyer, Adam, additional, Keles, Elif, additional, and Smits, Anne, additional

Published
2022
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