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2,626 results on '"Abbosh, A."'

1. The interplay of mutagenesis and ecDNA shapes urothelial cancer evolution

Author

Nguyen, Duy D., Hooper, William F., Liu, Weisi, Chu, Timothy R., Geiger, Heather, Shelton, Jennifer M., Shah, Minita, Goldstein, Zoe R., Winterkorn, Lara, Helland, Adrienne, Sigouros, Michael, Manohar, Jyothi, Moyer, Jenna, Al Assaad, Majd, Semaan, Alissa, Cohen, Sandra, Madorsky Rowdo, Florencia, Wilkes, David, Osman, Mohamed, Singh, Rahul R., Sboner, Andrea, Valentine, Henkel L., Abbosh, Phillip, Tagawa, Scott T., Nanus, David M., Nauseef, Jones T., Sternberg, Cora N., Molina, Ana M., Scherr, Douglas, Inghirami, Giorgio, Mosquera, Juan Miguel, Elemento, Olivier, Robine, Nicolas, and Faltas, Bishoy M.

Published
2024
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2. Next-generation MRD assays: do we have the tools to evaluate them properly?

Author

Stetson, Dan, Labrousse, Paul, Russell, Hugh, Shera, David, Abbosh, Chris, Dougherty, Brian, Barrett, J. Carl, Hodgson, Darren, and Hadfield, James

Subjects
Quantitative Biology - Other Quantitative Biology
Abstract

Circulating tumour DNA (ctDNA) detection of molecular residual disease (MRD) in solid tumours correlates strongly with patient outcomes and is being adopted as a new clinical standard. ctDNA levels are known to correlate with tumor volume, and although the absolute levels vary across indication and histology, its analysis is driving the adoption of MRD. MRD assays must detect tumor when imaging cannot and, as such, require very high sensitivity to detect the low levels of ctDNA found after curative intent therapy. The minimum threshold is 0.01% Tumour Fraction but current methods like Archer and Signatera are limited by detection sensitivity resulting in some patients receiving a false negative call thereby missing out on earlier therapeutic intervention. Multiple vendors are increasing the number of somatic variants tracked in tumour-informed and personalized NGS assays, from tens to thousands of variants. Most recently, assays using other biological features of ctDNA, e.g methylation or fragmentome, have been developed at the LOD required for clinical utility. These uniformed, or tumour-naive and non-personalised assays may be more easily, and therefore more rapidly, adopted in the clinic. However, this rapid development in MRD assay technology results in significant challenges in benchmarking these new technologies for use in clinical trials. This is further complicated by the fact that previous reference materials have focused on somatic variants, and do not retain all of the epigenomic features assessed by newer technologies. In this Comments and Controversy paper, we detail what is known and what remains to be determined for optimal reference materials of MRD methods and provide opinions generated during three-years of MRD technology benchmarking in AstraZeneca Translational Medicine to help guide the community conversation.

Published
2023

4. Multi-ancestry genome-wide association study of kidney cancer identifies 63 susceptibility regions

Author

Purdue, Mark P., Dutta, Diptavo, Machiela, Mitchell J., Gorman, Bryan R., Winter, Timothy, Okuhara, Dayne, Cleland, Sara, Ferreiro-Iglesias, Aida, Scheet, Paul, Liu, Aoxing, Wu, Chao, Antwi, Samuel O., Larkin, James, Zequi, Stênio C., Sun, Maxine, Hikino, Keiko, Hajiran, Ali, Lawson, Keith A., Cárcano, Flavio, Blanchet, Odile, Shuch, Brian, Nepple, Kenneth G., Margue, Gaëlle, Sundi, Debasish, Diver, W. Ryan, Folgueira, Maria A. A. K., van Bokhoven, Adrie, Neffa, Florencia, Brown, Kevin M., Hofmann, Jonathan N., Rhee, Jongeun, Yeager, Meredith, Cole, Nathan R., Hicks, Belynda D., Manning, Michelle R., Hutchinson, Amy A., Rothman, Nathaniel, Huang, Wen-Yi, Linehan, W. Marston, Lori, Adriana, Ferragu, Matthieu, Zidane-Marinnes, Merzouka, Serrano, Sérgio V., Magnabosco, Wesley J., Vilas, Ana, Decia, Ricardo, Carusso, Florencia, Graham, Laura S., Anderson, Kyra, Bilen, Mehmet A., Arciero, Cletus, Pellegrin, Isabelle, Ricard, Solène, Scelo, Ghislaine, Banks, Rosamonde E., Vasudev, Naveen S., Soomro, Naeem, Stewart, Grant D., Adeyoju, Adebanji, Bromage, Stephen, Hrouda, David, Gibbons, Norma, Patel, Poulam, Sullivan, Mark, Protheroe, Andrew, Nugent, Francesca I., Fournier, Michelle J., Zhang, Xiaoyu, Martin, Lisa J., Komisarenko, Maria, Eisen, Timothy, Cunningham, Sonia A., Connolly, Denise C., Uzzo, Robert G., Zaridze, David, Mukeria, Anush, Holcatova, Ivana, Hornakova, Anna, Foretova, Lenka, Janout, Vladimir, Mates, Dana, Jinga, Viorel, Rascu, Stefan, Mijuskovic, Mirjana, Savic, Slavisa, Milosavljevic, Sasa, Gaborieau, Valérie, Abedi-Ardekani, Behnoush, McKay, James, Johansson, Mattias, Phouthavongsy, Larry, Hayman, Lindsay, Li, Jason, Lungu, Ilinca, Bezerra, Stephania M., Souza, Aline G., Sares, Claudia T. G., Reis, Rodolfo B., Gallucci, Fabio P., Cordeiro, Mauricio D., Pomerantz, Mark, Lee, Gwo-Shu M., Freedman, Matthew L., Jeong, Anhyo, Greenberg, Samantha E., Sanchez, Alejandro, Thompson, R. Houston, Sharma, Vidit, Thiel, David D., Ball, Colleen T., Abreu, Diego, Lam, Elaine T., Nahas, William C., Master, Viraj A., Patel, Alpa V., Bernhard, Jean-Christophe, Freedman, Neal D., Bigot, Pierre, Reis, Rui M., Colli, Leandro M., Finelli, Antonio, Manley, Brandon J., Terao, Chikashi, Choueiri, Toni K., Carraro, Dirce M., Houlston, Richard, Eckel-Passow, Jeanette E., Abbosh, Philip H., Ganna, Andrea, Brennan, Paul, Gu, Jian, and Chanock, Stephen J.

Published
2024
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6. Clinical electromagnetic brain scanner

Author

Amin Abbosh, Konstanty Bialkowski, Lei Guo, Ahmed Al-Saffar, Ali Zamani, Adnan Trakic, Aida Brankovic, Alina Bialkowski, Guohun Zhu, David Cook, and Stuart Crozier

Subjects
Medicine, Science
Abstract

Abstract Stroke is a leading cause of death and disability worldwide, and early diagnosis and prompt medical intervention are thus crucial. Frequent monitoring of stroke patients is also essential to assess treatment efficacy and detect complications earlier. While computed tomography (CT) and magnetic resonance imaging (MRI) are commonly used for stroke diagnosis, they cannot be easily used onsite, nor for frequent monitoring purposes. To meet those requirements, an electromagnetic imaging (EMI) device, which is portable, non-invasive, and non-ionizing, has been developed. It uses a headset with an antenna array that irradiates the head with a safe low-frequency EM field and captures scattered fields to map the brain using a complementary set of physics-based and data-driven algorithms, enabling quasi-real-time detection, two-dimensional localization, and classification of strokes. This study reports clinical findings from the first time the device was used on stroke patients. The clinical results on 50 patients indicate achieving an overall accuracy of 98% in classification and 80% in two-dimensional quadrant localization. With its lightweight design and potential for use by a single para-medical staff at the point of care, the device can be used in intensive care units, emergency departments, and by paramedics for onsite diagnosis.

Published
2024
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7. Synthetic Microwave Focusing Techniques for Medical Imaging: Fundamentals, Limitations, and Challenges

Author

Younis M. Abbosh, Kamel Sultan, Lei Guo, and Amin Abbosh

Subjects
synthetic focusing, electromagnetic imaging, microwave imaging, medical imaging, delay and sum, confocal imaging, Biotechnology, TP248.13-248.65
Abstract

Synthetic microwave focusing methods have been widely adopted in qualitative medical imaging to detect and localize anomalies based on their electromagnetic scattering signatures. This paper discusses the principles, challenges, and limitations of synthetic microwave-focusing techniques in medical applications. It is shown that the various focusing techniques, including time reversal, confocal imaging, and delay-and-sum, are all based on the scalar solution of the electromagnetic scattering problem, assuming the imaged object, i.e., the tissue or object, is linear, reciprocal, and time-invariant. They all aim to generate a qualitative image, revealing any strong scatterer within the imaged domain. The differences among these techniques lie only in the assumptions made to derive the solution and create an image of the relevant tissue or object. To get a fast solution using limited computational resources, those methods assume the tissue is homogeneous and non-dispersive, and thus, a simplified far-field Green’s function is used. Some focusing methods compensate for dispersive effects and attenuation in lossy tissues. Other approaches replace the simplified Green’s function with more representative functions. While these focusing techniques offer benefits like speed and low computational requirements, they face significant ongoing challenges in real-life applications due to their oversimplified linear solutions to the complex problem of non-linear medical microwave imaging. This paper discusses these challenges and potential solutions.

Published
2024
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8. Author Correction: The evolution of lung cancer and impact of subclonal selection in TRACERx

Author

Frankell, Alexander M., Dietzen, Michelle, Al Bakir, Maise, Lim, Emilia L., Karasaki, Takahiro, Ward, Sophia, Veeriah, Selvaraju, Colliver, Emma, Huebner, Ariana, Bunkum, Abigail, Hill, Mark S., Grigoriadis, Kristiana, Moore, David A., Black, James R. M., Liu, Wing Kin, Thol, Kerstin, Pich, Oriol, Watkins, Thomas B. K., Naceur-Lombardelli, Cristina, Cook, Daniel E., Salgado, Roberto, Wilson, Gareth A., Bailey, Chris, Angelova, Mihaela, Bentham, Robert, Martínez-Ruiz, Carlos, Abbosh, Christopher, Nicholson, Andrew G., Le Quesne, John, Biswas, Dhruva, Rosenthal, Rachel, Puttick, Clare, Hessey, Sonya, Lee, Claudia, Prymas, Paulina, Toncheva, Antonia, Smith, Jon, Xing, Wei, Nicod, Jerome, Price, Gillian, Kerr, Keith M., Naidu, Babu, Middleton, Gary, Blyth, Kevin G., Fennell, Dean A., Forster, Martin D., Lee, Siow Ming, Falzon, Mary, Hewish, Madeleine, Shackcloth, Michael J., Lim, Eric, Benafif, Sarah, Russell, Peter, Boleti, Ekaterini, Krebs, Matthew G., Lester, Jason F., Papadatos-Pastos, Dionysis, Ahmad, Tanya, Thakrar, Ricky M., Lawrence, David, Navani, Neal, Janes, Sam M., Dive, Caroline, Blackhall, Fiona H., Summers, Yvonne, Cave, Judith, Marafioti, Teresa, Herrero, Javier, Quezada, Sergio A., Peggs, Karl S., Schwarz, Roland F., Van Loo, Peter, Miedema, Daniël M., Birkbak, Nicolai J., Hiley, Crispin T., Hackshaw, Allan, Zaccaria, Simone, Jamal-Hanjani, Mariam, McGranahan, Nicholas, and Swanton, Charles

Published
2024
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10. Transcriptome- and proteome-wide association studies identify genes associated with renal cell carcinoma

Author

Purdue, Mark P., Dutta, Diptavo, Machiela, Mitchell J., Gorman, Bryan R., Winter, Timothy, Okuhara, Dayne, Cleland, Sara, Ferreiro-Iglesias, Aida, Scheet, Paul, Liu, Aoxing, Wu, Chao, Antwi, Samuel O., Larkin, James, Zequi, Stênio C., Sun, Maxine, Hikino, Keiko, Hajiran, Ali, Lawson, Keith A., Cárcano, Flavio, Blanchet, Odile, Shuch, Brian, Nepple, Kenneth G., Margue, Gaëlle, Sundi, Debasish, Diver, W. Ryan, Folgueira, Maria A.A.K., van Bokhoven, Adrie, Neffa, Florencia, Brown, Kevin M., Hofmann, Jonathan N., Rhee, Jongeun, Yeager, Meredith, Cole, Nathan R., Hicks, Belynda D., Manning, Michelle R., Hutchinson, Amy A., Rothman, Nathaniel, Huang, Wen-Yi, Linehan, W. Marston, Lori, Adriana, Ferragu, Matthieu, Zidane-Marinnes, Merzouka, Serrano, Sérgio, Magnabosco, Wesley J., BioBank Japan Project Consortium, Vilas, Ana, Decia, Ricardo, Carusso, Florencia, Graham, Laura S., Anderson, Kyra, Bilen, Mehmet A., Arciero, Cletus, Pellegrin, Isabelle, Ricard, Solène, FinnGen, Scelo, Ghislaine, Banks, Rosamonde E., Vasudev, Naveen S., Soomro, Naeem, Stewart, Grant D., Adeyoju, Adebanji, Bromage, Stephen, Hrouda, David, Gibbons, Norma, Patel, Poulam, Sullivan, Mark, Protheroe, Andrew, Nugent, Francesca I., Fournier, Michelle J., Zhang, Xiaoyu, Martin, Lisa J., Komisarenko, Maria, Eisen, Timothy, Cunningham, Sonia A., Connolly, Denise C., Uzzo, Robert G., Zaridze, David, Mukeria, Anush, Holcatova, Ivana, Hornakova, Anna, Foretova, Lenka, Janout, Vladimir, Mates, Dana, Jinga, Viorel, Rascu, Stefan, Mijuskovic, Mirjana, Savic, Slavisa, Milosavljevic, Sasa, Gaborieau, Valérie, Abedi-Ardekani, Behnoush, McKay, James, Johansson, Mattias, Phouthavongsy, Larry, Hayman, Lindsay, Li, Jason, Lungu, Ilinca, Bezerra, Stephania M., de Souza, Aline G., Sares, Claudia T.G., Reis, Rodolfo B., Gallucci, Fabio P., Cordeiro, Mauricio D., Pomerantz, Mark, Lee, Gwo-Shu M., Freedman, Matthew L., Jeong, Anhyo, Greenberg, Samantha E., Sanchez, Alejandro, Thompson, R. Houston, Sharma, Vidit, Thiel, David D., Ball, Colleen T., Abreu, Diego, Lam, Elaine T., Nahas, William C., Master, Viraj A., Patel, Alpa V., Bernhard, Jean-Christophe, Freedman, Neal D., Bigot, Pierre, Reis, Rui M., Colli, Leandro M., Finelli, Antonio, Manley, Brandon J., Terao, Chikashi, Choueiri, Toni K., Carraro, Dirce M., Houlston, Richard, Eckel-Passow, Jeanette E., Abbosh, Philip H., Ganna, Andrea, Brennan, Paul, Gu, Jian, Chanock, Stephen J., Guo, Xinyu, Winter, Timothy D., Jahagirdar, Om, Ha, Eunji, and Susztak, Katalin

Published
2024
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11. Comprehensive review of deep learning in orthopaedics: Applications, challenges, trustworthiness, and fusion

Author

Alzubaidi, Laith, AL-Dulaimi, Khamael, Salhi, Asma, Alammar, Zaenab, Fadhel, Mohammed A., Albahri, A.S., Alamoodi, A.H., Albahri, O.S., Hasan, Amjad F., Bai, Jinshuai, Gilliland, Luke, Peng, Jing, Branni, Marco, Shuker, Tristan, Cutbush, Kenneth, Santamaría, Jose, Moreira, Catarina, Ouyang, Chun, Duan, Ye, Manoufali, Mohamed, Jomaa, Mohammad, Gupta, Ashish, Abbosh, Amin, and Gu, Yuantong

Published
2024
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14. Urine Biopsy as Dynamic Biomarker to Enhance Clinical Staging of Bladder Cancer in Radical Cystectomy Candidates

Author

Satyal, Uttam, Valentine, Henkel, Liu, David, Slifker, Michael, Lallas, Costas D., Trabulsi, Edouard J., Bukavina, Laura, Szeto, Lauren, Hoffman-Censits, Jean H., Mouw, Kent W., Faltas, Bishoy M., Grivas, Petros, Ibragimova, Ilsiya, Porten, Sima P., Van Allen, Eliezer M., Geynisman, Daniel M., Parker, Daniel C., OʼNeill, John P., Drevik, Johnathan, Christianson, Sarah S., Ginzburg, Serge, Correa, Andres F., Uzzo, Robert G., Ross, Eric A., Zibelman, Matthew R., Ghatalia, Pooja, Plimack, Elizabeth R., Kutikov, Alexander, and Abbosh, Philip H.

Published
2024
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18. Operational Learning-based Boundary Estimation in Electromagnetic Medical Imaging

Author

Al-Saffar, A., Stancombe, A., Zamani, A., and Abbosh, A.

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Incorporating boundaries of the imaging object as a priori information to imaging algorithms can significantly improve the performance of electromagnetic medical imaging systems. To avoid overly complicating the system by using different sensors and the adverse effect of the subject's movement, a learning-based method is proposed to estimate the boundary (external contour) of the imaged object using the same electromagnetic imaging data. While imaging techniques may discard the reflection coefficients for being dominant and uninformative for imaging, these parameters are made use of for boundary detection. The learned model is verified through independent clinical human trials by using a head imaging system with a 16-element antenna array that works across the band 0.7-1.6 GHz. The evaluation demonstrated that the model achieves average dissimilarity of 0.012 in Hu-moment while detecting head boundary. The model enables fast scan and image creation while eliminating the need for additional devices for accurate boundary estimation., Comment: Under Review

Published
2021
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19. Graph Attention Network For Microwave Imaging of Brain Anomaly

Author

Al-Saffar, A., Guo, L., and Abbosh, A.

Subjects
Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing, Quantitative Biology - Neurons and Cognition
Abstract

So far, numerous learned models have been pressed to use in microwave imaging problems. These models however, are oblivious to the imaging geometry. It has always been hard to bake the physical setup of the imaging array into the structure of the network, resulting in a data-intensive models that are not practical. This work put forward a graph formulation of the microwave imaging array. The architectures proposed is made cognizant of the physical setup, allowing it to incorporate the symmetries, resulting in a less data requirements. Graph convolution and attention mechanism is deployed to handle the cases of fully-connected graphs corresponding to multi-static arrays. The graph-treatment of the problem is evaluated on experimental setup in context of brain anomaly localization with microwave imaging., Comment: This submission has been removed by arXiv administrators as the submitter did not have the authority to grant the license at the time of submission

Published
2021

20. Introducing: DeepHead, Wide-band Electromagnetic Imaging Paradigm

Author

Al-Saffar, A., Guo, L., and Abbosh, A.

Subjects
Physics - Medical Physics, Computer Science - Machine Learning
Abstract

Electromagnetic medical imaging in the microwave regime is a hard problem notorious for 1) instability 2) under-determinism. This two-pronged problem is tackled with a two-pronged solution that uses double compression to maximally utilizing the cheap unlabelled data to a) provide a priori information required to ease under-determinism and b) reduce sensitivity of inference to the input. The result is a stable solver with a high resolution output. DeepHead is a fully data-driven implementation of the paradigm proposed in the context of microwave brain imaging. It infers the dielectric distribution of the brain at a desired single frequency while making use of an input that spreads over a wide band of frequencies. The performance of the model is evaluated with both simulations and human volunteers experiments. The inference made is juxtaposed with ground-truth dielectric distribution in simulation case, and the golden MRI / CT imaging modalities of the volunteers in real-world case., Comment: Under review, major revision

Published
2021

21. Embracing cancer complexity: Hallmarks of systemic disease

Author

Swanton, Charles, Bernard, Elsa, Abbosh, Chris, André, Fabrice, Auwerx, Johan, Balmain, Allan, Bar-Sagi, Dafna, Bernards, René, Bullman, Susan, DeGregori, James, Elliott, Catherine, Erez, Ayelet, Evan, Gerard, Febbraio, Mark A., Hidalgo, Andrés, Jamal-Hanjani, Mariam, Joyce, Johanna A., Kaiser, Matthew, Lamia, Katja, Locasale, Jason W., Loi, Sherene, Malanchi, Ilaria, Merad, Miriam, Musgrave, Kathryn, Patel, Ketan J., Quezada, Sergio, Wargo, Jennifer A., Weeraratna, Ashani, White, Eileen, Winkler, Frank, Wood, John N., Vousden, Karen H., and Hanahan, Douglas

Published
2024
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23. The evolution of non-small cell lung cancer metastases in TRACERx

Author

Al Bakir, Maise, Huebner, Ariana, Martínez-Ruiz, Carlos, Grigoriadis, Kristiana, Watkins, Thomas B. K., Pich, Oriol, Moore, David A., Veeriah, Selvaraju, Ward, Sophia, Laycock, Joanne, Johnson, Diana, Rowan, Andrew, Razaq, Maryam, Akther, Mita, Naceur-Lombardelli, Cristina, Prymas, Paulina, Toncheva, Antonia, Hessey, Sonya, Dietzen, Michelle, Colliver, Emma, Frankell, Alexander M., Bunkum, Abigail, Lim, Emilia L., Karasaki, Takahiro, Abbosh, Christopher, Hiley, Crispin T., Hill, Mark S., Cook, Daniel E., Wilson, Gareth A., Salgado, Roberto, Nye, Emma, Stone, Richard Kevin, Fennell, Dean A., Price, Gillian, Kerr, Keith M., Naidu, Babu, Middleton, Gary, Summers, Yvonne, Lindsay, Colin R., Blackhall, Fiona H., Cave, Judith, Blyth, Kevin G., Nair, Arjun, Ahmed, Asia, Taylor, Magali N., Procter, Alexander James, Falzon, Mary, Lawrence, David, Navani, Neal, Thakrar, Ricky M., Janes, Sam M., Papadatos-Pastos, Dionysis, Forster, Martin D., Lee, Siow Ming, Ahmad, Tanya, Quezada, Sergio A., Peggs, Karl S., Van Loo, Peter, Dive, Caroline, Hackshaw, Allan, Birkbak, Nicolai J., Zaccaria, Simone, Jamal-Hanjani, Mariam, McGranahan, Nicholas, and Swanton, Charles

Published
2023
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24. The evolution of lung cancer and impact of subclonal selection in TRACERx

Author

Frankell, Alexander M., Dietzen, Michelle, Al Bakir, Maise, Lim, Emilia L., Karasaki, Takahiro, Ward, Sophia, Veeriah, Selvaraju, Colliver, Emma, Huebner, Ariana, Bunkum, Abigail, Hill, Mark S., Grigoriadis, Kristiana, Moore, David A., Black, James R. M., Liu, Wing Kin, Thol, Kerstin, Pich, Oriol, Watkins, Thomas B. K., Naceur-Lombardelli, Cristina, Cook, Daniel E., Salgado, Roberto, Wilson, Gareth A., Bailey, Chris, Angelova, Mihaela, Bentham, Robert, Martínez-Ruiz, Carlos, Abbosh, Christopher, Nicholson, Andrew G., Le Quesne, John, Biswas, Dhruva, Rosenthal, Rachel, Puttick, Clare, Hessey, Sonya, Lee, Claudia, Prymas, Paulina, Toncheva, Antonia, Smith, Jon, Xing, Wei, Nicod, Jerome, Price, Gillian, Kerr, Keith M., Naidu, Babu, Middleton, Gary, Blyth, Kevin G., Fennell, Dean A., Forster, Martin D., Lee, Siow Ming, Falzon, Mary, Hewish, Madeleine, Shackcloth, Michael J., Lim, Eric, Benafif, Sarah, Russell, Peter, Boleti, Ekaterini, Krebs, Matthew G., Lester, Jason F., Papadatos-Pastos, Dionysis, Ahmad, Tanya, Thakrar, Ricky M., Lawrence, David, Navani, Neal, Janes, Sam M., Dive, Caroline, Blackhall, Fiona H., Summers, Yvonne, Cave, Judith, Marafioti, Teresa, Herrero, Javier, Quezada, Sergio A., Peggs, Karl S., Schwarz, Roland F., Van Loo, Peter, Miedema, Daniël M., Birkbak, Nicolai J., Hiley, Crispin T., Hackshaw, Allan, Zaccaria, Simone, Jamal-Hanjani, Mariam, McGranahan, Nicholas, and Swanton, Charles

Published
2023
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25. Tracking early lung cancer metastatic dissemination in TRACERx using ctDNA

Author

Abbosh, Christopher, Frankell, Alexander M., Harrison, Thomas, Kisistok, Judit, Garnett, Aaron, Johnson, Laura, Veeriah, Selvaraju, Moreau, Mike, Chesh, Adrian, Chaunzwa, Tafadzwa L., Weiss, Jakob, Schroeder, Morgan R., Ward, Sophia, Grigoriadis, Kristiana, Shahpurwalla, Aamir, Litchfield, Kevin, Puttick, Clare, Biswas, Dhruva, Karasaki, Takahiro, Black, James R. M., Martínez-Ruiz, Carlos, Bakir, Maise Al, Pich, Oriol, Watkins, Thomas B. K., Lim, Emilia L., Huebner, Ariana, Moore, David A., Godin-Heymann, Nadia, L’Hernault, Anne, Bye, Hannah, Odell, Aaron, Roberts, Paula, Gomes, Fabio, Patel, Akshay J., Manzano, Elizabeth, Hiley, Crispin T., Carey, Nicolas, Riley, Joan, Cook, Daniel E., Hodgson, Darren, Stetson, Daniel, Barrett, J. Carl, Kortlever, Roderik M., Evan, Gerard I., Hackshaw, Allan, Daber, Robert D., Shaw, Jacqui A., Aerts, Hugo J. W. L., Licon, Abel, Stahl, Josh, Jamal-Hanjani, Mariam, Birkbak, Nicolai J., McGranahan, Nicholas, and Swanton, Charles

Published
2023
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26. Lung adenocarcinoma promotion by air pollutants

Author

Hill, William, Lim, Emilia L., Weeden, Clare E., Lee, Claudia, Augustine, Marcellus, Chen, Kezhong, Kuan, Feng-Che, Marongiu, Fabio, Evans, Jr, Edward J., Moore, David A., Rodrigues, Felipe S., Pich, Oriol, Bakker, Bjorn, Cha, Hongui, Myers, Renelle, van Maldegem, Febe, Boumelha, Jesse, Veeriah, Selvaraju, Rowan, Andrew, Naceur-Lombardelli, Cristina, Karasaki, Takahiro, Sivakumar, Monica, De, Swapnanil, Caswell, Deborah R., Nagano, Ai, Black, James R. M., Martínez-Ruiz, Carlos, Ryu, Min Hyung, Huff, Ryan D., Li, Shijia, Favé, Marie-Julie, Magness, Alastair, Suárez-Bonnet, Alejandro, Priestnall, Simon L., Lüchtenborg, Margreet, Lavelle, Katrina, Pethick, Joanna, Hardy, Steven, McRonald, Fiona E., Lin, Meng-Hung, Troccoli, Clara I., Ghosh, Moumita, Miller, York E., Merrick, Daniel T., Keith, Robert L., Al Bakir, Maise, Bailey, Chris, Hill, Mark S., Saal, Lao H., Chen, Yilun, George, Anthony M., Abbosh, Christopher, Kanu, Nnennaya, Lee, Se-Hoon, McGranahan, Nicholas, Berg, Christine D., Sasieni, Peter, Houlston, Richard, Turnbull, Clare, Lam, Stephen, Awadalla, Philip, Grönroos, Eva, Downward, Julian, Jacks, Tyler, Carlsten, Christopher, Malanchi, Ilaria, Hackshaw, Allan, Litchfield, Kevin, DeGregori, James, Jamal-Hanjani, Mariam, and Swanton, Charles

Published
2023
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27. Evolutionary characterization of lung adenocarcinoma morphology in TRACERx

Author

Karasaki, Takahiro, Moore, David A., Veeriah, Selvaraju, Naceur-Lombardelli, Cristina, Toncheva, Antonia, Magno, Neil, Ward, Sophia, Bakir, Maise Al, Watkins, Thomas B. K., Grigoriadis, Kristiana, Huebner, Ariana, Hill, Mark S., Frankell, Alexander M., Abbosh, Christopher, Puttick, Clare, Zhai, Haoran, Gimeno-Valiente, Francisco, Saghafinia, Sadegh, Kanu, Nnennaya, Dietzen, Michelle, Pich, Oriol, Lim, Emilia L., Martínez-Ruiz, Carlos, Black, James R. M., Biswas, Dhruva, Campbell, Brittany B., Lee, Claudia, Colliver, Emma, Enfield, Katey S. S., Hessey, Sonya, Hiley, Crispin T., Zaccaria, Simone, Litchfield, Kevin, Birkbak, Nicolai J., Cadieux, Elizabeth Larose, Demeulemeester, Jonas, Van Loo, Peter, Adusumilli, Prasad S., Tan, Kay See, Cheema, Waseem, Sanchez-Vega, Francisco, Jones, David R., Rekhtman, Natasha, Travis, William D., Hackshaw, Allan, Marafioti, Teresa, Salgado, Roberto, Le Quesne, John, Nicholson, Andrew G., McGranahan, Nicholas, Swanton, Charles, and Jamal-Hanjani, Mariam

Published
2023
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28. Trustworthy deep learning framework for the detection of abnormalities in X-ray shoulder images.

Author

Laith Alzubaidi, Asma Salhi, Mohammed A Fadhel, Jinshuai Bai, Freek Hollman, Kristine Italia, Roberto Pareyon, A S Albahri, Chun Ouyang, Jose Santamaría, Kenneth Cutbush, Ashish Gupta, Amin Abbosh, and Yuantong Gu

Subjects
Medicine, Science
Abstract

Musculoskeletal conditions affect an estimated 1.7 billion people worldwide, causing intense pain and disability. These conditions lead to 30 million emergency room visits yearly, and the numbers are only increasing. However, diagnosing musculoskeletal issues can be challenging, especially in emergencies where quick decisions are necessary. Deep learning (DL) has shown promise in various medical applications. However, previous methods had poor performance and a lack of transparency in detecting shoulder abnormalities on X-ray images due to a lack of training data and better representation of features. This often resulted in overfitting, poor generalisation, and potential bias in decision-making. To address these issues, a new trustworthy DL framework has been proposed to detect shoulder abnormalities (such as fractures, deformities, and arthritis) using X-ray images. The framework consists of two parts: same-domain transfer learning (TL) to mitigate imageNet mismatch and feature fusion to reduce error rates and improve trust in the final result. Same-domain TL involves training pre-trained models on a large number of labelled X-ray images from various body parts and fine-tuning them on the target dataset of shoulder X-ray images. Feature fusion combines the extracted features with seven DL models to train several ML classifiers. The proposed framework achieved an excellent accuracy rate of 99.2%, F1Score of 99.2%, and Cohen's kappa of 98.5%. Furthermore, the accuracy of the results was validated using three visualisation tools, including gradient-based class activation heat map (Grad CAM), activation visualisation, and locally interpretable model-independent explanations (LIME). The proposed framework outperformed previous DL methods and three orthopaedic surgeons invited to classify the test set, who obtained an average accuracy of 79.1%. The proposed framework has proven effective and robust, improving generalisation and increasing trust in the final results.

Published
2024
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29. Cell-free DNA in early-stage non-small cell lung cancer

Author

Abbosh, Christopher

Subjects
616.99
Abstract

Circulating tumour DNA (ctDNA) detection following curative treatment is indicative of Minimal Residual Disease (MRD) in solid tumour-types. In this thesis, I discuss the development, analytical validation, orthogonal validation, and clinical validation of two separate tumour-informed personalised ctDNA enrichment assays designed as MRD detection tools. Through analyses of patient samples from the TRACERx-100 and TRACERx-421 cohorts, I identify clinical predictors of pre-operative ctDNA detection, including non-adenocarcinoma histology and tumour proliferation rate. I explore transcriptomic pathways upregulated in pre-operative ctDNA positive adenocarcinomas and observe that pathways involved in DNA replication and proliferation enrich in preoperative ctDNA positive versus ctDNA negative cases. Additionally, I find that preoperative ctDNA detection in adenocarcinomas lead to poor post-surgical outcomes with a DFS-rate of only 25% at 2 years and a high rate of extra thoracic relapse. I compare ctDNA levels in plasma, to tumour volume and demonstrate a correlation between tumour-size and quantity of ctDNA in peripheral blood. I note that this correlation varies by NSCLC histological subtype. In the post-operative setting I identify that MRD detection could highlight patients destined to relapse from their NSCLC and perform clinically focused analyses integrating imaging data from standard of care surveillance scans with MRD detection data. This work demonstrates that MRD detection can precede abnormal imaging findings on routine post-operative surveillance scans and aid interpretation of the equivocal imaging findings which are common in the NSCLC postoperative setting. Through longitudinal MRD tracking, I explore ctDNA doubling times prior to clinical relapse to gain insight into metastatic tumour growth dynamics in recurrent NSCLC. Finally, I discuss how work from this thesis has informed trial design concepts incorporating ctDNA as an MRD biomarker and reference the MERMAID study, an adjuvant phase III global randomised trial informed by MRD data generated in this study.

Published
2021

30. A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications

Author

Laith Alzubaidi, Jinshuai Bai, Aiman Al-Sabaawi, Jose Santamaría, A. S. Albahri, Bashar Sami Nayyef Al-dabbagh, Mohammed A. Fadhel, Mohamed Manoufali, Jinglan Zhang, Ali H. Al-Timemy, Ye Duan, Amjed Abdullah, Laith Farhan, Yi Lu, Ashish Gupta, Felix Albu, Amin Abbosh, and Yuantong Gu

Subjects
Deep learning, Data scarcity, Machine learning, Convolutional neural network (CNN), Deep neural network architectures, Lack of training data, Computer engineering. Computer hardware, TK7885-7895, Information technology, T58.5-58.64, Electronic computers. Computer science, QA75.5-76.95
Abstract

Abstract Data scarcity is a major challenge when training deep learning (DL) models. DL demands a large amount of data to achieve exceptional performance. Unfortunately, many applications have small or inadequate data to train DL frameworks. Usually, manual labeling is needed to provide labeled data, which typically involves human annotators with a vast background of knowledge. This annotation process is costly, time-consuming, and error-prone. Usually, every DL framework is fed by a significant amount of labeled data to automatically learn representations. Ultimately, a larger amount of data would generate a better DL model and its performance is also application dependent. This issue is the main barrier for many applications dismissing the use of DL. Having sufficient data is the first step toward any successful and trustworthy DL application. This paper presents a holistic survey on state-of-the-art techniques to deal with training DL models to overcome three challenges including small, imbalanced datasets, and lack of generalization. This survey starts by listing the learning techniques. Next, the types of DL architectures are introduced. After that, state-of-the-art solutions to address the issue of lack of training data are listed, such as Transfer Learning (TL), Self-Supervised Learning (SSL), Generative Adversarial Networks (GANs), Model Architecture (MA), Physics-Informed Neural Network (PINN), and Deep Synthetic Minority Oversampling Technique (DeepSMOTE). Then, these solutions were followed by some related tips about data acquisition needed prior to training purposes, as well as recommendations for ensuring the trustworthiness of the training dataset. The survey ends with a list of applications that suffer from data scarcity, several alternatives are proposed in order to generate more data in each application including Electromagnetic Imaging (EMI), Civil Structural Health Monitoring, Medical imaging, Meteorology, Wireless Communications, Fluid Mechanics, Microelectromechanical system, and Cybersecurity. To the best of the authors’ knowledge, this is the first review that offers a comprehensive overview on strategies to tackle data scarcity in DL.

Published
2023
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31. Synthetic Microwave Focusing Techniques for Medical Imaging: Fundamentals, Limitations, and Challenges.

Author

Abbosh, Younis M., Sultan, Kamel, Guo, Lei, and Abbosh, Amin

Subjects
MICROWAVE imaging, GREEN'S functions, TIME reversal, ELECTROMAGNETIC wave scattering, DIAGNOSTIC imaging
Abstract

Synthetic microwave focusing methods have been widely adopted in qualitative medical imaging to detect and localize anomalies based on their electromagnetic scattering signatures. This paper discusses the principles, challenges, and limitations of synthetic microwave-focusing techniques in medical applications. It is shown that the various focusing techniques, including time reversal, confocal imaging, and delay-and-sum, are all based on the scalar solution of the electromagnetic scattering problem, assuming the imaged object, i.e., the tissue or object, is linear, reciprocal, and time-invariant. They all aim to generate a qualitative image, revealing any strong scatterer within the imaged domain. The differences among these techniques lie only in the assumptions made to derive the solution and create an image of the relevant tissue or object. To get a fast solution using limited computational resources, those methods assume the tissue is homogeneous and non-dispersive, and thus, a simplified far-field Green's function is used. Some focusing methods compensate for dispersive effects and attenuation in lossy tissues. Other approaches replace the simplified Green's function with more representative functions. While these focusing techniques offer benefits like speed and low computational requirements, they face significant ongoing challenges in real-life applications due to their oversimplified linear solutions to the complex problem of non-linear medical microwave imaging. This paper discusses these challenges and potential solutions. [ABSTRACT FROM AUTHOR]

Published
2024
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33. Quasi-Gradient Nonlinear Simplex Optimization Method in Electromagnetics

Author

Arman Afsari, Amin Abbosh, and Yahya Rahmat-Samii

Subjects
Device optimization, gradient-free optimization, heuristic methods, parameter estimation, quasi-gradient optimization, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

Particle swarm optimization (PSO), genetic algorithm (GA), and nonlinear simplex optimization method (SOM) are some of the most prominent gradient-free optimization algorithms in engineering. When it comes to a common group of electromagnetic optimization problems wherein less than 10 optimization parameters are present in the problem domain, SOM features faster convergence rate vs PSO and GA. Nevertheless, PSO and GA still outperform SOM by having more accuracy in finding the global minimum. To improve the accuracy of SOM in problems with few optimization parameters, a quasi-gradient (Q-G) search direction is added to the conventional algorithm. An extra decision is made by the proposed algorithm to move alongside the reflection or quasi-gradient direction during the error-reduction operations. This modification will improve the accuracy of SOM, which otherwise fails in the examples presented in this article, to levels similar to PSO and GA, while retaining approximately 33% faster convergence speed with relatively small number of parameters, and 20% faster convergence speed with larger number of optimization parameters. Following a standard benchmark test verification, the proposed algorithm successfully solves a suite of electromagnetic optimization problems. Representative examples include the optimization of absorber dimensions in an anechoic chamber, and estimation of the properties of an unknown embedded object by scattered microwave signals.

Published
2023
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35. Mixed responses to targeted therapy driven by chromosomal instability through p53 dysfunction and genome doubling

Author

Hobor, S, Al Bakir, M, Hiley, C, Skrzypski, M, Frankell, A, Bakker, B, Watkins, T, Markovets, A, Dry, J, Brown, A, van der Aart, J, van den Bos, H, Spierings, D, Oukrif, D, Novelli, M, Chakrabarti, T, Rabinowitz, A, Ait Hassou, L, Litiere, S, Kerr, D, Tan, L, Kelly, G, Moore, D, Renshaw, M, Venkatesan, S, Hill, W, Huebner, A, Martinez-Ruiz, C, Black, J, Wu, W, Angelova, M, Mcgranahan, N, Downward, J, Chmielecki, J, Barrett, C, Litchfield, K, Chew, S, Blakely, C, de Bruin, E, Foijer, F, Vousden, K, Bivona, T, Lester, J, Bajaj, A, Nakas, A, Sodha-Ramdeen, A, Tufail, M, Scotland, M, Boyles, R, Rathinam, S, Wilson, C, Marrone, D, Dulloo, S, Fennell, D, Matharu, G, Shaw, J, Boleti, E, Cheyne, H, Khalil, M, Richardson, S, Cruickshank, T, Price, G, Kerr, K, Benafif, S, French, J, Gilbert, K, Naidu, B, Patel, A, Osman, A, Enstone, C, Langman, G, Shackleford, H, Djearaman, M, Kadiri, S, Middleton, G, Leek, A, Hodgkinson, J, Totton, N, Montero, A, Smith, E, Fontaine, E, Granato, F, Paiva-Correia, A, Novasio, J, Rammohan, K, Joseph, L, Bishop, P, Shah, R, Moss, S, Joshi, V, Crosbie, P, Brown, K, Carter, M, Chaturvedi, A, Oliveira, P, Lindsay, C, Blackhall, F, Krebs, M, Summers, Y, Clipson, A, Tugwood, J, Kerr, A, Rothwell, D, Dive, C, Aerts, H, Schwarz, R, Kaufmann, T, Wilson, G, Rosenthal, R, Van Loo, P, Birkbak, N, Szallasi, Z, Kisistok, J, Sokac, M, Salgado, R, Diossy, M, Demeulemeester, J, Bunkum, A, Dwornik, A, Magness, A, Rowan, A, Karamani, A, Toncheva, A, Chain, B, Castignani, C, Bailey, C, Abbosh, C, Puttick, C, Weeden, C, Lee, C, Richard, C, Naceur-Lombardelli, C, Pearce, D, Karagianni, D, Biswas, D, Levi, D, Larose Cadieux, E, Lim, E, Colliver, E, Nye, E, Galvez-Cancino, F, Gimeno-Valiente, F, Kassiotis, G, Stavrou, G, Mastrokalos, G, Lowe, H, Matos, I, Noorani, I, Goldman, J, Reading, J, Rane, J, Nicod, J, Hartley, J, Peggs, K, Enfield, K, Selvaraju, K, Thol, K, Ng, K, Chen, K, Dijkstra, K, Grigoriadis, K, Thakkar, K, Ensell, L, Shah, M, Litovchenko, M, Jamal-Hanjani, M, Werner Sunderland, M, Huska, M, Hill, M, Dietzen, M, Leung, M, Escudero, M, Tanic, M, Sivakumar, M, Chervova, O, Lucas, O, Pich, O, Al-Sawaf, O, Prymas, P, Hobson, P, Pawlik, P, Stone, R, Bentham, R, Vendramin, R, Saghafinia, S, Gamble, S, Veeriah, S, Ung, S, Quezada, S, Vanloo, S, Hessey, S, Ward, S, Harries, S, Boeing, S, Beck, S, Bola, S, Karasaki, T, Denner, T, Marafioti, T, Jones, T, Spanswick, V, Barbe, V, Lu, W, Liu, W, Wu, Y, Naito, Y, Ramsden, Z, Veiga, C, Royle, G, Collins-Fekete, C, Fraioli, F, Ashford, P, Forster, M, Lee, S, Borg, E, Falzon, M, Papadatos-Pastos, D, Wilson, J, Ahmad, T, Procter, A, Ahmed, A, Taylor, M, Nair, A, Lawrence, D, Patrini, D, Navani, N, Thakrar, R, Janes, S, Martinoni Hoogenboom, E, Monk, F, Holding, J, Choudhary, J, Bhakhri, K, Scarci, M, Gorman, P, Khiroya, R, Stephens, R, Wong, Y, Kaplar, Z, Bandula, S, Hackshaw, A, Hacker, A, Sharp, A, Smith, S, Kaur Dhanda, H, Pilotti, C, Leslie, R, Grapa, A, Zhang, H, Abduljabbar, K, Pan, X, Yuan, Y, Chuter, D, Mackenzie, M, Chee, S, Alzetani, A, Cave, J, Richards, J, De Sousa, P, Jordan, S, Rice, A, Raubenheimer, H, Bhayani, H, Ambrose, L, Devaraj, A, Chavan, H, Begum, S, Buderi, S, Kaniu, D, Malima, M, Booth, S, Nicholson, A, Fernandes, N, Shah, P, Proli, C, Hewish, M, Danson, S, Shackcloth, M, Robinson, L, Russell, P, Blyth, K, Kidd, A, Dick, C, Le Quesne, J, Kirk, A, Asif, M, Bilancia, R, Kostoulas, N, Thomas, M, Hynds, R, Kanu, N, Zaccaria, S, Gronroos, E, Swanton, C, Hobor S., Al Bakir M., Hiley C. T., Skrzypski M., Frankell A. M., Bakker B., Watkins T. B. K., Markovets A., Dry J. R., Brown A. P., van der Aart J., van den Bos H., Spierings D., Oukrif D., Novelli M., Chakrabarti T., Rabinowitz A. H., Ait Hassou L., Litiere S., Kerr D. L., Tan L., Kelly G., Moore D. A., Renshaw M. J., Venkatesan S., Hill W., Huebner A., Martinez-Ruiz C., Black J. R. M., Wu W., Angelova M., McGranahan N., Downward J., Chmielecki J., Barrett C., Litchfield K., Chew S. K., Blakely C. M., de Bruin E. C., Foijer F., Vousden K. H., Bivona T. G., Lester J. F., Bajaj A., Nakas A., Sodha-Ramdeen A., Tufail M., Scotland M., Boyles R., Rathinam S., Wilson C., Marrone D., Dulloo S., Fennell D. A., Matharu G., Shaw J. A., Boleti E., Cheyne H., Khalil M., Richardson S., Cruickshank T., Price G., Kerr K. M., Benafif S., French J., Gilbert K., Naidu B., Patel A. J., Osman A., Enstone C., Langman G., Shackleford H., Djearaman M., Kadiri S., Middleton G., Leek A., Hodgkinson J. D., Totton N., Montero A., Smith E., Fontaine E., Granato F., Paiva-Correia A., Novasio J., Rammohan K., Joseph L., Bishop P., Shah R., Moss S., Joshi V., Crosbie P. A. J., Brown K. D., Carter M., Chaturvedi A., Oliveira P., Lindsay C. R., Blackhall F. H., Krebs M. G., Summers Y., Clipson A., Tugwood J., Kerr A., Rothwell D. G., Dive C., Aerts H. J. W. L., Schwarz R. F., Kaufmann T. L., Wilson G. A., Rosenthal R., Van Loo P., Birkbak N. J., Szallasi Z., Kisistok J., Sokac M., Salgado R., Diossy M., Demeulemeester J., Bunkum A., Dwornik A., Magness A., Rowan A. J., Karamani A., Toncheva A., Chain B., Castignani C., Bailey C., Abbosh C., Puttick C., Weeden C. E., Lee C., Richard C., Naceur-Lombardelli C., Pearce D. R., Karagianni D., Biswas D., Levi D., Larose Cadieux E., Lim E. L., Colliver E., Nye E., Galvez-Cancino F., Gimeno-Valiente F., Kassiotis G., Stavrou G., Mastrokalos G. -T., Lowe H. L., Matos I. G., Noorani I., Goldman J., Reading J. L., Rane J. K., Nicod J., Hartley J. A., Peggs K. S., Enfield K. S. S., Selvaraju K., Thol K., Ng K. W., Chen K., Dijkstra K., Grigoriadis K., Thakkar K., Ensell L., Shah M., Litovchenko M., Jamal-Hanjani M., Werner Sunderland M., Huska M. R., Hill M. S., Dietzen M., Leung M. M., Escudero M., Tanic M., Sivakumar M., Chervova O., Lucas O., Pich O., Al-Sawaf O., Prymas P., Hobson P., Pawlik P., Stone R. K., Bentham R., Vendramin R., Saghafinia S., Gamble S., Veeriah S., Ung S. K. A., Quezada S. A., Vanloo S., Hessey S., Ward S., Harries S., Boeing S., Beck S., Bola S. K., Karasaki T., Denner T., Marafioti T., Jones T. P., Spanswick V., Barbe V., Lu W. -T., Liu W. K., Wu Y., Naito Y., Ramsden Z., Veiga C., Royle G., Collins-Fekete C. -A., Fraioli F., Ashford P., Forster M. D., Lee S. M., Borg E., Falzon M., Papadatos-Pastos D., Wilson J., Ahmad T., Procter A. J., Ahmed A., Taylor M. N., Nair A., Lawrence D., Patrini D., Navani N., Thakrar R. M., Janes S. M., Martinoni Hoogenboom E., Monk F., Holding J. W., Choudhary J., Bhakhri K., Scarci M., Gorman P., Khiroya R., Stephens R. C. M., Wong Y. N. S., Kaplar Z., Bandula S., Hackshaw A., Hacker A. -M., Sharp A., Smith S., Kaur Dhanda H., Pilotti C., Leslie R., Grapa A., Zhang H., AbdulJabbar K., Pan X., Yuan Y., Chuter D., MacKenzie M., Chee S., Alzetani A., Cave J., Richards J., Lim E., De Sousa P., Jordan S., Rice A., Raubenheimer H., Bhayani H., Ambrose L., Devaraj A., Chavan H., Begum S., Buderi S. I., Kaniu D., Malima M., Booth S., Nicholson A. G., Fernandes N., Shah P., Proli C., Hewish M., Danson S., Shackcloth M. J., Robinson L., Russell P., Blyth K. G., Kidd A., Dick C., Le Quesne J., Kirk A., Asif M., Bilancia R., Kostoulas N., Thomas M., Hynds R. E., Kanu N., Zaccaria S., Gronroos E., Swanton C., Hobor, S, Al Bakir, M, Hiley, C, Skrzypski, M, Frankell, A, Bakker, B, Watkins, T, Markovets, A, Dry, J, Brown, A, van der Aart, J, van den Bos, H, Spierings, D, Oukrif, D, Novelli, M, Chakrabarti, T, Rabinowitz, A, Ait Hassou, L, Litiere, S, Kerr, D, Tan, L, Kelly, G, Moore, D, Renshaw, M, Venkatesan, S, Hill, W, Huebner, A, Martinez-Ruiz, C, Black, J, Wu, W, Angelova, M, Mcgranahan, N, Downward, J, Chmielecki, J, Barrett, C, Litchfield, K, Chew, S, Blakely, C, de Bruin, E, Foijer, F, Vousden, K, Bivona, T, Lester, J, Bajaj, A, Nakas, A, Sodha-Ramdeen, A, Tufail, M, Scotland, M, Boyles, R, Rathinam, S, Wilson, C, Marrone, D, Dulloo, S, Fennell, D, Matharu, G, Shaw, J, Boleti, E, Cheyne, H, Khalil, M, Richardson, S, Cruickshank, T, Price, G, Kerr, K, Benafif, S, French, J, Gilbert, K, Naidu, B, Patel, A, Osman, A, Enstone, C, Langman, G, Shackleford, H, Djearaman, M, Kadiri, S, Middleton, G, Leek, A, Hodgkinson, J, Totton, N, Montero, A, Smith, E, Fontaine, E, Granato, F, Paiva-Correia, A, Novasio, J, Rammohan, K, Joseph, L, Bishop, P, Shah, R, Moss, S, Joshi, V, Crosbie, P, Brown, K, Carter, M, Chaturvedi, A, Oliveira, P, Lindsay, C, Blackhall, F, Krebs, M, Summers, Y, Clipson, A, Tugwood, J, Kerr, A, Rothwell, D, Dive, C, Aerts, H, Schwarz, R, Kaufmann, T, Wilson, G, Rosenthal, R, Van Loo, P, Birkbak, N, Szallasi, Z, Kisistok, J, Sokac, M, Salgado, R, Diossy, M, Demeulemeester, J, Bunkum, A, Dwornik, A, Magness, A, Rowan, A, Karamani, A, Toncheva, A, Chain, B, Castignani, C, Bailey, C, Abbosh, C, Puttick, C, Weeden, C, Lee, C, Richard, C, Naceur-Lombardelli, C, Pearce, D, Karagianni, D, Biswas, D, Levi, D, Larose Cadieux, E, Lim, E, Colliver, E, Nye, E, Galvez-Cancino, F, Gimeno-Valiente, F, Kassiotis, G, Stavrou, G, Mastrokalos, G, Lowe, H, Matos, I, Noorani, I, Goldman, J, Reading, J, Rane, J, Nicod, J, Hartley, J, Peggs, K, Enfield, K, Selvaraju, K, Thol, K, Ng, K, Chen, K, Dijkstra, K, Grigoriadis, K, Thakkar, K, Ensell, L, Shah, M, Litovchenko, M, Jamal-Hanjani, M, Werner Sunderland, M, Huska, M, Hill, M, Dietzen, M, Leung, M, Escudero, M, Tanic, M, Sivakumar, M, Chervova, O, Lucas, O, Pich, O, Al-Sawaf, O, Prymas, P, Hobson, P, Pawlik, P, Stone, R, Bentham, R, Vendramin, R, Saghafinia, S, Gamble, S, Veeriah, S, Ung, S, Quezada, S, Vanloo, S, Hessey, S, Ward, S, Harries, S, Boeing, S, Beck, S, Bola, S, Karasaki, T, Denner, T, Marafioti, T, Jones, T, Spanswick, V, Barbe, V, Lu, W, Liu, W, Wu, Y, Naito, Y, Ramsden, Z, Veiga, C, Royle, G, Collins-Fekete, C, Fraioli, F, Ashford, P, Forster, M, Lee, S, Borg, E, Falzon, M, Papadatos-Pastos, D, Wilson, J, Ahmad, T, Procter, A, Ahmed, A, Taylor, M, Nair, A, Lawrence, D, Patrini, D, Navani, N, Thakrar, R, Janes, S, Martinoni Hoogenboom, E, Monk, F, Holding, J, Choudhary, J, Bhakhri, K, Scarci, M, Gorman, P, Khiroya, R, Stephens, R, Wong, Y, Kaplar, Z, Bandula, S, Hackshaw, A, Hacker, A, Sharp, A, Smith, S, Kaur Dhanda, H, Pilotti, C, Leslie, R, Grapa, A, Zhang, H, Abduljabbar, K, Pan, X, Yuan, Y, Chuter, D, Mackenzie, M, Chee, S, Alzetani, A, Cave, J, Richards, J, De Sousa, P, Jordan, S, Rice, A, Raubenheimer, H, Bhayani, H, Ambrose, L, Devaraj, A, Chavan, H, Begum, S, Buderi, S, Kaniu, D, Malima, M, Booth, S, Nicholson, A, Fernandes, N, Shah, P, Proli, C, Hewish, M, Danson, S, Shackcloth, M, Robinson, L, Russell, P, Blyth, K, Kidd, A, Dick, C, Le Quesne, J, Kirk, A, Asif, M, Bilancia, R, Kostoulas, N, Thomas, M, Hynds, R, Kanu, N, Zaccaria, S, Gronroos, E, Swanton, C, Hobor S., Al Bakir M., Hiley C. T., Skrzypski M., Frankell A. M., Bakker B., Watkins T. B. K., Markovets A., Dry J. R., Brown A. P., van der Aart J., van den Bos H., Spierings D., Oukrif D., Novelli M., Chakrabarti T., Rabinowitz A. H., Ait Hassou L., Litiere S., Kerr D. L., Tan L., Kelly G., Moore D. A., Renshaw M. J., Venkatesan S., Hill W., Huebner A., Martinez-Ruiz C., Black J. R. M., Wu W., Angelova M., McGranahan N., Downward J., Chmielecki J., Barrett C., Litchfield K., Chew S. K., Blakely C. M., de Bruin E. C., Foijer F., Vousden K. H., Bivona T. G., Lester J. F., Bajaj A., Nakas A., Sodha-Ramdeen A., Tufail M., Scotland M., Boyles R., Rathinam S., Wilson C., Marrone D., Dulloo S., Fennell D. A., Matharu G., Shaw J. A., Boleti E., Cheyne H., Khalil M., Richardson S., Cruickshank T., Price G., Kerr K. M., Benafif S., French J., Gilbert K., Naidu B., Patel A. J., Osman A., Enstone C., Langman G., Shackleford H., Djearaman M., Kadiri S., Middleton G., Leek A., Hodgkinson J. D., Totton N., Montero A., Smith E., Fontaine E., Granato F., Paiva-Correia A., Novasio J., Rammohan K., Joseph L., Bishop P., Shah R., Moss S., Joshi V., Crosbie P. A. J., Brown K. D., Carter M., Chaturvedi A., Oliveira P., Lindsay C. R., Blackhall F. H., Krebs M. G., Summers Y., Clipson A., Tugwood J., Kerr A., Rothwell D. G., Dive C., Aerts H. J. W. L., Schwarz R. F., Kaufmann T. L., Wilson G. A., Rosenthal R., Van Loo P., Birkbak N. J., Szallasi Z., Kisistok J., Sokac M., Salgado R., Diossy M., Demeulemeester J., Bunkum A., Dwornik A., Magness A., Rowan A. J., Karamani A., Toncheva A., Chain B., Castignani C., Bailey C., Abbosh C., Puttick C., Weeden C. E., Lee C., Richard C., Naceur-Lombardelli C., Pearce D. R., Karagianni D., Biswas D., Levi D., Larose Cadieux E., Lim E. L., Colliver E., Nye E., Galvez-Cancino F., Gimeno-Valiente F., Kassiotis G., Stavrou G., Mastrokalos G. -T., Lowe H. L., Matos I. G., Noorani I., Goldman J., Reading J. L., Rane J. K., Nicod J., Hartley J. A., Peggs K. S., Enfield K. S. S., Selvaraju K., Thol K., Ng K. W., Chen K., Dijkstra K., Grigoriadis K., Thakkar K., Ensell L., Shah M., Litovchenko M., Jamal-Hanjani M., Werner Sunderland M., Huska M. R., Hill M. S., Dietzen M., Leung M. M., Escudero M., Tanic M., Sivakumar M., Chervova O., Lucas O., Pich O., Al-Sawaf O., Prymas P., Hobson P., Pawlik P., Stone R. K., Bentham R., Vendramin R., Saghafinia S., Gamble S., Veeriah S., Ung S. K. A., Quezada S. A., Vanloo S., Hessey S., Ward S., Harries S., Boeing S., Beck S., Bola S. K., Karasaki T., Denner T., Marafioti T., Jones T. P., Spanswick V., Barbe V., Lu W. -T., Liu W. K., Wu Y., Naito Y., Ramsden Z., Veiga C., Royle G., Collins-Fekete C. -A., Fraioli F., Ashford P., Forster M. D., Lee S. M., Borg E., Falzon M., Papadatos-Pastos D., Wilson J., Ahmad T., Procter A. J., Ahmed A., Taylor M. N., Nair A., Lawrence D., Patrini D., Navani N., Thakrar R. M., Janes S. M., Martinoni Hoogenboom E., Monk F., Holding J. W., Choudhary J., Bhakhri K., Scarci M., Gorman P., Khiroya R., Stephens R. C. M., Wong Y. N. S., Kaplar Z., Bandula S., Hackshaw A., Hacker A. -M., Sharp A., Smith S., Kaur Dhanda H., Pilotti C., Leslie R., Grapa A., Zhang H., AbdulJabbar K., Pan X., Yuan Y., Chuter D., MacKenzie M., Chee S., Alzetani A., Cave J., Richards J., Lim E., De Sousa P., Jordan S., Rice A., Raubenheimer H., Bhayani H., Ambrose L., Devaraj A., Chavan H., Begum S., Buderi S. I., Kaniu D., Malima M., Booth S., Nicholson A. G., Fernandes N., Shah P., Proli C., Hewish M., Danson S., Shackcloth M. J., Robinson L., Russell P., Blyth K. G., Kidd A., Dick C., Le Quesne J., Kirk A., Asif M., Bilancia R., Kostoulas N., Thomas M., Hynds R. E., Kanu N., Zaccaria S., Gronroos E., and Swanton C.

Abstract

The phenomenon of mixed/heterogenous treatment responses to cancer therapies within an individual patient presents a challenging clinical scenario. Furthermore, the molecular basis of mixed intra-patient tumor responses remains unclear. Here, we show that patients with metastatic lung adenocarcinoma harbouring co-mutations of EGFR and TP53, are more likely to have mixed intra-patient tumor responses to EGFR tyrosine kinase inhibition (TKI), compared to those with an EGFR mutation alone. The combined presence of whole genome doubling (WGD) and TP53 co-mutations leads to increased genome instability and genomic copy number aberrations in genes implicated in EGFR TKI resistance. Using mouse models and an in vitro isogenic p53-mutant model system, we provide evidence that WGD provides diverse routes to drug resistance by increasing the probability of acquiring copy-number gains or losses relative to non-WGD cells. These data provide a molecular basis for mixed tumor responses to targeted therapy, within an individual patient, with implications for therapeutic strategies.

Published
2024

36. The artificial intelligence-based model ANORAK improves histopathological grading of lung adenocarcinoma

Author

Pan, X, Abduljabbar, K, Coelho-Lima, J, Grapa, A, Zhang, H, Cheung, A, Baena, J, Karasaki, T, Wilson, C, Sereno, M, Veeriah, S, Aitken, S, Hackshaw, A, Nicholson, A, Jamal-Hanjani, M, Le Quesne, J, Janes, S, Hacker, A, Sharp, A, Smith, S, Dhanda, H, Chan, K, Pilotti, C, Leslie, R, Chuter, D, Mackenzie, M, Chee, S, Alzetani, A, Lim, E, De Sousa, P, Jordan, S, Rice, A, Raubenheimer, H, Bhayani, H, Ambrose, L, Devaraj, A, Chavan, H, Begum, S, Buderi, S, Kaniu, D, Malima, M, Booth, S, Fernandes, N, Shah, P, Proli, C, Hewish, M, Danson, S, Shackcloth, M, Robinson, L, Russell, P, Blyth, K, Kidd, A, Kirk, A, Asif, M, Bilancia, R, Kostoulas, N, Thomas, M, Dick, C, Lester, J, Bajaj, A, Nakas, A, Sodha-Ramdeen, A, Tufail, M, Scotland, M, Boyles, R, Rathinam, S, Fennell, D, Marrone, D, Dulloo, S, Matharu, G, Shaw, J, Riley, J, Primrose, L, Boleti, E, Cheyne, H, Khalil, M, Richardson, S, Cruickshank, T, Price, G, Kerr, K, Benafif, S, Papadatos-Pastos, D, Wilson, J, Ahmad, T, French, J, Gilbert, K, Naidu, B, Patel, A, Osman, A, Lacson, C, Langman, G, Shackleford, H, Djearaman, M, Kadiri, S, Middleton, G, Leek, A, Hodgkinson, J, Totten, N, Montero, A, Smith, E, Fontaine, E, Granato, F, Novasio, J, Rammohan, K, Joseph, L, Bishop, P, Shah, R, Moss, S, Joshi, V, Crosbie, P, Paiva-Correia, A, Chaturvedi, A, Priest, L, Oliveira, P, Gomes, F, Brown, K, Carter, M, Lindsay, C, Blackhall, F, Krebs, M, Summers, Y, Clipson, A, Tugwood, J, Kerr, A, Rothwell, D, Dive, C, Aerts, H, Schwarz, R, Kaufmann, T, Van Loo, P, Wilson, G, Rosenthal, R, Rowan, A, Bailey, C, Lee, C, Colliver, E, Enfield, K, Hill, M, Angelova, M, Pich, O, Leung, M, Frankell, A, Hiley, C, Zhai, H, Bakir, M, Birkbak, N, Lucas, O, Huebner, A, Puttick, C, Grigoriadis, K, Dietzen, M, Biswas, D, Athanasopoulou, F, Ward, S, Demeulemeester, J, Castignani, C, Cadieux, E, Kisistok, J, Sokac, M, Szallasi, Z, Diossy, M, Salgado, R, Stewart, A, Magness, A, Weeden, C, Levi, D, Gronroos, E, Noorani, I, Goldman, J, Escudero, M, Hobson, P, Vendramin, R, Boeing, S, Denner, T, Barbe, V, Lu, W, Hill, W, Naito, Y, Ramsden, Z, Kassiotis, G, Dwornik, A, Karamani, A, Chain, B, Pearce, D, Karagianni, D, Galvez-Cancino, F, Stavrou, G, Mastrokalos, G, Lowe, H, Matos, I, Reading, J, Hartley, J, Selvaraju, K, Chen, K, Ensell, L, Shah, M, Litovchenko, M, Chervova, O, Pawlik, P, Hynds, R, Gamble, S, Ung, S, Bola, S, Spanswick, V, Wu, Y, Al-Sawaf, O, Jones, T, Beck, S, Tanic, M, Marafioti, T, Borg, E, Falzon, M, Khiroya, R, Toncheva, A, Abbosh, C, Richard, C, Naceur-Lombardelli, C, Gimeno-Valiente, F, Thakkar, K, Sunderland, M, Sivakumar, M, Kanu, N, Prymas, P, Saghafinia, S, Vanloo, S, Lam, J, Liu, W, Bunkum, A, Hessey, S, Zaccaria, S, Martinez-Ruiz, C, Black, J, Thol, K, Bentham, R, Litchfield, K, Mcgranahan, N, Quezada, S, Forster, M, Lee, S, Herrero, J, Nye, E, Stone, R, Nicod, J, Rane, J, Peggs, K, Ng, K, Dijkstra, K, Huska, M, Hoogenboom, E, Monk, F, Holding, J, Choudhary, J, Bhakhri, K, Scarci, M, Gorman, P, Stephens, R, Wong, Y, Kaplar, Z, Bandula, S, Watkins, T, Veiga, C, Royle, G, Collins-Fekete, C, Fraioli, F, Ashford, P, Procter, A, Ahmed, A, Taylor, M, Nair, A, Lawrence, D, Patrini, D, Navani, N, Thakrar, R, Swanton, C, Yuan, Y, Moore, D, Pan X., AbdulJabbar K., Coelho-Lima J., Grapa A. -I., Zhang H., Cheung A. H. K., Baena J., Karasaki T., Wilson C. R., Sereno M., Veeriah S., Aitken S. J., Hackshaw A., Nicholson A. G., Jamal-Hanjani M., Le Quesne J., Janes S. M., Hacker A. -M., Sharp A., Smith S., Dhanda H. K., Chan K., Pilotti C., Leslie R., Chuter D., MacKenzie M., Chee S., Alzetani A., Lim E., De Sousa P., Jordan S., Rice A., Raubenheimer H., Bhayani H., Ambrose L., Devaraj A., Chavan H., Begum S., Buderi S. I., Kaniu D., Malima M., Booth S., Fernandes N., Shah P., Proli C., Hewish M., Danson S., Shackcloth M. J., Robinson L., Russell P., Blyth K. G., Kidd A., Kirk A., Asif M., Bilancia R., Kostoulas N., Thomas M., Dick C., Lester J. F., Bajaj A., Nakas A., Sodha-Ramdeen A., Tufail M., Scotland M., Boyles R., Rathinam S., Fennell D. A., Wilson C., Marrone D., Dulloo S., Matharu G., Shaw J. A., Riley J., Primrose L., Boleti E., Cheyne H., Khalil M., Richardson S., Cruickshank T., Price G., Kerr K. M., Benafif S., Papadatos-Pastos D., Wilson J., Ahmad T., French J., Gilbert K., Naidu B., Patel A. J., Osman A., Lacson C., Langman G., Shackleford H., Djearaman M., Kadiri S., Middleton G., Leek A., Hodgkinson J. D., Totten N., Montero A., Smith E., Fontaine E., Granato F., Novasio J., Rammohan K., Joseph L., Bishop P., Shah R., Moss S., Joshi V., Crosbie P., Paiva-Correia A., Chaturvedi A., Priest L., Oliveira P., Gomes F., Brown K., Carter M., Lindsay C. R., Blackhall F. H., Krebs M. G., Summers Y., Clipson A., Tugwood J., Kerr A., Rothwell D. G., Dive C., Aerts H. J. W. L., Schwarz R. F., Kaufmann T. L., Van Loo P., Wilson G. A., Rosenthal R., Rowan A., Bailey C., Lee C., Colliver E., Enfield K. S. S., Hill M. S., Angelova M., Pich O., Leung M., Frankell A. M., Hiley C. T., Lim E. L., Zhai H., Bakir M. A., Birkbak N. J., Lucas O., Huebner A., Puttick C., Grigoriadis K., Dietzen M., Biswas D., Athanasopoulou F., Ward S., Demeulemeester J., Castignani C., Cadieux E. L., Kisistok J., Sokac M., Szallasi Z., Diossy M., Salgado R., Stewart A., Magness A., Weeden C. E., Levi D., Gronroos E., Noorani I., Goldman J., Escudero M., Hobson P., Vendramin R., Boeing S., Denner T., Barbe V., Lu W. -T., Hill W., Naito Y., Ramsden Z., Kassiotis G., Dwornik A., Karamani A., Chain B., Pearce D. R., Karagianni D., Galvez-Cancino F., Stavrou G., Mastrokalos G., Lowe H. L., Matos I. G., Reading J. L., Hartley J. A., Selvaraju K., Chen K., Ensell L., Shah M., Litovchenko M., Chervova O., Pawlik P., Hynds R. E., Gamble S., Ung S. K. A., Bola S. K., Spanswick V., Wu Y., Al-Sawaf O., Jones T. P., Beck S., Tanic M., Marafioti T., Borg E., Falzon M., Khiroya R., Toncheva A., Abbosh C., Richard C., Naceur-Lombardelli C., Gimeno-Valiente F., Thakkar K., Sunderland M. W., Sivakumar M., Kanu N., Prymas P., Saghafinia S., Vanloo S., Lam J. M., Liu W. K., Bunkum A., Hessey S., Zaccaria S., Martinez-Ruiz C., Black J. R. M., Thol K., Bentham R., Litchfield K., McGranahan N., Quezada S. A., Forster M. D., Lee S. M., Herrero J., Nye E., Stone R. K., Nicod J., Rane J. K., Peggs K. S., Ng K. W., Dijkstra K., Huska M. R., Hoogenboom E. M., Monk F., Holding J. W., Choudhary J., Bhakhri K., Scarci M., Gorman P., Stephens R. C. M., Wong Y. N. S., Kaplar Z., Bandula S., Watkins T. B. K., Veiga C., Royle G., Collins-Fekete C. -A., Fraioli F., Ashford P., Procter A. J., Ahmed A., Taylor M. N., Nair A., Lawrence D., Patrini D., Navani N., Thakrar R. M., Swanton C., Yuan Y., Moore D. A., Pan, X, Abduljabbar, K, Coelho-Lima, J, Grapa, A, Zhang, H, Cheung, A, Baena, J, Karasaki, T, Wilson, C, Sereno, M, Veeriah, S, Aitken, S, Hackshaw, A, Nicholson, A, Jamal-Hanjani, M, Le Quesne, J, Janes, S, Hacker, A, Sharp, A, Smith, S, Dhanda, H, Chan, K, Pilotti, C, Leslie, R, Chuter, D, Mackenzie, M, Chee, S, Alzetani, A, Lim, E, De Sousa, P, Jordan, S, Rice, A, Raubenheimer, H, Bhayani, H, Ambrose, L, Devaraj, A, Chavan, H, Begum, S, Buderi, S, Kaniu, D, Malima, M, Booth, S, Fernandes, N, Shah, P, Proli, C, Hewish, M, Danson, S, Shackcloth, M, Robinson, L, Russell, P, Blyth, K, Kidd, A, Kirk, A, Asif, M, Bilancia, R, Kostoulas, N, Thomas, M, Dick, C, Lester, J, Bajaj, A, Nakas, A, Sodha-Ramdeen, A, Tufail, M, Scotland, M, Boyles, R, Rathinam, S, Fennell, D, Marrone, D, Dulloo, S, Matharu, G, Shaw, J, Riley, J, Primrose, L, Boleti, E, Cheyne, H, Khalil, M, Richardson, S, Cruickshank, T, Price, G, Kerr, K, Benafif, S, Papadatos-Pastos, D, Wilson, J, Ahmad, T, French, J, Gilbert, K, Naidu, B, Patel, A, Osman, A, Lacson, C, Langman, G, Shackleford, H, Djearaman, M, Kadiri, S, Middleton, G, Leek, A, Hodgkinson, J, Totten, N, Montero, A, Smith, E, Fontaine, E, Granato, F, Novasio, J, Rammohan, K, Joseph, L, Bishop, P, Shah, R, Moss, S, Joshi, V, Crosbie, P, Paiva-Correia, A, Chaturvedi, A, Priest, L, Oliveira, P, Gomes, F, Brown, K, Carter, M, Lindsay, C, Blackhall, F, Krebs, M, Summers, Y, Clipson, A, Tugwood, J, Kerr, A, Rothwell, D, Dive, C, Aerts, H, Schwarz, R, Kaufmann, T, Van Loo, P, Wilson, G, Rosenthal, R, Rowan, A, Bailey, C, Lee, C, Colliver, E, Enfield, K, Hill, M, Angelova, M, Pich, O, Leung, M, Frankell, A, Hiley, C, Zhai, H, Bakir, M, Birkbak, N, Lucas, O, Huebner, A, Puttick, C, Grigoriadis, K, Dietzen, M, Biswas, D, Athanasopoulou, F, Ward, S, Demeulemeester, J, Castignani, C, Cadieux, E, Kisistok, J, Sokac, M, Szallasi, Z, Diossy, M, Salgado, R, Stewart, A, Magness, A, Weeden, C, Levi, D, Gronroos, E, Noorani, I, Goldman, J, Escudero, M, Hobson, P, Vendramin, R, Boeing, S, Denner, T, Barbe, V, Lu, W, Hill, W, Naito, Y, Ramsden, Z, Kassiotis, G, Dwornik, A, Karamani, A, Chain, B, Pearce, D, Karagianni, D, Galvez-Cancino, F, Stavrou, G, Mastrokalos, G, Lowe, H, Matos, I, Reading, J, Hartley, J, Selvaraju, K, Chen, K, Ensell, L, Shah, M, Litovchenko, M, Chervova, O, Pawlik, P, Hynds, R, Gamble, S, Ung, S, Bola, S, Spanswick, V, Wu, Y, Al-Sawaf, O, Jones, T, Beck, S, Tanic, M, Marafioti, T, Borg, E, Falzon, M, Khiroya, R, Toncheva, A, Abbosh, C, Richard, C, Naceur-Lombardelli, C, Gimeno-Valiente, F, Thakkar, K, Sunderland, M, Sivakumar, M, Kanu, N, Prymas, P, Saghafinia, S, Vanloo, S, Lam, J, Liu, W, Bunkum, A, Hessey, S, Zaccaria, S, Martinez-Ruiz, C, Black, J, Thol, K, Bentham, R, Litchfield, K, Mcgranahan, N, Quezada, S, Forster, M, Lee, S, Herrero, J, Nye, E, Stone, R, Nicod, J, Rane, J, Peggs, K, Ng, K, Dijkstra, K, Huska, M, Hoogenboom, E, Monk, F, Holding, J, Choudhary, J, Bhakhri, K, Scarci, M, Gorman, P, Stephens, R, Wong, Y, Kaplar, Z, Bandula, S, Watkins, T, Veiga, C, Royle, G, Collins-Fekete, C, Fraioli, F, Ashford, P, Procter, A, Ahmed, A, Taylor, M, Nair, A, Lawrence, D, Patrini, D, Navani, N, Thakrar, R, Swanton, C, Yuan, Y, Moore, D, Pan X., AbdulJabbar K., Coelho-Lima J., Grapa A. -I., Zhang H., Cheung A. H. K., Baena J., Karasaki T., Wilson C. R., Sereno M., Veeriah S., Aitken S. J., Hackshaw A., Nicholson A. G., Jamal-Hanjani M., Le Quesne J., Janes S. M., Hacker A. -M., Sharp A., Smith S., Dhanda H. K., Chan K., Pilotti C., Leslie R., Chuter D., MacKenzie M., Chee S., Alzetani A., Lim E., De Sousa P., Jordan S., Rice A., Raubenheimer H., Bhayani H., Ambrose L., Devaraj A., Chavan H., Begum S., Buderi S. I., Kaniu D., Malima M., Booth S., Fernandes N., Shah P., Proli C., Hewish M., Danson S., Shackcloth M. J., Robinson L., Russell P., Blyth K. G., Kidd A., Kirk A., Asif M., Bilancia R., Kostoulas N., Thomas M., Dick C., Lester J. F., Bajaj A., Nakas A., Sodha-Ramdeen A., Tufail M., Scotland M., Boyles R., Rathinam S., Fennell D. A., Wilson C., Marrone D., Dulloo S., Matharu G., Shaw J. A., Riley J., Primrose L., Boleti E., Cheyne H., Khalil M., Richardson S., Cruickshank T., Price G., Kerr K. M., Benafif S., Papadatos-Pastos D., Wilson J., Ahmad T., French J., Gilbert K., Naidu B., Patel A. J., Osman A., Lacson C., Langman G., Shackleford H., Djearaman M., Kadiri S., Middleton G., Leek A., Hodgkinson J. D., Totten N., Montero A., Smith E., Fontaine E., Granato F., Novasio J., Rammohan K., Joseph L., Bishop P., Shah R., Moss S., Joshi V., Crosbie P., Paiva-Correia A., Chaturvedi A., Priest L., Oliveira P., Gomes F., Brown K., Carter M., Lindsay C. R., Blackhall F. H., Krebs M. G., Summers Y., Clipson A., Tugwood J., Kerr A., Rothwell D. G., Dive C., Aerts H. J. W. L., Schwarz R. F., Kaufmann T. L., Van Loo P., Wilson G. A., Rosenthal R., Rowan A., Bailey C., Lee C., Colliver E., Enfield K. S. S., Hill M. S., Angelova M., Pich O., Leung M., Frankell A. M., Hiley C. T., Lim E. L., Zhai H., Bakir M. A., Birkbak N. J., Lucas O., Huebner A., Puttick C., Grigoriadis K., Dietzen M., Biswas D., Athanasopoulou F., Ward S., Demeulemeester J., Castignani C., Cadieux E. L., Kisistok J., Sokac M., Szallasi Z., Diossy M., Salgado R., Stewart A., Magness A., Weeden C. E., Levi D., Gronroos E., Noorani I., Goldman J., Escudero M., Hobson P., Vendramin R., Boeing S., Denner T., Barbe V., Lu W. -T., Hill W., Naito Y., Ramsden Z., Kassiotis G., Dwornik A., Karamani A., Chain B., Pearce D. R., Karagianni D., Galvez-Cancino F., Stavrou G., Mastrokalos G., Lowe H. L., Matos I. G., Reading J. L., Hartley J. A., Selvaraju K., Chen K., Ensell L., Shah M., Litovchenko M., Chervova O., Pawlik P., Hynds R. E., Gamble S., Ung S. K. A., Bola S. K., Spanswick V., Wu Y., Al-Sawaf O., Jones T. P., Beck S., Tanic M., Marafioti T., Borg E., Falzon M., Khiroya R., Toncheva A., Abbosh C., Richard C., Naceur-Lombardelli C., Gimeno-Valiente F., Thakkar K., Sunderland M. W., Sivakumar M., Kanu N., Prymas P., Saghafinia S., Vanloo S., Lam J. M., Liu W. K., Bunkum A., Hessey S., Zaccaria S., Martinez-Ruiz C., Black J. R. M., Thol K., Bentham R., Litchfield K., McGranahan N., Quezada S. A., Forster M. D., Lee S. M., Herrero J., Nye E., Stone R. K., Nicod J., Rane J. K., Peggs K. S., Ng K. W., Dijkstra K., Huska M. R., Hoogenboom E. M., Monk F., Holding J. W., Choudhary J., Bhakhri K., Scarci M., Gorman P., Stephens R. C. M., Wong Y. N. S., Kaplar Z., Bandula S., Watkins T. B. K., Veiga C., Royle G., Collins-Fekete C. -A., Fraioli F., Ashford P., Procter A. J., Ahmed A., Taylor M. N., Nair A., Lawrence D., Patrini D., Navani N., Thakrar R. M., Swanton C., Yuan Y., and Moore D. A.

Abstract

The introduction of the International Association for the Study of Lung Cancer grading system has furthered interest in histopathological grading for risk stratification in lung adenocarcinoma. Complex morphology and high intratumoral heterogeneity present challenges to pathologists, prompting the development of artificial intelligence (AI) methods. Here we developed ANORAK (pyrAmid pooliNg crOss stReam Attention networK), encoding multiresolution inputs with an attention mechanism, to delineate growth patterns from hematoxylin and eosin-stained slides. In 1,372 lung adenocarcinomas across four independent cohorts, AI-based grading was prognostic of disease-free survival, and further assisted pathologists by consistently improving prognostication in stage I tumors. Tumors with discrepant patterns between AI and pathologists had notably higher intratumoral heterogeneity. Furthermore, ANORAK facilitates the morphological and spatial assessment of the acinar pattern, capturing acinus variations with pattern transition. Collectively, our AI method enabled the precision quantification and morphology investigation of growth patterns, reflecting intratumoral histological transitions in lung adenocarcinoma.

Published
2024

37. Human Gut Mycobiome and Fungal Community Interaction: The Unknown Musketeer in the Chemotherapy Response Status in Bladder Cancer

Author

Laura Bukavina, Megan Prunty, Ilaha Isali, Adam Calaway, Rashida Ginwala, Mohit Sindhani, Mahmoud Ghannoum, Kirtishri Mishra, Alexander Kutikov, Robert G. Uzzo, Lee E. Ponsky, and Philip H. Abbosh

Subjects
Mycobiome, Bladder cancer, Urothelial cancer, Microbiome, Diseases of the genitourinary system. Urology, RC870-923, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Background: Until recently, the properties of microbiome and mycobiome in humans and its relevance to disease have largely been unexplored. While the interest of microbiome and malignancy over the past few years have burgeoned with advent of new technologies, no research describing the composition of mycobiome in bladder cancer has been done. Deciphering of the metagenome and its aggregate genetic information can be used to understand the functional properties and relationships between the bacteria, fungi, and cancer. Objective: The aim of this project is to characterize the compositional range of the normal versus bladder cancer mycobiome of the gut. Design, setting, and participants: An internal transcribed spacer (ITS) survey of 52 fecal samples was performed to evaluate the gut mycobiome differences between noncancer controls and bladder cancer patients. Outcome measurements and statistical analysis: Our study evaluated the differences in mycobiome among patients with bladder cancer, versus matched controls. Our secondary analysis evaluated compositional differences in the gut as a function of response status with neoadjuvant chemotherapy. Data demultiplexing and classification were performed using the QIIME v.1.1.1.1 platform. The Ion Torrent–generated fungal ITS sequence data were processed using QIIME (v.1.9.1), and the reads were demultiplexed, quality filtered, and clustered into operation taxonomic units using default parameters. Alpha and beta diversity were computed and plotted in Phyloseq, principal coordinate analysis was performed on Bray-Curtis dissimilarity indices, and a one-way permutational multivariate analysis of variance was used to test for significant differences between cohorts. Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) was applied to infer functional categories associated with taxonomic composition. Results and limitations: We found distinctive mycobiome differences between control group (n = 32) and bladder cancer (n = 29) gut flora, and identified an increasing abundance of Tremellales, Hypocreales, and Dothideales. Significant differences in alpha and beta diversity were present between the groups (control vs bladder; p = 0.002), noting distinct compositions within each cohort. A subgroup analysis by sex and neoadjuvant chemotherapy status did not show any further differences in mycobiome composition and diversity. Our results indicate that the gut mycobiome may modulate tumor response to preoperative chemotherapy in bladder cancer patients. We propose that patients with a “favorable” mycobiome composition (eg, high diversity, and low abundance of Agaricomycetes and Saccharomycetes) may have enhanced systemic immune response to chemotherapy through antigen presentation. Conclusions: Our study is the first to characterize the enteric mycobiome in patients with bladder cancer and describe complex ecological network alterations, indicating complex bacteria-fungi interactions, particularly highlighted among patients with complete neoadjuvant chemotherapy response. Patient summary: Our study has demonstrated that the composition of stool mycobiome (fungal inhabitants of the gastrointestinal tract) in patients with bladder cancer is different from that in noncancer individuals. Furthermore, when evaluating how patients respond to chemotherapy given prior to their surgery, our study noted significant differences between patients who responded and those who did not.

Published
2022
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38. Wide-Angle Beam Steering Closed-Form Pillbox Antenna Fed by Substrate-Integrated Waveguide Horn for On-the-Move Satellite Communications

Author

Muhammad Ikram, Kamel Sultan, Ahmed Toaha Mobashsher, Mahdi Moosazadeh, and Amin Abbosh

Subjects
flat panel antenna, low earth orbit satellites, on-the-move communications, Chemical technology, TP1-1185
Abstract

Wide-angle mechanical beam steering for on-the-move satellite communications is presented in this paper based on a closed-form pillbox antenna system. It includes three main parts: a fixed-feed part, which is a substrate-integrated waveguide (SIW) horn with an extended aperture attached to a parabolic reflector; a novel quasi-optical system, which is a single coupling slot alongside and without spacing from the parabolic reflector; and a radiating disc, which is a leaky-wave metallic pattern. To make the antenna compact, pillbox-based feeding is implemented underneath the metallic patterns. The antenna is designed based on a substrate-guided grounded concept using leaky-wave metallic patterns operating at 20 GHz. Beam scanning is achieved using mechanical rotation of the leaky-wave metallic patterns. The proposed antenna has an overall size of 340 × 335 × 2 mm3, a gain of 23.2 dBi, wide beam scanning range of 120°, from −60° to +60° in the azimuthal plane, and a low side lobe level of −17.8 dB at a maximum scan angle of 60°. The proposed antenna terminal is suitable for next-generation ubiquitous connectivity for households and small businesses in remote areas, ships, unmanned aerial vehicles, and disaster management.

Published
2024
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39. Shielded Cone Coil Array for Non-Invasive Deep Brain Magnetic Stimulation

Author

Rawan Abu Yosef, Kamel Sultan, Ahmed Toaha Mobashsher, Firuz Zare, Paul C. Mills, and Amin Abbosh

Subjects
transcranial magnetic stimulation, deep brain stimulation, neurological disease, coil array, vivo pig, Alzheimer, Biotechnology, TP248.13-248.65
Abstract

Non-invasive deep brain stimulation using transcranial magnetic stimulation is a promising technique for treating several neurological disorders, such as Alzheimer’s and Parkinson’s diseases. However, the currently used coils do not demonstrate the required stimulation performance in deep regions of the brain, such as the hippocampus, due to the rapid decay of the field inside the head. This study proposes an array that uses the cone coil method for deep stimulation. This study investigates the impact of magnetic core and shielding on field strength, focality, decay rate, and safety. The coil’s size and shape effects on the electric field distribution in deep brain areas are also examined. The finite element method is used to calculate the induced electric field in a realistic human head model. The simulation results indicate that the magnetic core and shielding increase the electric field intensity and enhance focality but do not improve the field decay rate. However, the decay rate can be reduced by increasing the coil size at the expense of focality. By adopting an optimum cone structure, the proposed five-coil array reduces the electric field attenuation rate to reach the stimulation threshold in deep regions while keeping all other regions within safety limits. In vitro and in vivo experimental results using a head phantom and a dead pig’s head validate the simulated results and confirm that the proposed design is a reliable and efficient candidate for non-invasive deep brain magnetic stimulation.

Published
2024
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40. Training Universal Deep-Learning Networks for Electromagnetic Medical Imaging Using a Large Database of Randomized Objects

Author

Fei Xue, Lei Guo, Alina Bialkowski, and Amin Abbosh

Subjects
database, deep learning, electromagnetic imaging, antenna sensing, Chemical technology, TP1-1185
Abstract

Deep learning has become a powerful tool for solving inverse problems in electromagnetic medical imaging. However, contemporary deep-learning-based approaches are susceptible to inaccuracies stemming from inadequate training datasets, primarily consisting of signals generated from simplified and homogeneous imaging scenarios. This paper introduces a novel methodology to construct an expansive and diverse database encompassing domains featuring randomly shaped structures with electrical properties representative of healthy and abnormal tissues. The core objective of this database is to enable the training of universal deep-learning techniques for permittivity profile reconstruction in complex electromagnetic medical imaging domains. The constructed database contains 25,000 unique objects created by superimposing from 6 to 24 randomly sized ellipses and polygons with varying electrical attributes. Introducing randomness in the database enhances training, allowing the neural network to achieve universality while reducing the risk of overfitting. The representative signals in the database are generated using an array of antennas that irradiate the imaging domain and capture scattered signals. A custom-designed U-net is trained by using those signals to generate the permittivity profile of the defined imaging domain. To assess the database and confirm the universality of the trained network, three distinct testing datasets with diverse objects are imaged using the designed U-net. Quantitative assessments of the generated images show promising results, with structural similarity scores consistently exceeding 0.84, normalized root mean square errors remaining below 14%, and peak signal-to-noise ratios exceeding 33 dB. These results demonstrate the practicality of the constructed database for training deep learning networks that have generalization capabilities in solving inverse problems in medical imaging without the need for additional physical assistant algorithms.

Published
2023
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42. Effects of hospital facilities on patient outcomes after cancer surgery: an international, prospective, observational study

Author

Knight, Stephen R, Shaw, Catherine A, Pius, Riinu, Drake, Thomas M, Norman, Lisa, Ademuyiwa, Adesoji O, Adisa, Adewale O, Aguilera, Maria Lorena, Al-Saqqa, Sara W, Al-Slaibi, Ibrahim, Bhangu, Aneel, Biccard, Bruce M, Brocklehurst, Peter, Costas-Chavarri, Ainhoa, Chu, Kathryn, Dare, Anna, Elhadi, Muhammed, Fairfield, Cameron J, Fitzgerald, J Edward, Ghosh, Dhruv, Glasbey, James, van Berge Henegouwen, Mark I., Ingabire, J.C. Allen, Kingham, T Peter, Lapitan, Marie Carmela, Lawani, Ismaïl, Lieske, Bettina, Lilford, Richard, Martin, Janet, McLean, Kenneth A, Moore, Rachel, Morton, Dion, Nepogodiev, Dmitri, Ntirenganya, Faustin, Pata, Francesco, Pinkney, Thomas, Qureshi, Ahmad Uzair, Ramos-De la Medina, Antonio, Riad, Aya, Salem, Hosni Khairy, Simões, Joana, Spence, Richard, Smart, Neil, Tabiri, Stephen, Thomas, Hannah, Weiser, Thomas G, West, Malcolm, Whitaker, John, Harrison, Ewen M, Gjata, Arben, Modolo, Maria Marta, King, Sebastian, Chan, Erick, Nahar, Sayeda Nazmun, Waterman, Ade, Vervoort, Dominique, Bedada, Alemayehu Ginbo, De Azevedo, Bernardo, Figueiredo, Ana Gabriela, Sokolov, Manol, Barendegere, Venerand, Ekwen, Gerald, Agarwal, Arnav, Liu, Qinyang, Camilo Correa, Juan, Malemo, Kalisya Luc, Bake, Jacques, Mihanovic, Jakov, Kunčarová, Kamila, Orhalmi, Julius, Salem, Hosni, Teras, Jyri, Kechagias, Aristotelis, Arnaud, Alexis P, Lindert, Judith, Kalles, Vasileios, Aguilera-Arevalo, Maria-Lorena, Recinos, Gustavo, Baranyai, Zsolt, Kumar, Basant, Neelamraju Lakshmi, Harish, Zachariah, Sanoop Koshy, Alexander, Philip, Kumar Venkatappa, Sunil, Pramesh, C, Amandito, Radhian, Fleming, Christina, Ansaloni, Luca, Pellino, Gianluca, Altibi, Ahmed M., Nour, Ibrahim, Hamdun, Intisar, Ghellai, Ali M., Venskutonis, Donatas, Poskus, Tomas, Zilinskas, Justas, Malemia, Precious, Tew, Yong Yong, Borg, Elaine, Ellul, Sarah, Wafqui, fatima Zahraa, Borowski, David W, van Dalen, Anne Sophie, Wells, Cameron, Adamou, Harissou, Ademuyiwa, Adesoji, Adisa, Adewale, Søreide, Kjetil, Al Saqqa, Sara, Alser, Osaid, Tahboub, Haya, Segovia Lohse, Helmut Alfredo, Shu Yip, Sebastian, Major, Piotr, Sampaio Soares, António, Bratu, Matei Razvan, Litvin, Andrey, Vardanyan, Armen, Allen Ingabire, JC, Gudal, Ahmad, Albati, Naif, Juloski, Jovan, Rems, Miran, Rayne, Sarah, Van Straten, Stephanie, Moodley, Yoshan, Ortega Vázquez, Irene, Ruiz-Tovar, Jaime, Senanayake, Kithsiri Janakantha, Thalgaspitiya, Sujeewa Priyantha Bandara, Omer, Omer Abdelbagi, Homeida, Anmar, Cengiz, Yucel, Clerc, Daniel, Alshaar, Muhammad, Bouaziz, Hanen, Altinel, Yuksel, Doe, Matthew, Freigofer, Maryna, Teasdale, Ella, Kabariti, Rakan, Clements, Joshua Michael, Knight, Stephen Richard, Ashfaq, Ahsan, Azodo, Ijeoma, Wagner, Gabriela, Trostchansky, Ivan, Maimbo, Mayaba, Linyama, David, Nina, Helidon, Zeko, Amanda, Fermani, Claudio Gabriel, Villalobos, Santiago, Carballo, Federico, Farina, Pablo, Guckenheimer, Sebastian, Dickfos, Marilla, Ajmera, Ankit, Chong, Chester, Gourlay, Ralph, Hussaini, Sikandar, Lee, Yi Jia, Majid, Adeeb, Martin, Peter, Miles, Rebecca, Morris, Owen James, Phua, Jamie, Ridley, William, Saluja, Tarunpreet, Tan, Ryan Renxin, Teh, Jen, Wells, Anna, Arora, Bharti, Dollie, Qaasim, Ho, Debbie, Ma, Yanru, Perera, Omattage Mahasha, Truong, Anthony, Dawson, Amanda Caroline, Lim, Bryan, Pahalawatta, Upuli, Phan, Jacqueline, Woon-Shoo-Tong, Xiao-Ming Sarah, Yeoh, Andrea, Charman, Lillian, Drane, Andrew, Laura, Sharon, Lo, Charmaine Chu Wen, Mozes, Amy, Poon, Rita, Tan, Hao Han, Wall, Ellen, Chopra, Prakshi, De Giovanni, Jasmine, Dhital, Bal, Draganic, Brian, Duller, Alexander, Gani, Jonathan, Goh, Yao Kuan, Jeong, Jun Young, McManus, Brendan, Nagappan, Prakash, Pockney, Peter, Rugendyke, Anya, Sarrami, Mahsa, Smith, Stephen, Wills, Vanessa, Wong, Hsu Ven, Ye, Geoffrey, Zhang, Geoffrey, Brooker, Ethan, Feng, Daniel, Lau, Bonnie, Ngai, Carlin, Birks, Sarah, Gyorki, David, Otero de Pablos, Jaime, Abbosh, Ali, Gillespie, Chris, Mahmoud, Ahmed, Kwan, Bianca, Lawson, Joshua, Warwick, Andrea, Bingham, Janne, Cockbain, Andrew J, Dudi-Venkata, Nagendra Naidu, Ellaby-Hall, Jordan, Finlay, Ben, Humphries, Emily, Pisaniello, Jade, Pisaniello, Monique, Salih, Salma, Sammour, Tarik, Abd Wahab, Haidar Hadri, De Silva, April, Hayward, Nicola, Iyer, Kartik, Maddern, Guy, Prevost, Gian Andrea, Annapureddy, Naga, Settipalli, Krishna Pranathi, Yeo, Jeremy, Hempenstall, Lucy, Pham, Lily, Purcell, Shaun, Talavera, Cherry, Vaska, Ashish I, Chaggar, Gurpreet, Chrapko, Phillip, Cocco, Annelise, Coulter-Nile, Sarah Michelle Crystal Jade, Ctercteko, Grahame, French, James, Gong, Houchen, Gosselink, Martijn, Jegathees, Thuvarahan, Jin, Ivan, Kalachov, Michelle, Kiefhaber, Kathryn, Lee, Katherine, Luong, Jason, Phan, Steven, Pleass, Henry, Veale, Kelly, Zeng, Zhi, Au, Angela, DeBiasio, Ashe, Deng, Idy, Myooran, Jananee, Nair, Amrita, Stewart, Peter, Stift, Anton, Unger, Lukas Walter, Wimmer, Kerstin, Ahmed, Nabila, Hasan, Syed, Rahman, Saber, O'Shea, Margaret, Padmore, Greg, Peters, Adrian, Perduca, Pietro, Pulcina, Guenda, Tinton, Nicolas, Buxant, Frederic, Dabin, Elsa, Garofalo, Giulia, Dossou, Francis, Gnangnon, Freddy Houehanou Rodrigue, Imorou Souaibou, Yacoubou, Motlaleselelo, Pako, Tlhomelang, Omphile, Lima Buarque, Igor, Mendonça Ataíde Gomes, Gustavo, Vieira Barros, Aldo, Batashki, Ilia, Damianov, Nikolai, Stoyanov, Vladislav, Dardanov, Dragomir, Maslyankov, Svilen, Petkov, Plamen, Todorov, George, Zhivkov, Evgeni, Akisheva, Aygulya, Castilla Moreno, Miguel Angel, Genov, Geno, Ilieva, Ivelina, Ivanov, Tsvetomir, Karamanliev, Martin, Khan, Azhar, Mitkov, Emil, Yotsov, Tsanko, Atanasov, Boyko, Belev, Nikolay, Slavchev, Mihail, Nsengiyumva, Carlos, Jones, Elgan, Stock, Simon, Kyota, Steve, Brown, James, Mabanza K., Tresor, Nigo Samuel, Lemery, Otuneme, Chidi, Prosper, Ngwang, Umenze, Franklin, Boutros, Marylise, Caminsky, Natasha, Dumitra, Sinziana, Garfinkle, Richard, Morency, Dominique, Salama, Ebram, Banks, Alexander, Ferri, Lorenzo, He, Haitian, Katz, Amit, Liberman, Alexander Sender, Meterissian, Sarkis, Pang, Allison, Parvez, Elena, Hameed, Usmaan, Osman, Fahima, Sequeira, Sangita, Coburn, Natalie, Jaffer, Alisha, Karanicolas, Paul, Mosseler, Matthew, Musselman, Reilly, Liu, Xinyuan, Yip, Ching Wan, Garces-Otero, Juan Sebastian, Guzman, Carolina, Sierra, Sebastian, Uribe Valencia, Andres, Cabrera Rivera, Paulo Andrés, Camelo, Saul, Gonzalez, Andrea, González-Orozco, Alejandro, Mosquera Paz, Manuel Santiago, Perez Rivera, Carlos J, Gonzalez, Felipe, Isaza-Restrepo, Andres, Nino- Torres, Laura, Arias Madrid, Natalia, Mendoza Arango, Maria Clara, Tsandiraki, Justin, Jemendžić, Damir, Kocman, Branislav, Šuman, Oliver, Canic, Renata, Jurišić, Darko, Karakas, Ivana, Krizanovic Rupcic, Ana, Pitlovic, Vlatka, Samardžić, Josip, Kopljar, Mario, Bacic, Ivan, Domini, Edgar, Karlo, Robert, Miljanić, Danijela, Simic, Andrea, Ahmed, Mariam, Al Nassrallah, Majdi, Altaf, Rabiya, Amjad, Talal, Eltoum, Ruba, Haidar, Heba, Hassan, Alhassan, Khalil, Omar, Qasem, Marwan, Ramesh, Rommel, Sajith, Gautham, Wisal, Maham, Žatecký, Jan, Bujda, Michele, Jirankova, Katerina, Paclik, Ales, Abdallah, Aya, Abdulgawad Almogy, Mariam, Ayman El-sawy, Esraa, ElFayoumy, Ahmed Moustafa, Elghareeb, Nourhan, Esmat, Nourhan Ahmed, Fadel, Ahmed, Habater, Abdullah, Hamdy, Heba, Hefni, Amr, Kamal, Marwa, Mohamed Abobakr, Norhan, Sayed, Ahmed, Shaker, Nancy, Taha, Ehab, Tharwat, Hoda, Zakaria, Omar, Abdelmotaleb, Ibrahem, Al-Dhufri, Ali, Al-Himyari, Hamza S., El sheikh, Enas, Eldmaty, Asmaa, Elkhalawy, Aya, M.Elkhashen, Ahmed, Magdy, Kithara, Mostafa, Safa, Sadia, Habib Doutoum, Saleh, Mohamed mahmoud, Samir, Dina, Yahia Mohamed Ali, Mohamed, A. Nassar, Mahmoud, Abdelhady, Samar, Abdelrazek, Aly, Abdelsalam, Israa, El-Sawy, Aya, Essam, Eman, Gadelkarim, Mohamed, Ghaly, Khaled, Hassabalnaby, Mohamed, Masarani, Rana, Mohamed Shaaban, Nourhan, Sabry, Ahmed, Salem, Menatalla, Soliman, Nourhan Akram, Zahran, Diaaaldin, Abou El.soud, Moustafa Ramadan, Badr, Esraa Tarek, Borham, Hala, Elmeslemany, Nehal, Elsayed, Mohammad, Elsherif, Fawzia, Eslam, Sara, Gaber, Gehad, Ibrahim, Sondos, Kamh, Yara, Mahmoud, Abdelrahman, Mohamed, Shimaa gamal, Morshedy, Eman, Omar, Cinderella, Salem Soliman, Fatima, Abdelkawy, Shaza, Abdelmohsen, Naglaa, Abdelshakour, Mahmoud, Dahy, Ahmed, Gamal, Norhan, Gamal, Mohammed, Hasan, Ahmad, Hetta, Helal, Mousa, Nehad, Omar, Mohamed, Rabie, Somia, Saad, Mahmoud, Saleh, Bakeer, Sayed Mohamed, Marwa, Shawqi, Muhammad, Abdelhady Mousa, Heba, Alnoury, Mostafa, Elbealawy, Mohamed, Elshafey, Ahmed, Essam Ibrahim El Desouki Muhammad Ahmed, Muhammad, Ghonaim, Mennatullah, Hgag, Fawzy, Ibrahim, Mohamed, Morsy, Mahmoud, Reda Loaloa, Mohamed, Refaat, Ahmed, Samir, Hadeer, Shahien, Fatma, Sobhy, Mohamed, Sroor, Fathy, Abdellatif, Esraa, Adel, Marina, Afifi, Amr Abdelghani, Afifi, Eman, Antaky, Marco, Dawoud, Amr, El Zoghby, Naira, El-remaily, Amira, Elzanfaly, Ali Abdelazez, Gadallah, Ahmed, Gamal, Fatma Alzahraa, Hashem, Omar, Medhat Youssef, Shrouk, Muhammad Attyah, Aliaa, Munir, Malak, Shazly, Omar, Taha, Esraa, Wilson, Karim, Adel, Sawsan, Ali, Asmaa, Eid, Esraa, Elhelow, Esraa, Elmahdy, Marwa, Elshatby, Bassant, Hossam el-din Zakaria, Amany, Hossny, Ahmad, Ibrahim, Eman, M.Yonis, Ahmed, Metwalli, Maram, Yousry, Basant, Zid, Esraa, A Yacoub, Mina, Abdelhakim, Ahmed, Abouelsoad, Nervana, Alkhatib, Mo'min, Ashraf, Ahmed, Ashraf, Alaa, Elazab, Yasmin, Elfanty, Mahmoud, Elkabir, Osama, Elsayed, Mai, Elshimy, Ahmed, Elsobky, Hager, Eskander, John, Gad, Ahmed, Hamsho, Ward, Khaled Abdelwahed, Noura, Magdy, Menna, Moharam, Dalia, Osama, Abeer, Ramadan, Shereen, Roum, Radwa, Sayed, Taqwa, Shehada, Tarneem, Zidan, Ahmed Mohy, Abbas, Khalid, Ali, Amr, Attia, Mohamed, Balata, Mohamed, El Nakeeb, Ayman, Elewaily, Mohamed Ibrahim Elsayed, Elfallal, Ahmed, Elfeki, Hossam, Elkhadragy, Ahmed, Emile, Sameh, Ezzat, Helmy, Hosni, Hasnaa, Mansour, Islam, Omar, Waleed, Othman, Gehad, Sadek, Kareem, Shalaby, Mostafa, Shehab-Eldeen, Noura, Anas khalifa, Rawda, Badr, Helmy, Eldeep, Mostafa, Eldeep, Ahmed, Eldoseuky mohammed, Amany, Khallaf, Salwa, Magdy Hegazy, Eman, Mahmoud, Rokia, Mikhail, Pola, Morsi, Mahmoud, Mowafy, Sara, Raafat, Dina, Safy, Amina, Sera, Marwa, Sera, Ahmed shible, AbdAllah, Mostafa Salim Mohamed, Abdelkader, Muhammad, Abdou, Abdulrahman Osama, Ahmed, Ahmedgaber, Gaafar, Shireen, Ibrahim negm, Fatma, Lapic, Mina, Maher, Ahmed, Mahmoud, Hagar, Mostafa, Ahmed, Samir, Mohamed, Samy, Fatma, Semeda, Nourhan, Shalaby, Hind I., El-taweel, Alaa, Galal Elnagar, Ahmed, Hemidan, Ahmed Gamal, Hussein, Mohamed, Kandil, Ahmed.A., Moawad, Mf, Nasser Hamamah, Ayat Allah, Soliman, Mostafa, Abdelkhalek, Mohamed, Abdelmaksoud Tawakel, Noura, Abdelwahed, Ahmed Mohamed, Abdou, Alrawy, Atallah, Khalid, Elsherbeny, Mohammed Yasser, Emara, Eman, Hamdy, Mohamed, Hamdy, Omar, Haron, Amira, Ismail, Salma, Metwally, Islam Hany, Mohamed Hamed Elgaml, Nihal, Nassar, Ahmed, Refky, Basel, Sadek, Mirna, Saleh, Mahmoud, Yunes, Asmaa, Zakaria, Mai, Zuhdy, Mohammed, Fayed, Notila, Mohammed, Mohammed Mustafa Hassan, Kütner, Sander, Melnik, Priit, Seire, Indrek, Ümarik, Toomas, Ainoa, Eppu, Eerola, Verner, Koppatz, Hanna, Koskenvuo, Laura, Sallinen, Ville, Takala, Sini, Katunin, Jevgeni, Turunen, Arto, Christou, Niki, Mathonnet, Muriel, Lavoue, Vincent, Nyangoh Timoh, Krystel, Soulabaille, Lucie, Lesourd, Romain, Merdrignac, Aude, Sulpice, Laurent, André, Benoît, Chantalat, Elodie, Vaysse, Charlotte, Dousset, Bertrand, Gaujoux, Sebastien, Martin, Gregory, Clonda, Octavian, Juodis, Domantas, Kienle, Klaus, Mravik, Andras, Palmer, Samuel, Szabadhegyi, Gabor, Agbeko, Anita Eseenam, Gyabaah, Solomon, Gyamfi, Frank Enoch, Naabo, Nuhu, Owusu senior, Atta, Yorke, Joseph, Owusu, Frank, Abantanga, Francis, Anyomih, Theophilus Teddy Kojo, Muntaka, Abdul-Jalilu Mohammed, Owusu Abem, Emmanuel, Sheriff, Mohammed, Wondoh, Paul M., Balalis, Dimitrios, Korkolis, Dimitrios, Gkiokas, Georgios, Pantiora, Eirini, Theodosopoulos, Theodosios, Ioannidis, Argyrios, Konstantinidis, Konstantinos, Konstantinidou, Sofia, Machairas, Nikolaos, Paspala, Anna, Prodromidou, Anastasia, Chouliaras, Christos, Papadopoulos, Konstantinos, Baloyiannis, Ioannis, Mamaloudis, Ioannis, Tzovaras, George, Akrida, Ioanna, Argentou, Maria-Ioanna, Germanos, Stylianos, Iliopoulos, Evangelos, Maroulis, Ioannis, Skroubis, George, Theofanis, George, Chatzakis, Christos, Ioannidis, Orestis, Loutzidou, Lydia, Karathanasis, Panagiotis, Michalopoulos, Nikolaos, Theodoropoulos, Charalampos, Theodorou, Dimitrios, Triantafyllou, Tania, Garoufalia, Zoe, Hasemaki, Natasha, Kontos, Michalis, Kouraklis, Gregory, Kykalos, Stylianos, Liakakos, Theodore, Mpaili, Eustratia, Papalampros, Alexandros, Schizas, Dimitrios, Syllaios, Athanasios, Tampaki, Ekaterini Christina, Tsimpoukelis, Antonios, Antonopoulou, Maria Ioanna, Deskou, Eirini, Manatakis, Dimitrios K., Papageorgiou, Dimitrios, Zoulamoglou, Menelaos, Anthoulakis, Christos, Margaritis, Michalis, Nikoloudis, Nikolaos, Campo, Veronica, Ceballos, André, Flores, Mario-Andrés, Giron, Waleska, Ko, Donghyun, Martinez, Gabriel, Rivera Lara, Verónica, Rueda, Nataly, Sanchez, Andres, Tejeda Garrido, Jorge Carlos Guillermo, Alvarez Rivera, Alvaro Eduardo, Bamaca Ixcajoc, Elvis Benjamin, Barreda Zelaya, Lilian Elizabeth, Chacòn-Herrera, Patricia, Corea Ruiz, Ligia Margarita, Echeverria-Davila, Guillermo, Garcia, Mario, García, Danilo, Gutiérrez Mayen, Edgar Fernando, José, Noriega, Mazariegos, Nery, Méndez, Diego, Paniagua Espinoza, Michael, Bardos, David, Benke, Marton, Illes, Kristof, Kokas, Balint András, Szabó, Réka, Appukuttan, Akhila, Asok, Anjitha, D.k, Vijaykumar, Malik, Kapil, Ravishankaran, Praveen, Tapkire, Ritesh, Moorthy, Guru, Abraham, Joyner, Muthuvel, Ramesh, Alapatt, John, Kattepur, Abhay, Pareekutty, Nizamudheen, Garod, Mebanshanbor, Harris, Caleb, Wanniang, Cliff, Gupta, Ashish, Nehra, Deepak, Parshad, Sanjeev, Acharya, Rajgopal, Badwe, Rajendra, Bhandare, Manish, Jain, Urvashi, Kirti, Karishma, Nair, Nita, Shrikhande, Shailesh, Thakkar, Purvi, Anandan, Premkumar, C S, Archana, Holenarasipur Narasannaiah, Arun, Jagarlamudi, Tejaswi, M R, Rashmi, Manangi, Mallikarjuna, Raghavendra, Abhishek, Rao, K. Seshagiri, S, Vinay, Sajjan, Vinay, Shenoy, Aneesh, Shivashankar Chikkanayakanahalli, Santhosh, Tharanath, Kavya, V, Sushmita, Adidharma, Peter, Agarwal, Raksheeth, Anggita Gultom, Phebe, Arifin, Ghafur Rasyid, Billy, Matthew, Elfizri, Zatira, Fahira, Alessa, Felicia, Devi, Gunardi, Triana Hardianti, Johanna, Nadya, Nugrahadi, Nadia Rahmadiani, Panigoro, Sonar Soni, Rahmayanti, Siti, Sihotang, Retta Catherina, Brata, Santi Yuanita, Winoto, Hadi, Barati, Nastaran, Karami, Manoochehr, Khorshidi, Hamidreza, Naderifar, Homa, Abdulla, Mazin A., Coleman, Maggie, Doherty, Ronan J, Hannon, Rob, Murphy, Brenda, Stakelum, Aine, Winter, Des, Aljohmani, Lylas, Farnan, Richard, Seldon, Yeshey, Tan, Tanna, Varghese, Shriya, Alherz, Mohammad, Ather, Muaaz, Bajilan, Mohammad, Graziadei, Vivien, Pilkington, Isobel, Quidwai, Omar, Ridgway, Paul, Shiwani, Haaris, Tahir, Abd al-Rahman, Blunnie, Eimear, Burke, Daniel, Kennedy, Niall, Macdonagh, Kate, O'Neill, Maeve, Rooney, Siobhan, Falco, Giuseppe, Ferrari, Guglielmo, Mele, Simone, Nita, Gabriela Elisa, Ugoletti, Lara, Zizzo, Maurizio, Confalonieri, Gianmaria, Pesenti, Giovanni, Tagliabue, Fulvio, Baronio, Gianluca, Ongaro, Deborah, Pata, Giacomo, Compagnoni, Bruno, Salvadori, Renato, Taglietti, Lucio, D'Alessandro, Nicola, Di Lascio, Pierpaolo, Pascale, Giovanni, Bortolasi, Luca, Campagnaro, Tommaso, Carlini, Massimo, Lisi, Giorgio, Lombardi, Davide, Pedrazzani, Corrado, Spoletini, Domenico, Turri, Giulia, Violi, Paola, Altomare, Donato Francesco, Aquilino, Fabrizio, Musa, Nicola, Papagni, Vincenzo, Picciariello, Arcangelo, Vincenti, Leonardo, Andreotti, Dario, Occhionorelli, Savino, Tondo, Matteo, Basso, Stefano Maria Massimiliano., Ubiali, Paolo, Cirelli, Riccardo, Maino, Marco Enrico Mario, Piozzi, Guglielmo Niccolò, Picone, Emanuele, Scaramuzzo, Rosa, Sinibaldi, Giovanni, Amendola, Alfonso, Anastasio, Lorenzo, Bucci, Luigi, Caruso, Emanuele, Castaldi, Antonio, Di Maso, Sara, Dinuzzi, Vincenza Paola, Esposito, Giovanni, Gaudiello, Maria, Giglio, Mariano Cesare, Greco, Paola Antonella, Luglio, Gaetano, Manfreda, Andrea, Marra, Ester, Mastella, Federica, Pagano, Gianluca, Peltrini, Roberto, Pepe, Vincenzo, Sacco, Michele, Sollazzo, Viviana, Spiezio, Giovanni, Cianchetti, Ettore, Menduni, Nunzia, Carvello, Michele Maria, Di Candido, Francesca, Spinelli, Antonino, Corsi, Fabio, Sorrentino, Luca, Marino, Fabio, Asti, Emanuele Luigi Giuseppe, Bonavina, Luigi, Rausa, Emanuele, Asta, Martina, Belli, Andrea, Bianco, Francesco, Cervone, Carmela, Delrio, Paolo, Falato, Armando, Fares Bucci, Andrea, Guarino, Rita, Pace, Ugo, Rega, Daniela, De Luca, Emilia, Gallo, Gaetano, Sammarco, Giuseppe, Sena, Giuseppe, Vescio, Giuseppina, Santandrea, Letizia, Ugolini, Giampaolo, Zattoni, Davide, Chetta, Nicola, Logrieco, Gaetano, Vanella, Serafino, Garulli, Gianluca, Zanini, Nicola, Bondurri, Andrea, Cammarata, Francesco, Colombo, Francesco, Foschi, Diego, Lamperti, Giulia Maria Beatrice, Maffioli, Anna, Sampietro, Gianluca Matteo, Yakushkina, Al'ona, Zaffaroni, Gloria, Cicuttin, Enrico, Sibilla, Maria Grazia, Impellizzeri, Harmony, Inama, Marco, Moretto, Gianluigi, Mochet, Sylvie, Ponte, Elisa, Usai, Antonella, Mancini, Stefano, Sagnotta, Andrea, Solinas, Luigi, Bolzonaro, Elisa, Tamini, Nicolò, Curletti, Gianluca, Galleano, Raffaele, Malerba, Michele, Campanella, Sofia, Cocorullo, Gianfranco, Colli, Francesco, De Marco, Paolino, Falco, Nicolò, Fontana, Tommaso, Kamdem Mambou, Leonel jospin, La Brocca, Antonella, Licari, Leo, Randisi, Brenda, Rizzo, Giovanna, Rotolo, Giulia, Salamone, Giuseppe, Tutino, Roberta, Venturelli, Paolina, Malabarba, Stefano, Sgrò, Alessandro, Vella, Ivan, Cirillo, Bruno, Crocetti, Daniele, De Toma, Giorgio, Lapolla, Pierfrancesco, Mingoli, Andrea, Sapienza, Paolo, Belvedere, Angela, Bianchini, Stefania, Binetti, Margherita, Birindelli, Arianna, Tonini, Valeria, Podda, Mauro, Pulighe, Fabio, De Rosa, Michele, Bono, Lorenzo, Borghi, Felice, Geretto, Paolo, Giuffrida, Maria Carmela, Lauro, Corrado, Marano, Alessandra, Pellegrino, Luca, Salusso, Paola, Sasia, Diego, Campanelli, Michela, Realis Luc, Alberto, Trompetto, Mario, Cardia, Roberto, Cillara, Nicola, Giordano, Antonio Nicola, Costanzo, Antonio, Giovilli, Mario Alessandro, Turati, Luca, Canonico, Silvestro, Sciaudone, Guido, Selvaggi, Francesco, Selvaggi, Lucio, Albsoul, Nader, AlBsoul, Ahmad, Alkhatib, Ala'a Aldeen, Alsallaq, Osama, Amarin, Justin Z., Ayoub, Rami, Bsisu, Isam, El Muhtaseb, M S, Jabaiti, Mohammad, Melhem, Jamal, Qwaider, Yasmeen Z., Salameh, Mohammad Hasan, Suleihat, Ahmad, Suradi, Haya H., Alammarin, Mohammad, Aljaafreh, Almoutuz, Bani hani, Mohammad, Bani hani, Zeina, Bani Hani, Farah, Fahmawee, Toqa, Hamouri, Shadi, Katanani, Cyrine, Tawalbeh, Ra'fat, Tawalbeh, Tamara, Zawahrah, Hassan, Abou Chaar, Mohamad K., Abusalem, Lana, Al-Masri, Mahmoud, Al-Najjar, Hani, Barghuthi, Lutfi, Ahmed, Zahra, Maulana, Adnan, Ngotho, Omar, Kamau, Charbel, Stanley Mwenda, Aruyaru, Bosire, Fridah, Mwachiro, Elizabeth, Parker, Robert, Simel, Ian, Sylvester, Kimutai, Althini, Abdulmunem Ahmed Mustafa, Elbarouni, Sofian, Elbeshina, Aya Elseed, Gwea, Ahmed, Malek, Ans, Masoud Farag, Wedad Albashir, Abdalei, Abdulwahab, Abdel Malik, Abu Baker, Abo-khammash, Areej, Abuhlaiga, Ma'aly, Adnan, Nour, Albaggar, Marwa, Alfitory, Asma, Aljanfi, Asma, Almuzghi, Fakhruddin, Altumei, Zohoor, Alzabti, Fatima, Ashoushan, Hana, Assalhi, Mohamed, Azzubia, Joma, Bnhameida, Sondos, Delhen, Malik, Elshafei, Houssein, Elteir, Hana, Esbaga, Fatima, Gobbi, Abdel Aziz, Hamouda, Fatma, Hilan, Hamdan, Ismail, Rania, Jebran, Fieruz, Kasbour, Muataz, Maderi, Galia, Mohammad, Saja, Mohammed, Burooj, Murtadi, Habib, Mustafa, Hamassat, Rajab, Mohamed, Trenba, Sarah, Wafaa, Mariam, Al Sagheir, Eman, Almigheerbi, Alabas, Alzahaf, Ahmed, Bahroun, Sumayyah Ghayth, Ben Dallah, Najah, Elshaibani, Mahmoud, Eswaye, Haitem, Karar, Maha, Omar, Samah, Younes, Eman, Younes, Maha, Zreeg, Dafer, Abujamra, Saleh, Ashour, Firas, Elgammudi, Mala, Omar F. Aljadidi, Wesal, Saddouh, Enas, Sharif, Randa, Alabuzidi, Aya, Alwerfally, AbdulMawlay, Aribi, Sarra, Bibas, Fatma, Elfaituri, Taha, Elhajjaji, Yasmine, Khaled, Ala, Khalil, Wegdan, Layas, Tesneem, Soula, Enas, Tarek, Ahmed, Abu hallalah, Muad fathi khalleefah, Ahmed, Hazem Abdelkarem, Alsharef, Tagwa, Ben Saoud, Abdulsalam Ali, El Gharmoul, Tasnim, Elhadi, Ahmed, Elrais, Safa, Shebani, Abdulhalim, Zarti, Heba, Zeiton, Asaid, Ambrazevicius, Marijus, Kaselis, Nerijus, Stakyte, Migle, Aliosin, Oleg, Cizauskaite, Agne, Dailidenas, Sarunas, Eismontas, Vitalijus, Kybransiene, Migle, Nutautiene, Vitalija, Samalavicius, Narimantas, Simcikas, Dainius, Slepavicius, Algirdas, Tamosiunas, Albinas, Ubartas, Nerijus, Zeromskas, Paulius, Bradulskis, Saulius, Dainius, Edvinas, Juočas, Juozas, Kubiliute, Egle, Kutkevičius, Juozas, Opolskis, Aurimas, Parseliunas, Audrius, Subocius, Andrejus, Virbickaite, Egle, Zuikyte, Diana, Bogusevicius, Algirdas, Buzaite, Kristina, Čepuliené, Daiva, Cesleviciene, Ieva, Cesna, Vaidotas, Gribauskaite, Jolanta, Ignatavicius, Povilas, Jokubauskas, Mantas, Liugailaitè, Monika, Margelis, Ernest, Mazelyte, Ruta, Pankratjevaitè, Lina, Pažusis, Matas, Rackeviciute, Agne, Saladyte, Justina, Škimelytè, Monika, Šlenfuktas, Vygintas, Sudeikyte, Monika, Tamelis, Algimantas, Vanagas, Tomas, Žumbakys, Žygimantas, Atkociunas, Aivaras, Dulskas, Audrius, Kuliavas, Justas, Birutis, Justas, Paškevičius, Sigitas, Šatkauskas, Mindaugas, Danys, Donatas, Jakubauskas, Matas, Jakubauskiene, Lina, Kryzauskas, Marius, Lipnickas, Vytautas, Makūnaitè, Gabija, Rasoaherinomenjanahary, Fanjandrainy, Rasolofonarivo, Herizo, Samison, Luc Hervé, Banda, Bitiel, Msosa, Vanessa, Ahmad Izzuddin, Ahmad Imran, Das, Andre, Gan, Ying Yee, Shong Sheng, Tan, Siaw, Jia yng, Ab Rahim, Mohd Fadliyazid, Abang Jamari, Dyg Zahratul Hamrak, Che Husin, Nurfariza, Kamarulzaman, Muhd Yusairi, Lim, Yi Ping, Mohamed Kamil, Nil Amri, Mohd Hassan, Mohd Razeen, Mohd Sahid, Saidah, Mustafa, Johari, Ng, Elaine Hui Been, Wan Khazim, Wan Khamizar, Chang Ern, Ng, Lingeshan, P.g., Sulaiman, Syariz Ezuan, Ang, Sue Ean, Bin Mohamad Sithik, Muhammad Navid, Cheong, Yih Jeng, Deva Tata, Mahadevan, Jia Xian, Law, Kadravello, Aravinthan, Koh, I-Ern, Ng, Li-Yen, Ng We Yong, Yuki Julius, Palayan, Kandasami, Sam, Chi Xuan, Siow Jin, Phuah, Tan Ern Hwei, Jeremy, Tang, Yita, Ter, Alvin Zubin, Wong, Michael Pak-Kai, Zakaria, Andee Dzulkarnaen, Zakaria, Zaidi, Henry, Fitjerald, Kalaiselvan, Thyivya, Abd Karim, Muhammad Fairuz Shah, Abdul Aziz, Mohamed Rezal, Abdul Aziz, Nora, Khong, Tak Loon, Lau, Peng Choong, Lim, Hiong Chin, Roslani, April Camilla, Seak, Jonathan Chen Ken, Wong, Sui-Weng, Wong, Lai Fen, Yeen Chin, Leow, Anyanwu, Mercy Chinemerem, Busuttil, Zachary, Calleja, Thomas, Chircop, Kurt Lee, Cutajar, Ruth, Dimech, Andrew Michael, Galea, Joseph, Gascon Perai, Kiara, Gatt, Ruth, Kelman, Lisa, Micallef, Elizabeth, Nwolu, Favour, Sammut, Kim, Thompson, Joanna, Warwicker, Sean, Zammit, Matthew, Cordera, Fernando, Cruz González, Efraín, Sánchez-García, Jorge, Barbosa Camacho, Francisco José, Barrera López, Francisco Javier, Zuloaga Fernandez del Valle, Carlos Jose, Acosta, Eric, González Espinoza, Iván Romarico, Moreno, Perla, Cortes-Flores, Ana Olivia, Fuentes Orozco, Clotilde, Gonzalez Ojeda, Alejandro, Corro Díaz González, Samantha, Martinez, Laura, Mosqueda Amador, Bonifacio, Novoa, Armando, Olazo Espejo, Dennet Arturo, Jimenez, Alejandro, Lopez Rosales, Federico, Vanoye, Elva Gabriela, Garcia Gonzalez, Luis Alberto, Miranda-Ackerman, Roberto Carlos, Solano-Genesta, Manuel, Alvarez-Cano, Alethia, Romero-Garza, Hector Hugo, Medina-Franco, Heriberto, Mejía-Fernández, Lorelí, Salgado-Nesme, Noel, Vergara-Fernandez, Omar, Gutiérrez-Mota, Guadalupe Montserrat, Hernandez Vera, Francisco Xavier, Llantada Lopez, Anabella, Morgan Villela, Gilberto, Ramirez Padilla, Felipe de Jesus, Tapia Marin, Walezka, Martínez Maldonado, Mónica, Sánchez Suárez, Ramses, Troche, José Manuel, Benyaiche, Chaymae, Outani, Oumaima, Amine, Souadka, Benkabbou, Amine, Majbar, Anass Mohammed, Mohsine, Raouf, Rafik, Ali, Oung, Thida, Tin, Moe Moe, Plarre, Philipp, Alberga, Anna, Sluiter, Nina, Tuynman, Jurriaan, Blok, Robin, Cömert, Didem, Hompes, Roel, Kalff, Marianne, Stellingwerf, Merel Elisabeth, Tanis, Pieter, van Berge Henegouwen, Mark, van Praag, Elise Maria, Wisselink, Daan, Gerhards, Michael, Lopes Cardozo, Josephine, Westerduin, Emma, de Jonge, Joske, van Geloven, Aaw, van Schilt, Kaz, den Boer, Frank, Stoots, Simone, Vlek, Stijn, Adams, Jamie, Al-Busaidi, Ibrahim S., Budd, Gabrielle, Choi, Seung il, Chu, Michael Jen Jie, Ganugapati, Anurag, McKinstry, Lucy, Pascoe, Rebecca, Richards, Simon, Rosser, Kenrick, Stevenson, Annie, White, Rebecca, Farik, Shebani, Kwun, Jin, Murad, Ahmed, Cowan, Sarah, Hall, Timothy, Hayton, Michael, Malam Sani, Laminou, Oumarou Garba, Souleymane, Amadou Magagi, Ibrahim, Habou, Oumarou, Aliyu, Halima, Daniyan, Muhammad, Sholadoye, Tunde T., Abdullahi, Lawal, Anyanwu, Lofty-John, Mohammad Mohammad, Aminu, Muhammad, Abubakar Bala, Sheshe, Abdurrahman Abba, Suleiman, Ibrahim, Adesina, Alaba, Awolowo, Ajibola, Onuoha, Clement, Salami, Omotayo, Taiwo, Ogechukwu, Taiwo, Agboola, Kache, Stephen, Makama, Jerry Godfrey, Sale, Danjuma, Abiola, Olajide, Ajao, Akinlabi, Ajiboye, Anthony, Etonyeaku, Amarachukwu, Olaogun, Julius, Adebanjo, Ademola, Adesanya, Opeoluwa, Afolayan, Michael Olatunji, Balogun, Olanrewaju, Makanjuola, Ayomide, Nwokocha, Samuel, Ojewola, Rufus Wale, Olajide, Thomas Olagboyega, Aderounmu, Adewale, Adesunkanmi, Abdul-Rashid, Agbakwuru, Augustine, Akeem Aderogba, Adeleke, Alatise, Olusegun Isaac, Arowolo, Olukayode, Lawal, Oladejo, Mohammed, Tajudeen, Ndegbu, Chinedu, Olasehinde, Olalekan, Wuraola, Funmilola, Akinkuolie, Akinbolaji, Mosanya, Arinzechukwu, Ayandipo, Omobolaji, Elemile, Peter, Lawal, Taiwo Akeem, Ali SANI, Samuel, Garba, Stephen, Hauwa SANI, Rebecca, Olori, Samson, Onyebuashi, Henry, Umoke, Ifeanyi, Adenuga, Adedire, Adeyeye, Ademola, Habeeb, Olufemi, Lawal, Bashir, Nasir, Abdulrasheed, Aahlin, Eirik Kjus, Kjønås, Didrik, Myrseth, Elisabeth, Abbasy, Jibran, Alvi, Abdul, Saleem, Omair, Afzal, Asma, Nazir, Anam, Farooq, Muhammad, Liaqat, Ayesha, Naqi, Syed Asghar, Raza, Ali, Sarfraz, Muzna, Sarwar, Muhammad, Banglani, Muntaha, Munir, Ambreen, Sehrish, Rahmat, Ayub, Bushra, Sayyed, Raza, Altaf, Amna, Ayub, Saima, Saeed, Komal, Syed, Bilal, Akbar, Sana Amir, Anwer, Abdul Wahid, Khan, Ruqayya Naheed, Khan, Amina Iqbal, Khattak, Shahid, Mohtasham, Sameen, Parvaiz, Muhammad Asad, Syed, Aamir Ali, Ansari, Abdul Basit, Shahzad, Noman, Khaliq, Tanwir, Rashid, Isbah, Waqar, Shahzad Hussain, Abu Al-saleem, Hasan, Abu Alqumboz, Amjad, Alqadi, Mohammad, Amro, Adham, Assa, Rawan, Awesat, Eman, Ayyad, Rawan, Hammad, Mohammed, Haymony, Ayat, Hijazi, Bassel, Hmeidat, Bara, Lahaseh, Rowaa, Qawasmi, Aseel, Rajabi, Alaa, Shehada, Mohammed, Shkokani, Sundus, Yaghi, Yasmine, Yaghi, Nadine, AlZohour, Mohammad, Farid, Mohammad, Habes, Yousef Mahmoud, Juba, Wesam, Nubani, Yanal, Rabee, Abdelrahman, Sa'deh, Mohammad, Abed, Saeed, Al basos, Iyad, Alswerki, Mohammad, Ashour, Dina, Awad, Israa, Diab, Samar, El Jamassi, Alaa, El-Kahlout, Sahar, Elhout, Somaya, Hajjaj, Ahmed N K, Hasanain, Doaa, Nabil hajjaj, Baraa, Obaid, Mohammed, Saikaly, Eman, Salhi, Ahmed, Al-Tammam, Hiba, Almasri, Murad, Baniowda, Muath, Beshtawi, Doha, Horoub, Ali, Misk, Rami, Mohammad, Bayan, Qasrawi, Rami, Sholi, Tasnim, Abu-Nimeh, Samar, Abu-srour, Abrar, Abukhalaf, Sadi A., Adawi, Samer, Alsalameh, Barah, Ayesh, Kholoud, Elqadi, Muawiyah, Hammouri, Ahmad, Karim Mustafa, Fatima, Marzouqa, Natalie, Melhem, Shatha, Miqdad, Dima, Mohamad, Balqees, Rawhi, Mhammed, Abu Ahammala, Ayman B., Abu Ataya, Ahmed, Abu Jayyab, Israa, Al-Shwaikh, Samar, Alagha, Othman, Alasttal, Mohammed, Awadallah, Haneen, Elblbessy, Mahmood, Fares, Jehad, Jarbou, Akram, Mahfouz, Ibtisam, Albahnasawi, Moath A., Abo mahadi, Asmaa', Abuelhatal, Hasan, Abuelqomboz, Ayham, Almoqayyad, Abdelrahman, Alwali, Abdallah, Balaawi, Reem, Hamouda, Mahmoud, Humeid, Mohammed, Jedyan, Abdullah, Mahmoud Abu hamam, Tasneem, Matar, Ghadeer, Salem, Ali, Samra, Tahani, Shaheen, Nureddin, Shihada, Karam, A.Nemer, Ayoob, Abu Al Amrain, Mahmoud, Abu Alamrain, Abdulwhhab, Abu Jamie, Najlaa, Abu-Rous, Mohammed R., Alfarra, Nada, AlTaweel, Mohammed, Alwhaidi, Noor, Hamed, Ramadan, Saqqa, Bader, Shaheen, Ahmad, Aljaber, Dana, Aljaberi, Loay, Alwaheidi, Malak, Jawaada, Assef, Khaldi, Hani, Qahoush, Rami, Qari, Jalil, Saadeh, Rana, Salim, Ahlam, Yacoub, Aseel, Abbas, Abbas, Abu shua'ib, Rana, Abu Zainah, Baraa, AbuSirrees, Mahmoud, Babaa, Basheer, Barhoush, Ola, Belal qadomi, Asef, Daraghmeh, Laith, Haji, Reema, Khatatbeh, Alaa, Khatib, Lana, Qarariah, Salsabeel, Quzmar, Yara, Safadi, Khalil, Salameh, Roqaya, Hassan, Mohammad, Herzallah, Shifaa, Massad, Loai, Nazzal, Ahmed, Nazzal, Ranin, Escobar, Dennis, Machain V, Gustavo Miguel, Rodriguez Gonzalez, Agustin, Chachaima Mar, Jorge Emerson, Chinchihualpa Paredes, Nathaly Olga, Cuba, Vicente, Lopez, Walter, Niquen Jimenez, Maria Milagros, Sanchez Bartra, Nestor Alberto, Sapallanay Ojeda, Olenka, Sequeiros, Diego, Toscano Pacheco, Andrea, Vergara, María, Abarca, Sol, Alcorta, Rodrigo, Borda-Luque, Giuliano, Eusebio Zegarra, Ivan Edward, Luján López, Claudia, Marrufo, Mirella, Mogrovejo, Cinthya, Nomura, Andrea, Rodríguez Angeles, Yamile, Vidal Meza, Maitza Rosario, Zavala, Gabriela, Castillo Arrascue, José Neiser, Hidrogo Cabrera, Jomara Caroline, Larrea vera, José Julio Mariano, Osorio, Miguel, Ylatoma Díaz, Edgar Alcides, Fontanilla, Mark Anthony, Fuentes, Joseph Roy, Salazar, Anna Leah, Dominguez, Genieve, Lopez, Marc Paul, Macalindong, Shiela, Onglao, Mark Augustine, Ramirez, Arjel, Sacdalan, Marie Dione, Tampo, Mayou Martin, Uy, Gemma Leonora, Mangahas, Jeremiah, Yabut, Kenneth, Cañete, Joannes Paul, Cansana, Bernalynn Eris, Castro, Ernes John, Lipana, Maria Kaiserin, Roxas, Manuel Francisco, Zara, Vlu Jean, Chroł, Maciej, Franczak, Paula, Orłowski, Michał, Budzyński, Piotr, Budzyński, Andrzej, Bury, Pawel, Czerwińska, Agata, Dworak, Jadwiga, Dziedzic, Jacek, Kisielewski, Michał, Kulawik, Jan, Lasek, Anna, Małczak, Piotr, Migaczewski, Marcin, Pędziwiatr, Michał, Pisarska, Magdalena, Radkowiak, Dorota, Rubinkiewicz, Mateusz, Rzepa, Anna, Skoczylas, Tomasz, Stanek, Maciej, Truszkiewicz, Katarzyna, Wierdak, Mateusz, Winiarski, Marek, Zarzycki, Piotr, Zub-Pokrowiecka, Anna, Kowalewski, Piotr, Roszkowski, Rafał, Walędziak, Maciej, Tomé, Miguel, Patrocinio, Sara, Guerreiro, Ines, Almeida, Filipe, de Sousa, Xavier, Monteiro, Nuno, Costa Santos, Maria Teresa, de Oliveira, Daniela, Lopes Serra, Marta, Morgado, Daniela, Neves, Christian, Oliveira, Ana Carolina, Pimentel, Alice, Silva, Sofia, Carvalho, Márcia, Carvalho, Lúcia, Magalhães, Joana, Matos, Leonor, Monteiro, Tânia, Ramos, Carlota, Santos, Vanessa, Barbosa, José, Costa-Maia, Jose, Devezas, Vítor, Fareleira, Ana, Fernandes, Cristina, Gonçalves, Diana, Mora, Henrique, Morais, Marina, Silva de Sousa, Fabiana, Catarino Santos, Sara, Logrado, Ana, Tojal, André, Amorim, Edgar, Cunha, Miguel F., Fazenda, Ana, Melo Neves, João Pedro, Sampaio da Nóvoa Gomes Miguel, Inês Isabel, Veiga, Diogo, Azevedo, José, Cardoso Louro, Hugo, Leite, Mariana, Bairos Menezes, Maria, Gama, Bárbara, Brito, Diana, Cruz Martins, Marta Cristina, Graça e Magalhães, André, Longras, Ana Catarina, Lourenço, Rita, Matos, Diana, Castro, Luis, Policarpo, Filipa, Romano, Joana, Monteiro, Cristina, Pinto, Diogo, Duarte, Marina, Fortuna Martins, Sónia, Oliveira, Mariline, Galvão, Diogo, Martins, Lisandra, Silva, Anaisa, Taranu, Viorel, Vieira, Bárbara, Neves, Jessica, Oliveira, Simone, Ribeiro, Hugo, Cinza, Margarida, Felix, Rosa, Machado, Arnaldo, Oliveira, Joana, Patrício, Joana, Pedroso de Lima, Rita, Pereira, Mário, Rocha Melo, Miguel, Velez, Cristina, Abreu da Silva, Alberto, Claro, Mariana, Costa Santos, Daniel, Ferreira, Andreia, Capote, Hugo, Rosado, Daniela, Taré, Filipa, Nogueira, Oriana, Ângelo, Miguel, Baiao, José Miguel, Guimarães, Andreia, Marques, João, Nico Albano, Miguel, Silva, Marta, Valente da Costa, Ana, Vieira Caroço, Teresa, Almeida Braga, Sara, Capunge, Ines, Fragoso, Marta, Guimarães, João, Pinto, Bruno, Ribeiro, João, Angel, Miguel, Fialho, Guilherme, Guerrero, Monica, Campos Costa, Filipa, Cardoso, Diogo, Cardoso, Vasco, Alves, Magda, Estalagem, Inês, Louro, Tiago, Marques, Cláudia, Martelo, Rita, Morgado, Miguel, Canotilho, Rita, Correia, Ana Margarida, Martins, Pedro, Peyroteo, Mariana, Gomes, João, Monteiro, Rita, Romano, Manuela, Alves, Daniela Macedo, Peixoto, Rita, Quintela, Catarina, Jervis, Maria João, Melo, Débora, Pacheco, André, Paixão, Valter, Pedro, Vera, Pimenta, Joana, Pimenta de Castro, João, Rocha, Ana, Beuran, Mircea, Ciubotaru, Cezar, Diaconescu, Bogdan, Hostiuc, Sorin, Negoi, Ionut, Stoica, Bogdan, Anokhin, Evgeny, Kuznetsov, Georgy, Oganezov, Giorgi, Paramzin, Fedor, Romanova, Ekaterina, Rutkovskii, Valeryan, Rutkovskii, Vasilii, Shushval, Mikhail, Zabiyaka, Mikhail, Dzhumabaev, Khasan, Ivanov, Valerii, Mamedli, Zaman, Achkasov, Sergey, Balkarov, Artem, Nabiev, Elnur, Nagudov, Marat, Rybakov, Evgeny, Saifutdinova, Karina, Sushkov, Oleg, Joseph, Lule, Ndayishimiye, Isaac, Faustin, Ntirenganya, Mutabazi, Alphonse Zeta, Mvukiyehe, Jean Paul, Nsengimana, Vizir J.P, Uwakunda, Carine, Abbas, Mohammad Monir, Akeel, Nouf, Aljiffry, Murad, Awaji, Kholoud, Farsi, Ali, Jamjoum, Ghader, Khoja, Ahmad, Maghrabi, Ashraf, Malibary, Nadim, Nassif, Mohammed, Saleem, Abdulaziz, Sultan, Abdullah, Tashkandi, Wail, Tashkandi, Hanaa, Trabulsi, Nora, Ba, Mouhamadou Bachir, Diallo, Adja Coumba, Ndong, Abdourahmane, Cuk, Vladica, Janković, Uroš, Koh, Sharon Zhiling, Koh, Frederick, Lee, Kuok Chung, Lee, Kai Yin, Lee, Sean, Leong, Wei Qi, Lui, Su Ann, Prakash, Prajwala, Grosek, Jan, Norcic, Gregor, Tomazic, Ales, Fitchat, Nicolas, Jaich, Robert, Wineberg, Devorah, Koto, Modise Zacharia, Baiocchi, Daniella, Clarke, Damian, Steenkamp, Christina Johanna, Bannister, Sharon, Boutall, Adam, Chinnery, Galya, Coccia, Anna, Dell, Angela, Karjiker, Parveen, Kloppers, Christo, Loxton, Nicholas, Mabogoane, Tumi, Malherbe, Francois, Panieri, Eugenio, Rayamajhi, Shreya, van Wyngaard, Tirsa, Warden, Claire, Madiba, T E, Pillay, Nivashen, Brooks, Savannah, Kruger, Charlise, Van Der Merwe, Lisa Hannah, Gool, Ferhana, Kariem, Maahir, Bougard, Heather, Kariem, Nazmie, Noor, Fazlin, Pillay, Reantha, Steynfaardt, Leandi, González González, Lucía, Marín Santos, José Miguel, Martín-Borregón, Paula, Martínez Caballero, Javier, Nevado García, Cristina, Rodriguez Fraga, Pastora, De Castro Parga, Gonzalo, Fernández Veiga, Maria Pilar, Garrido López, Lucía, Infante Pino, Hugo, Lages Cal, Irene, López Otero, Marta, Nogueira Sixto, Manuel, Paniagua García Señorans, Marta, Rodríguez Fernández, Laura, Ruano Poblador, Alejandro, Rufo Crespo, Erika, Sanchez-Santos, Raquel, Vigorita, Vincenzo, Alonso Batanero, Ester, Asnel, Dorisme, Cifrian Canales, Isabel, Contreras Saiz, Elisa, De Santiago Alvarez, Irene, Díaz Vico, Tamara, Fernandez Arias, Sebastian, Fernández Martínez, Daniel, García Bernardo, Carmen, García Flórez, Luis Joaquín, Garcia Gutierrez, Carmen, García Munar, Manuel, Márquez Zorrilla Molina, Carlos Alberto, Merayo, Marta, Michi Campos, José Luis, Moreno Gijon, Maria, Otero-Diez, Jorge L., Rodicio Miravalles, Jose Luis, Solar-Garcia, Lorena, Suárez Sánchez, Aida, Truan, Nuria, Alejandre Villalobos, Cristina, Caballero Díaz, Yurena, Jimenez, Marta, Montesdeoca, Dacil, Navarro-Sánchez, Antonio, Vega, Victor, Beltrán de Heredia, Juan, Gómez, Zahira, Jezieniecki, Carlos, Legido Morán, Ana Patricia, Montes-Manrique, Mario, Rodriguez-Lopez, Mario, Ruiz Soriano, María, Trujillo Díaz, Jeancarlos, Vazquez Fernandez, Andrea, Argudo, Nuria, Pera, Miguel, Torrent Jansà, Laia, García Domínguez, Melody, Goded, Ignacio, Roldón Golet, Marta, Talal El-Abur, Issa, Utrilla Fornals, Alejandra, Zambrana Campos, Vanesa, Aguilar Martinez, Maria Del Mar, Bosch, Marina, García-Catalá, Luis, Sánchez-Guillén, Luis, Artigau, Eva, Gomez Romeu, Nuria, Julià Bergkvist, David, Espina Perez, Beatriz, Morató, Olga, Olona, Carles, Diéguez, Beatriz, Forero-Torres, Alexander, Losada, Manuel, Gomez-Abril, Segundo, Gonzálvez, Paula, Martinez, Rosario, Navarro Martínez, Sergio, Payá-Llorente, Carmen, Pérez Rubio, Álvaro, Santarrufina Martinez, Sandra, Sebastián Tomás, Juan Carlos, Trullenque Juan, Ramon, Gegúndez Simón, Alberto, Maté, Paloma, Prieto-Nieto, Maria Isabel, Rubio-Perez, Ines, Urbieta, Aitor, Vicario Bravo, Marina, Abelló, David, Frasson, Matteo, Garcia-Granero, Alvaro, Abad Gurumeta, Alfredo, Abad-Motos, Ane, Lucena-de Pablo, Elena, Nozal, Beatriz, Ripollés-Melchor, Javier, Salvachúa, Rut, Ferrero, Esther, Garcia-Sancho Tellez, Luis, Picardo, Antonio L., Rojo López, Jose Alberto, Zorrilla Matilla, Laura Patricia, Cagigas Fernandez, Carmen, 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Anna, Yong, Shun Qi, Boereboom, Catherine, Hanna, Michael, Walter, Catherine, Greensmith, Thomas Samuel, Mitchell, Rachel, Monaghan, Eimear, Crawford, James, Moug, Susan, Blackwell, James, Boyd-Carson, Hannah, Herrod, Philip, Al-Allaf, Omar, Beattie, Miriam, Bullock, Cameron, Burman, Shivang, Clark, Gemma, Flamey, Nicolas, Flannery, Oliver, Harding, Alexander, Kodiatt, Ben, Lawday, Samuel, Mahapatra, Shivani, Mukundu Nagesh, Navin, Ng, Michael, Rye, Dupinderjit, Yoong, Andrel, Clark, Laura, Deans, Chris, Edirisooriya, Monisha, Carrington, Emma Victoria, Wong, Tsz Lun Ernest, Yusuf, Baasil, Chamberlain, Carla, Duke, Kathryn, Kmiotek, Elizabeth, Botes, Azel, Condie, Natalie, Schrire, Timothy, Shah, Reena, Thomas-Jones, Iolo, Yates, Charlotte, Anthony, Natasha, Matthews, Edward, Sahnan, Kapil, Tankel, James, Tucker, Sally, Winter Beatty, Jasmine, Ziprin, Paul, Duggan, William, Kantartzi, Anastasia, Sridhar, Shruthi, Khaw, Rachel Alys, Srivastava, Prakhar, Underwood, Charlotte, Alves do 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Arciénega Yañez, María del pilar, Ferreira Fernandes, Martin, González, Daniel, Cubas, Santiago, González, María Catalina, Zubiaurre, Vanessa, Demolin, Rodrigo, Giroff, Nicolas, Sciuto, Pablo, Campos, Maite, Rodríguez Cantera, Gabriela, Deepika, Garg, Simuchimba, Elliot, Bulaya, Anadi, Chibuye, Chali, Chirengendure, Bright, Kabale, Mary-Rose, Kabongo, Kizito, Munthali, James, Mweso, Oliver, Pikiti, Francis, Otieno, James, Lai, Log Tung, Blackman, Brighid, Richards, Sophie, Subramaniam, Suren, Karim, Rafid, Kok, Nathan, Lee, Yanni Dion, Ali, Shabina, Sinha, Aanjaneya, Corrigan, Robert, Barnes, Nicole, Wong, Florence, Dennis, Grace, Jedamzik, Julia, Phillips, Emil, Piette, Wivine, Van hentenryck, Marie, Koco, Houenoukpo, Lawani, Souliath, Kassa, Mamo Woldu, Santos Bezerra, Tainá, Gribnev, Petar, Dimitrov, Dobromir, Krastev, Panche, Oum, Sovannarith, Bonghaseh, Divine Tim, Al Farsi, Maryam, Alsharqawi, Nourah, Acevedo, Veronica, Castillo Barbosa, Andrea Carolina, Giron, Felipe, Leon Rodriguez, Jimmy Paul, Kučan, Darko, Rosko, Damir, Barsic, Neven, Župan, Domagoj, Hegazi, Amgad, Trunčíková, Vendula, Fryba, Vladimir, Mohamed, Mostafa, Sultan, Ahmed, Nagi, Ahmed, Rashad Temerik, Abdallah, Elshawy, Mohamed Elemam, Mahmoud, Moustafa Ibrahim, Omar, Shrouk, Anwar, Mohamed, Rageh, Tarek, Elmokadem, Aya, Gaballa, Khaled, Teppo, Sandra, Turunen, Antti, Pengermä, Pasi, Ballouhey, Quentin, Bergeat, Damien, Weyl, Ariane, Hain, Elisabeth, Gyedu, Adam, Yenli, Edwin, Osei-Poku, Dorcas, Rompou, Vaia-Aliki, Zoikas, Athanasios, Gaitanidis, Apostolos, Koukis, Georgios, Perivoliotis, Konstantinos, Tavlas, Panagiotis, Galanos-Demiris, Konstantinos, Zografos, George, Karavokyros, Ioannis, Xanthopoulou, Georgia, Iordanidou, Eirini, Ayau, Fernanda, Garcia, Allan, Damján, Pekli, Wason, Deepender, B L, Ashika, Rangganata, Ervandy, Kamath, Prerna, O'Connor, Donal B, Pinto, Margherita, Perrone, Fabrizio, Tropeano, Francesca Paola, Troilo, Francesca, Bossi, Daniela, Scala, Dario, Pulitanò, Lucrezia, Carella, Marcella, Pietrabissa, Andrea, Gori, Alice, Giraudo, Giorgio, De Simone, Veronica, Russo, Alfio Alessandro, Braccio, Bartolomeo, Al-Taher, Raed, Athamneh, Sarah, Parker, Andrea, Sawiee, Adnan, Kattia, Amina, Salem, Malik, Tababa, Osama, Shaeeb, Zuhour, Syminas, Vilius, Jurgaitis, Jonas, Damulevičienè, Gytè, Svagzdys, Saulius, Razafimanjato, Narindra Njarasoa Mihaja, Chieng Loo, Ling, Tiong, Ing Ching, Wan Muhmad, Wan Farahiyah, Vijeyan, Harinthiran, Li Ying, Teoh, Grech, Gabriella, Arrangoiz, Rodrigo, Jimenez Ley, Vania Brickelia, Arizpe, Daniel, Lagunes Lara, Elizabeth, Castro López, Elizabeth Victoria, Eaazim, Jose, Gordinou de Gouberville, Marije, Bastiaenen, Vivian, Rottier, Simone, Nahab, Fouad, Ji, Maria Yeonhee, Seyoji, Mohammed, Nwachukwu, Callistus, Emeghara, Okechukwu, Muhammed, Sayyid Egbunu, Idowu, Ayodeji, Sowemimo, Olamiposi, Ogundoyin, Olakayode, Akande, Oluwatosin, Lott, Alexander, Nadeem, Maliha, Laghari, Ahsan Ali, Loya, Asif, Mushtaq, Hassan, Abdullah, Muhammad Tariq, Abuhilal, Baseel, Atawneh, Mohammad, Hamdan, Hamdan, Alhabil, Belal, Srour, Abedelrahman, Mousa, Ibrahim, Da Silva Medina, Luis, Bartosiak, Katarzyna, Ferreira, Pedro, Francisco, Vítor, Lemos, Ricardo, Frutuoso, Luísa, Fernandes, Sara, Fonseca, Telma, Pereira, Jorge, Rachadell, Juan, Torre, Ana, Madeira Martins, Filipe, Carvalho, Ana Cristina, Rodrigues Ferreira, Joana, Ribeiro da Silva, Bruno, Devesa, Helena, Vieira, Ana, Mónica, Inês, Amaro, Margarida, Sousa, Diogo, Reia, Marta, Louro, João, Martins, Ana, Dominguez, Joaquina, Santos, Inês, Freitas Oliveira, Nuno Miguel, Pereira, José Carlos, Silva-Vaz, Pedro, Freire, Ligia, Escrevente, Ricardo, Negoita, Valentina Madalina, Shakhmatov, Dmitry, Nezerwa, Yves, Radulovic, Radosav, Obery, Gareth, Viljoen, Francois, Mendes, Tome, Suarez, Antonio, Moncada, Enrique, Fernandez-Hevia, Maria, Curtis Martínez, Carolina, Gil Garcia, Julia Maria, González Zunzarren, Mariana, Idris, Tarig, Eklöv, Karolina, Grahn, Oskar, Amin, Leila, Blomqvist, Malin, Ajani, Costanza, Kraus, Rebecca, Seeger, Nico, Willemin, Melissa, Rayya, Fadi, Ayash, Mohammad, Msouti, Raneem, Kannas, Israa, Abazid, Eias, Esper, Asil, Slim, Skander, Kavcar, Akil Serdar, Aytac, Erman, Dural, Ahmet Cem, Ilker, Ayse, Eray, Ismail Cem, Kurnaz, Eray, Altiner, Saygin, Tepe, Mustafa Deniz, Şahin, Can, Savli, Evrim, Innocent, Aryon, Babirye, Lilian, Diachenko, Andrii, Hordoskiy, Vladislav, Curry, Heather, Chau, Charlene Yat Che, Robertson, Harry, Mahmoud, Arin, Lennon, Hannah, Loi, Lynette, Kirkham, Emily, McCann, Cameron, Watts, Daniel, Gurung, Binay, Wilson, Michael, Tribedi, Thomas, Garofalo, Eleonora, Zahra, Baryab, MacDonald, Scott, Daniels, Ian, Ng, Nathan, Khosla, Shivun, Olivier, James, Yue, Sum Yu Pansy, Suresh, Gayathri, Wellington, Jack, Lorejo, Emmanuel, Mossaad, Mafdi, Crutcher, Madison, Alimi, Marjan, Baiu, Ioana, Abdou, Hossam, Conway, Alison, Peck, Connor, Perdomo Perez, Mauro Andres, Zulu, Stanley, Nakazwe, Mildred, Burger, Sule, Davies, Justine, Donaldson, Rachel, Ede, Chikwendu, Garden, O James, Lesetedi, Chiapo, Mabedi, Charles, Magill, Laura, Makinde Alakaloko, Felix, Makupe, Alex, Monahan, Mark, Mulira, Soloman, Muller, Elmi, Musowoyo, Jospeh, Olory-Togbe, Jean Léon, Roberts, Tracey, Smith, Martin, Tayler, Viki, Windsor, John, Yepez, Raul, Sundar, Sudha, Runigamugabo, Emmy, Verjee, Azmina, Chen, José, Daya, Leonid, El Aroussi, Nouhaila, Farina, Valeria, Gnintedeme Olivier, Tchianze, Gonzales Nacarino, Mauricio, Hammani, Aamr, Honjo, Sarah, Jacobs, Rebecca, Kimura, Hitomi, Nkoronko, Mugisha, Oscullo Yepez, Jasson Javier, Pin Hung, Wei, Raj, Ankit, Romani Pozo, Alina, Rommaneh, Muna, Sassamela Fabiano, Samuel Chimbioputo, Shiroma Gago, Camila Milagros, Srinivas, Abhishekh, Sung, Chia-Yen, Tai, Aswan, Valle Aranda, Yener Cristyell, Venturini, Sara, and Wilguens Lartigue, Jean

Published
2022
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43. Association of circulating tumor DNA with patient prognosis in surgically resected renal cell carcinoma.

Author

Correa, Andres F, Kalashnikova, Ekaterina, Wu, Hsin-Ta, Winters, Ryan M, Balcioglu, Mustafa, Sudhaman, Sumedha, Connolly, Denise C, Gong, Yulan, Uzzo, Robert G, Sethi, Himanshu, ElNaggar, Adam C, Aleshin, Alexey, Liu, Minetta C, and Abbosh, Philip H

Subjects
CANCER relapse, RESEARCH funding, DNA, DESCRIPTIVE statistics, RENAL cell carcinoma, NUCLEIC acids, EXTRACELLULAR space
Abstract

Background Despite complete resection, 20%-50% of patients with localized renal cell carcinoma (RCC) experience recurrence within 5 years. Accurate assessment of prognosis in high-risk patients would aid in improving outcomes. Here we evaluate the use of circulating tumor DNA (ctDNA) in RCC using banked samples and clinical data from a single institution. Methods The cohort consisted of 45 RCC patients (≥pT1b) who underwent complete resection. The presence of ctDNA in plasma was determined using a personalized, tumor-informed ctDNA assay (Signatera RUO, Natera, Inc.). Relationships with outcomes and other relevant clinical variables were assessed. The median follow-up was 62 months. Results Plasma ctDNA was detected in 18 out of 36 patients (50%) pre-operatively and was associated with increased tumor size (mean 9.3 cm vs. 7.0 cm, P  < .05) and high Fuhrman grade (60% grades III-IV vs 27% grade II, P  = .07). The presence of ctDNA, either pre-operatively or at any time post-operatively, was associated with inferior relapse-free survival (HR = 2.70, P  = .046; HR = 3.23, P  = .003, respectively). Among patients who were ctDNA positive at any time point, the sensitivity of relapse prediction was 84% with a PPV of 90%. Of note, ctDNA positivity at a post-surgical time point revealed a PPV of 100% and NPV of 64%. The lack of ctDNA detection at both time points yielded an NPV of 80%. Conclusions Detection of plasma ctDNA using a personalized assay is prognostic of recurrence in patients with resected RCC. Herein, we describe a successful approach for its application and identify potential limitations to be addressed in future studies. [ABSTRACT FROM AUTHOR]

Published
2024
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44. Focused Planar Electromagnetic Waves for Enhanced Near-Field Microwave Imaging With Verification Using Tapered Gradient-Index Lens Antenna

Author

Azin S. Janani, Amin Darvazehban, Sasan Ahdi Rezaeieh, and Amin M. Abbosh

Subjects
Antenna, focused planar wave, radar-based imaging, near-field microwave imaging, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

Radar-based algorithms for electromagnetic (EM) imaging are developed based on the assumption that EM has a local planar wave front inside the imaging domain. However, this might not be the case for majority of utilized antennas as the imaged object is usually located within the near-field zone of the antenna. The impact of that assumption on imaging accuracy and whether utilizing an antenna that can create a focused planar wave front inside the imaging domain improves EM imaging are investigated in torso imaging as an example. Thus, three types of antennas are used to scan the torso; 1) bio-matched loop-dipole, 2) Gradient-Index lens (GRIN), and 3) Tapered GRIN (T-GRIN) lens antenna. The proposed T-GRIN lens antennas is designed to create a focused plane wave propagation inside the torso using tapered trapezoid water-filled cavities inside a host medium. The proposed design improves penetration depth by 33% compared to conventional GRIN lens and 75% compared to the bio-matched loop-dipole antenna, in a wide fractional bandwidth of 83% at 0.7-1.7 GHz. The realized results indicate that generating focused plane wave inside the imaged object, which is realized using T-GRIN lens antenna, improves the detection accuracy by 15 % and 56% compared to conventional GRIN lens and bio-matched loop-dipole antennas, respectively. Moreover, the localization accuracy is improved by 54.5% and 100% compared to conventional GRIN lens and bio-matched loop-dipole antenna, respectively. This study highlights the importance of creating focused planar wave front within the imaging domain for improved detection and localization using microwave techniques.

Published
2022
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45. Transistor Laser Antenna: Electromagnetic Model in Transmit and Receive Modes

Author

Arman Afsari, Paulo De Souza, Amin Abbosh, and Yahya Rahmat-Samii

Subjects
Antenna theory, cavity antenna, laser, optical antenna, rectenna, renewable energy, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

Transistor laser can drive recent innovative technologies like optical antennas and rectennas. To this end, this semiconductor device requires an accurate electromagnetic model capable of determining the antenna characteristics like radiation pattern, directivity, gain, bandwidth, and polarization. Nonetheless, the current semiconductor models of transistor laser describe the absorption and emission of light mainly by simplified expressions and circuit models. These models usually overlook the actual physical geometry and full-wave light-emitting analysis of the device. In this article, a comprehensive computational electromagnetic modeling and characterization is presented for transistor laser. The existing semiconductor and electromagnetic equations are reorganized in a systematic fashion, coupled, and solved numerically to get the electromagnetic field components emitted or absorbed by the device. These fields determine the radiation pattern, directivity, gain, bandwidth, and polarization of transistor laser in transmit and receive modes. The equations involved in the above electromagnetic model are the Poisson and continuity equations incorporating radiative and non-radiative recombination rates, the vector magnetic potential equation interacting with the Hamiltonian operator of electrons in valance and conduction bands, the equation of the dielectric properties fluctuations of semiconductor layers, and the Poynting vector determining the power flow. The constructed model demonstrates agreement with the general performance of the device in experimental reports.

Published
2022
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46. Next-Generation Healthcare: Enabling Technologies for Emerging Bioelectromagnetics Applications

Author

Asimina Kiourti, Amin M. Abbosh, Maria Athanasiou, Toni Bjorninen, Aline Eid, Cynthia Furse, Koichi Ito, Gianluca Lazzi, Mohamed Manoufali, Matteo Pastorino, Manos M. Tentzeris, Katrina Tisdale, Erdem Topsakal, Leena Ukkonen, William G. Whittow, Huanan Zhang, and Konstantina S. Nikita

Subjects
Implants, wearables, wireless power transfer, wireless telemetry, neurosensing, neurostimulation, Telecommunication, TK5101-6720
Abstract

Rapid advances in antennas, propagation, electromagnetics, and materials are opening new and unexplored opportunities in body area sensing and stimulation. Next-generation wearables and implants are seamlessly providing round-the-clock monitoring. In turn, numerous applications are brought forward with the potential to ultimately transform healthcare, sports, consumer electronics, and beyond. This review paper provides a comprehensive overview, discusses challenges and opportunities, and indicates future directions for: (a) enabling technologies needed to make body area sensing and stimulation a reality, and (b) emerging bioelectromagnetics applications that may readily benefit from such technologies.

Published
2022
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47. Sub-6 GHz and mm-Wave 5G Vehicle-to-Everything (5G-V2X) MIMO Antenna Array

Author

Muhammad Ikram, Kamel S. Sultan, Amin M. Abbosh, and Nghia Nguyen-Trong

Subjects
Dipole antenna, intelligent transportation, microwaves, mm-waves, tapered slot antenna, vehicle-to-everything (V2X), Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

A multi-beam and multi-polarized Multiple-Input Multiple-Output (MIMO) antenna system, which operates at Sub-6 GHz and mm-wave frequency bands is realized for 5G Vehicle-to-Everything (5G-V2X) application. Since the vehicle needs to communicate in different scenarios with the others, e.g. vehicles, passengers, control units, and mobile networks, in the surrounding area, an antenna with 360° coverage area is required. In this paper, an eight-element MIMO antenna is designed and implemented for this purpose. Four elements are distributed evenly in a circular substrate placed in the azimuth plane. Meanwhile, the other four elements are printed on two orthogonal substrates which are fixed perpendicularly to the circular substrate. Each radiating component is a tapered slot antenna integrated with a dipole to operate at both microwave and mm-wave bands. The simulated and measured results demonstrate capability of the proposed design in covering the whole surrounding area (360° coverage in azimuth plane). The antenna achieves a realized gain of more than 9 dBi at the mm-wave range and 4 dBi at the microwave range.

Published
2022
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48. Heart diseases classification through deep learning techniques: A review.

Author

Ali, Shatha M., Abbosh, Younis M., Breesam, Aqeel Majeed, Ali, Dia M., and Alhummada, Iman A.

Subjects
*HEART diseases, *BOLTZMANN machine, *NOSOLOGY, *HEART failure, *MYOCARDIAL infarction, *DEEP learning
Abstract

One of the leading causes of loss of life globally is heart disease. This refers to conditions that affect the heart's normal functioning, such as heart attacks and heart failure. Heart disease stands out as a major contributor to global mortality, and early detection performs a crucial function in enhancing the chances of recovery. In trendy years, deep learning (DL) strategies have established promising outcomes in numerous medical applications, which include several types of heart illnesses. DL algorithms can robotically extract applicable capabilities from unprocessed information, making them adequately appropriate for reading complex clinical datasets. By training on big quantities of categorized information, deep studying fashions can discover ways as needed to classify remarkable varieties of heart ailments based totally on numerous enter modalities collectively with electrocardiograms (ECG's), echocardiograms, and scientific pictures. Several studies have confirmed the effectiveness of DL in classifying heart diseases with excessive accuracy and performance. These models are constrained in their ability to effectively identify precise cardiac conditions. However also anticipate future cardiovascular occasions based on risk factors. To conclude, the use of DL to know strategies for the magnificence of heart illnesses shows splendid potential in enhancing evaluation and prognosis. Further investigation and progress within this domain may lead to more accurate and efficient approaches for early detection. This paper comprehensively surveys DL strategies for detecting heart diseases. Several important points that the researchers rely on in their work to obtain the best results were emphasized. Firstly, a comprehensive comparison was made between the research papers, then a focus was made on the techniques used in feature extraction and their impact on the work of deep learning techniques while considering that the same dataset was used. Finally, it was concluded that the highest accuracy obtained when CNN and continuous wavelet transform (CWT(algorithms were applied was 99.6%. The highest accuracy reached when using an elephant herding optimizer turned restricted Boltzmann machine network (EHO-RBM) in 2023 was 99.96%. Multiple Deep Learning methodologies were addressed in this paper. The implementation of these methodologies is categorized according to distinct metrics, and the datasets used for preparation and testing undergo thorough analysis. A complete evaluation of DL strategies for heart disease was given in this overview paper. [ABSTRACT FROM AUTHOR]

Published
2024
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49. Role of Gut Microbiome in Neoadjuvant Chemotherapy Response in Urothelial Carcinoma: A Multi-Institutional Prospective Cohort Evaluation

Author

Bukavina, Laura, primary, Ginwala, Rashida, additional, Eltoukhi, Mohamed, additional, Sindhani, Mohit, additional, Prunty, Megan, additional, Geynisman, Daniel M., additional, Ghatalia, Pooja, additional, Valentine, Henkel, additional, Calaway, Adam, additional, Correa, Andres F., additional, Brown, Jason R., additional, Mishra, Kirtishri, additional, Plimack, Elizabeth R., additional, Kutikov, Alexander, additional, Ghannoum, Mahmoud, additional, Elshaer, Mohammed, additional, Retuerto, Mauricio, additional, Ponsky, Lee, additional, Uzzo, Robert G., additional, and Abbosh, Philip H., additional

Published
2024
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50. Tumor-naïve pre-surgical ctDNA detection is prognostic in clinical stage I lung adenocarcinoma

Author

Hong, Tae Hee, primary, Hwang, Soohyun, additional, Dasgupta, Abhijit, additional, Abbosh, Christopher, additional, Hung, Tiffany, additional, Bredno, Jörg, additional, Walker, Jill, additional, Shi, Xiaojin, additional, Milenkova, Tsveta, additional, Horn, Leora, additional, Choi, Joon Young, additional, Lee, Ho Yun, additional, Cho, Jong Ho, additional, Choi, Yong Soo, additional, Shim, Young Mog, additional, Chai, Shoujie, additional, Rhodes, Kate, additional, Roychowdhury-Saha, Manami, additional, Hodgson, Darren, additional, Kim, Hong Kwan, additional, and Ahn, Myung, additional

Published
2024
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