39 results on '"Bertels, L."'
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2. Clinical course, management and outcomes of COVID-19 in HIV-infected renal transplant recipients: A case series
- Author
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Bertels, L, primary, Manning, K, additional, Redd, A, additional, Du Toit, T, additional, Barday, Z, additional, and Muller, E, additional
- Published
- 2024
- Full Text
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3. Kidney transplant utilising donors after circulatory death: The first report from the African continent
- Author
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Du Toit, T, primary, Manning, K, additional, Bertels, L, additional, Hoffman, G, additional, Thomson, D, additional, and Barday, Z A, additional
- Published
- 2024
- Full Text
- View/download PDF
4. Burnout among surgeons before and during the SARS-CoV-2 pandemic: an international survey
- Author
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Shalaby, M, Elsheikh, A, Hamed, H, Null, N, Elsheik, A, Sakr, A, Fouad, A, Kassem, A, Elfeki, H, Madbouly, K, Alzahrani, K, Marzouk, K, Ali, M, Helal, M, Elsorogy, M, Farid, M, Di Lorenzo, N, Sileri, P, Wexner, S, Khafagy, W, Adeyeye, A, El-Hussuna, A, Frontali, A, Saklani, A, Lelpo, B, Molena, D, Pandey, D, Karbovnichaya, E, Pata, F, Van Ramshor, G, Gallo, G, Spolverato, G, Pellino, G, Bagaglini, G, Rubio-Perez, I, Negoi, I, Frigerio, I, Juloski, J, Ninkovic, M, Franceschilli, M, Azer, M, Efetov, S, Ippoliti, S, Garoufalia, Z, Fazli, M, Dogjani, A, Cherfa, H, Omar, T, Minoldo, J, Alvarez Gallesio, J, Quesada, M, Bacher, A, Kropshofer, S, Ponholzer, F, Tesik, P, Gehwolf, P, Isci, S, Uranitsch, S, Berchtold, V, Samadov, E, Abualsel, A, Mitul, A, Islam, S, Vanlander, A, Van Praet, C, Van Daele, E, Vanommeslaeghe, H, Stijns, J, Abosi-Appeadu, K, Depuydt, M, Allaeys, M, Yves, V, Colleoni, R, Slavchev, M, Elbahrawy, A, Luc, J, Milford, K, Romic, I, Monti, A, Haydal, A, Klein, M, Ocklind, M, Hadi, S, Alqasaby, A, Elganash, A, Daibes, A, Elsaied, A, Elhattab, A, Lotfy, A, Alnashar, A, Elnour, A, Abdelhalim, A, Abdelhamid, A, Abdellatif, A, Abdelmohsen, A, Abdelrafee, A, Elhawary, A, Zidan, A, Eleshra, A, Elkafoury, A, Ezz, A, Abdelmomen, A, Elkased, A, Fawzy, A, Elkhouly, A, Hemidan, A, Abbas, A, Ismail, A, Attia, A, Farid, A, Elnakash, A, Negida, A, Soliman, A, Taki-Eldin, A, Albadry, A, Sanad, A, Elbatal, A, Elgazar, A, Saleh, A, Fahiem, A, Mohamed, A, Nageeb, A, Elmetwally, A, Alkhalegy, A, El-Wakeel, A, Shemes, A, Fadel, B, Lutfi, B, Ali, D, Abolnasr, K, Gamal, E, Abdallah, E, Ahmed, E, Salem, E, Hamed, E, Elshikh, E, Enad, F, Sarhan, F, Abouelnagah, G, Tagg, G, Atef, G, Shaker, G, Beshir, H, Zakaria, H, Barbary, H, Elgendy, H, Sharaf, H, Elnaghi, H, Elghadban, H, Elzayat, I, Fakhr, I, Sallam, I, Abdelmoneim, I, Elnemr, I, Zewar, K, Elalfy, K, Sabet, K, Mansour, K, Osman, K, Elgaly, M, Shams, M, Abozeid, M, Mohammed, M, Elkatt, M, Samaha, M, Mikhael, M, Khalil, M, Alhendawey, M, Elrefai, M, Gabr, M, Fayed, M, Abdelmaksoud, M, Salem, M, Mohamed Mohamed, M, Nabeeh, M, Elsayed, M, Abdelmonem, M, Eldemery, M, Elmesery, M, Fikry, M, Gharbia, M, Omar, M, Elmoghazy, M, Ghazala, M, Hamed, M, Metwally, M, Arnouse, M, Amen, M, Amary, M, Kandel, M, Abuzeid, M, Rabea, M, Sobh, M, Taman, M, Fathy, M, Moustafa, M, Zuhdy, M, Adel, M, Alaa, M, Alawady, M, Edassy, M, Eldesouki, M, Salim, M, Sanad, M, Khalaf, M, Henes, M, Abdelglil, M, Mohmmed, M, Abdelkhalik, M, Shetiwy, M, Elshazli, M, Hegazy, M, Ahmed, M, Abdelhalim, M, Shahein, M, Sofan, M, Hammad, M, Ahmad, M, Milad, N, Farouk, N, Eldesouky, O, Mohamed, O, Mahadel, O, Gaarour, O, Torky, R, Elhafez, R, Adly, R, Nageeb, R, Hamdi, S, Gamal, S, Emile, S, Regal, S, Abdelrasheed, S, Elzeftawy, S, Khashshan, S, Ashraf, T, Khafagy, T, Nabil, T, Abdelazim, T, Rizk, T, Amr, W, Yousef, Y, Youssef, Y, Castaldi, A, Fiore, A, Hasani, A, Mariani, A, Dagorno, C, Antonio, D, Izzo, G, Addari, G, Mangiameli, G, Rea, L, Pio, L, Paci, M, Andrea, P, De Fatico, G, Elvira, T, Schuldes, A, Rihan, E, Moeslein, G, Lederhuber, H, Botros, I, Jaman, I, Doerner, J, Elseberbihy, J, Sherbiny, K, Ghonim, M, Mikrish, A, Aziz, M, Hatm, M, Archid, R, Gendy, S, Ahmad, S, Charalabopoulos, A, Prodromidou, A, Ioannidis, A, Mpaili, E, Boukorou, G, Papadopoulos, G, Liakakos, T, Styliani, V, Agrawal, A, Jain, A, Rashid, A, Mehraj, A, Brahmachari, S, Lakshmi, H, Vishwakarma, K, Parida, L, Sharma, M, Zaieem, M, Makasarwala, M, Nittala, R, Kumar, S, Vikrantmr, S, Junaid, S, Khuller, S, More, V, Ahmed, A, Alomieri, A, Alhamdany, A, Del, M, Najm, G, Lateef, N, Mcnamara, D, Abdelmageed, M, Majeed, M, Troci, A, Porcu, A, Marano, A, Di Bartolomeo, A, Giani, A, Giardino, A, Canfora, A, Balla, A, Barberis, A, Belli, A, Borasi, A, Manetti, A, Mingoli, A, Morini, A, Maurizi, A, Marra, A, Epifani, A, Iossa, A, Parello, A, Guida, A, Maffioli, A, Scafa, A, Spinelli, A, Matarangolo, A, Picciariello, A, Pirozzi, B, Cirillo, B, Gazia, C, Ratto, C, Foppa, C, Marafante, C, Andrea, C, Tanda, C, Guerci, C, Don, C, Zigiotto, D, Coniglio, D, Sasia, D, Visconti, D, Altomare, D, Guaitoli, E, Botteri, E, Pinotti, E, Martinelli, F, Uggeri, F, Bàmbina, F, Falaschi, F, Costanzo, F, La Torre, F, Milana, F, Abbatini, F, De Lucia, F, Tropeano, F, Colombo, F, Ferrara, F, Litta, F, Carrano, F, Orlando, F, Roscio, F, Selvaggi, F, Giarratano, G, Pagano, G, Lisi, G, Argenio, G, Zancana, G, Cavallaro, G, Frazzetta, G, Mariateresa, G, Sciaudone, G, Vella, I, Siragusa, L, Santurro, L, Ferri, L, Petagna, L, Ferrario, L, Pitoni, L, Pignatelli, M, Angrisani, M, Giugliano, M, Inama, M, Marino, M, Veltri, M, Giuffrida, M, Menna, M, Valente, M, Rottoli, M, Sacchi, M, Uccelli, M, Rho, M, Garino, M, Montuori, M, Campanelli, M, Zese, M, De Falco, N, Cillara, N, Mariani, N, Tamini, N, Adorisio, O, Campennì, P, Venturelli, P, Bernante, P, Sapienza, P, Cianci, P, Marsanic, P, Lapolla, P, Tecchio, P, Familiari, P, Fransvea, P, Bruzzaniti, P, Hassan, R, Pirovano, R, Rimonda, R, Di Saverio, S, Di Carlo, S, Perra, T, Campagnaro, T, Testa, V, Andriola, V, Grappelli, V, Capizzi, V, Chiarella, V, Bellato, V, Yanaga, K, Farouk, M, Uraiqat, A, Almasri, M, Nabwana, A, Siboe, M, W, N, Njoroge, G, Ilkul, J, Obure, R, Palkhi, Y, Alkhayat, A, Ali, A, Malek, A, Abdelsayed, E, Zahra, T, Ayoub, L, Sleilati, F, Aoun, R, Algatanesh, N, Fieturi, N, Ng, J, Díaz, A, Duran, E, Guerrero, J, Jasso, M, Flores, M, Felix, M, Angulo, V, Mejdane, A, Aitali, A, Amal, B, Zentar, A, Bensaad, A, Yacir, E, Jawad, F, Rachid, M, Maliki-Alaoui, M, Ouadii, M, Mohammed, O, Thein, N, Koirala, D, Hilling, D, Pouwels, S, Okunlola, A, Adejumo, A, Akinmade, A, Shittu, A, Oluyomi, A, Abiodun, A, Lawal, B, Odion, C, Popoola, A, Jolayemi, E, Shomoye, E, Wuraola, F, Eke, G, Abiyere, H, Oluwasuyi, I, George, I, Njokanma, I, Aremu, I, Dare, J, Abdur-Rahman, L, Oludara, M, Mohammad, M, Adeoluwa, O, Situ, O, Agbonrofo, P, Kewulere, R, Aliyu, Y, Adebowale, Y, Galala, A, Rao, S, Waleed, A, Inam, A, Shaikh, A, Qureshi, A, Muhammad, A, Kerawala, A, Aslam, M, Mehr, A, Javed, A, Ahmad, F, Majid, H, Ahmed, H, Daudi, I, Akhtar, K, Niaz, K, Anwer, M, Amir, M, Hanif, M, Asif, M, Raza, M, Khokhar, M, Jameel, M, Nasir, M, Shafique, M, Ateeb, M, Nadeem, M, Shah, R, Waqar, S, Shah, S, Waseem, T, Ghafoor, T, Fatima, T, Bashir, U, Gonzales, E, Ruiz, L, Freitas, C, De Sousa, X, Al-Bahrani, A, Portela, C, Elgazar, E, Robles, E, Khan, I, Jarboa, L, Khawar, M, Echevarria, M, Bashah, M, Dawdi, S, Musthafa, S, Ali, S, Ciubotaru, C, Bonci, E, Muresan, M, Bogdan, S, Ioan, T, Zubayraeva, A, Derinov, A, Zakharenko, A, Novikova, A, Bashlachev, A, Kaldarov, A, Stanislav, B, Gorin, D, Puzenko, D, Kazachenko, E, Ashimov, E, Medkova, I, Ignatov, I, Sergeevich, K, Sidorova, L, Kiselev, M, Danilov, M, Aleksandr, O, Rodimov, S, Garmanovs, T, Kitsenko, Y, Valery, N, Japhet, N, Sibiany, A, Saadeldin, A, Abuosba, A, Alawadhi, A, Alharbi, A, Althumali, A, Alghuliga, A, Alotaibi, A, Abduraboh, A, Kateb, A, Sindy, A, Al Eisa, A, Almulhim, A, Aljawhari, A, Abozeid, A, Saad, A, Alqarni, A, Alwan, A, Alwusaibie, A, Bafaraj, A, Eldeeb, A, Tarabay, A, Alhedaithy, A, Almaghrabi, A, Abed, A, Abdullah, A, Semilan, A, Farag, M, Khudhayr, E, Hussain, M, Abbas, G, Alqudaihi, H, Abualnaja, Y, Shaheen, A, Mubarak, A, Ali, B, Alhazmi, B, Hijazi, B, Abdulrahman, C, Oyedepo, C, Alzamel, H, Tairab, E, Alsuwaimel, M, Hejazi, S, Alnoqaidan, E, Alhussien, F, Jallad, F, Khadwardi, F, Alghamdi, F, Haddad, F, Sauri, F, Alafghani, H, Alfalah, H, Gad, H, Aboelmagid, H, Ibrahim, H, Elzayady, H, Sharafeldin, H, Sembawa, H, Alabbas, H, Abbas, H, Elgamal, H, Alawfi, H, Al-Sadery, H, Abdelmotaleb, H, Al Hassn, I, Mudawi, I, Nekhala, I, Elsanhoury, K, Said, K, Albeshri, K, Albahooth, K, Mohammed, K, Asar, K, Osman, L, Alzamanan, M, Alnabarawi, M, Althobaiti, M, Al Naeb, M, Hassan, M, Abdelhamid, M, Alyami, M, Mirza, M, Sayouh, M, Alkhayat, M, Basendowah, M, Ghunaim, M, Alhussaini, M, Khoj, M, Sbaih, M, Saeed, M, Abdelaziz, N, Malibary, N, Abdo, N, Amer, N, Al Turki, N, Durayb, N, Yassin, N, Akeel, N, Larbi, N, Alsallum, O, Suliman, O, Elsherbiny, O, Abusalem, O, Albalawi, I, Abutalib, R, Alarabi, R, Khan, R, Alazzam, S, Alghamdi, S, Alsawat, S, Salim, S, Alshukr, S, Alzahrani, S, Golea, S, Alowairdhi, T, Salman, U, Abusiam, W, Abualkhair, W, Saber, W, Tashkandi, W, Alhazmi, W, Yassine, W, Alshabi, Y, Ibrahim, Y, Shahin, Y, Aljathlany, Y, Alnahas, Y, Alrashidi, Y, Wali, Z, Ndong, A, Ba, M, Faye, P, Arbutina, D, Milic, L, Cuk, V, Hussein, A, Mccaul, J, Bertels, L, Pohl, L, Arnold, M, Mbatani, N, Oosthuizen, P, Rayamajhi, S, Vosloo, S, Jooma, U, Landaluce-Olavarria, A, Vázquez-Melero, A, Marcos, A, Puerto Puerto, A, De La Hermosa, A, Senent-Boza, A, Ugarte-Sierra, B, Montalbán, B, Martin-Perez, B, Gomez, C, Colás-Ruiz, E, Santos, E, Senra, F, Mora-Guzmán, I, Dziakova, J, Díaz, J, Silva, J, Laina, J, Tallon-Aguilar, L, Di Martino, M, Chacón, M, Frasson, M, Calvo, M, Millan, M, Tejedoe, P, Pérez-Bertólez, S, Turrado-Rodríguez, V, Elsanosi, A, Abdalbakheet, D, Salim, O, Youssef, M, Barbon, C, Bouchrika, A, Maghrebi, H, Loukil, I, Yildiz, A, Dursun, A, Gulcu, B, Calik, B, Eral, B, Yeşilyurt, D, Yakar, F, Akin, F, Kilinc, G, Uslu, G, Tuncer, K, Koc, M, Leventoğlu, S, Sokmen, S, Atici, S, Kaya, T, Dere, Ü, Kırmızı, Y, Ssenono, K, Lule, H, Mbiine, R, Hamza, A, Peediyakkal, S, Varma, G, Mussa, H, Al-Masari, H, Shehata, M, Seiam, M, Nimir, N, Khare, R, Rashid, S, Kazim, S, Gondal, Z, Sherif, A, Ghanem, A, Helmy, A, Ibrahim, A, Elshaer, A, Marzouk, A, Tamburrini, A, Parente, A, Light, A, Diamantopoulou, A, Singh, B, Gurung, B, Frauenfelder, C, Leo, C, Raptis, D, Thakrar, D, Madhuri, T, Tsounaki, E, Garreffa, E, Soggiu, F, Stavrou, G, Ng, H, Tabasi, H, Nasef, H, Kostakis, I, Jeffery, J, Warusavitarne, J, Lund, J, Qurashi, K, Sahnan, K, Tong, K, Orecchia, L, Kaur, M, Zaidi, M, Ganau, M, Curtis, N, Bhatt, N, Machairas, N, Zafar, N, Toma, O, Sarmah, P, Bassuni, M, Davies, J, Shawer, S, Lewis, S, Subramanian, S, Nadeem, U, Njau, A, Tohamy, A, Pakula, A, Simioni, A, Jarvis, B, Skandalakis, G, Hesham, H, Isaiah, I, Villwock, J, Martin, L, Kress, M, Sebelik, M, Lathan, S, Towfigh, S, Holubar, S, Demeester, S, Alshehari, M, Ghabisha, S, Abdulatef, S, Al-Kubati, W, Obadiel, Y, Gots, A, Nakazwe, M, Chipaila, J, Mazingi, D, Shalaby, Mostafa, ElSheikh, Ahmed M., Hamed, Hosam, null, null, Elsheik, Ahmed, Sakr, Ahmad, Fouad, Amgad, Kassem, Amr, Elfeki, Hossam, Madbouly, Khaled, Alzahrani, Khalid H., Marzouk, Khalid, Ali, Mahmoud, Helal, Mohamed Alaa Abdelmoez, Elsorogy, Mohamed, Farid, Mohamed, Di Lorenzo, Nicola, Sileri, Pierpaolo, Wexner, Steven, Khafagy, Wael, Adeyeye, Ademola, El-Hussuna, Alaa, Frontali, Alice, Saklani, Avanish, Lelpo, Benedettao, Molena, Daniela, Pandey, Diwakar, Karbovnichaya, Elena, Pata, Francesco, Van Ramshor, Gabrielle H., Gallo, Gaetano, Spolverato, Gaya, Pellino, Gianluca, Bagaglini, Giulia, Rubio-Perez, Ines, Negoi, Ionut, Frigerio, Isabella, Juloski, Jovan, Ninkovic, Marijana, Franceschilli, Marzia, Azer, Mina, Efetov, Sergey, Ippoliti, Simona, Garoufalia, Zoe, Fazli, Mohammad Rafi, Dogjani, Agron, Cherfa, Harieche Abdennour Abderahim, Omar, Tilioua, Minoldo, Javier, Alvarez Gallesio, José Maria, Quesada, Matias, Bacher, Annica, Kropshofer, Stephan, Ponholzer, Florian, Tesik, Philip, Gehwolf, Philipp, Isci, Sevim, Uranitsch, Stefan, Berchtold, Valeria, Samadov, Elgun, Abualsel, Abdulmenem, Mitul, Ashrarur Rahman, Islam, S. M. Nazmul, Vanlander, Aude, Van Praet, Charles, Van Daele, Elke, Vanommeslaeghe, Hanne, Stijns, Jasper, Abosi-Appeadu, Kessewa, Depuydt, Martijn, Allaeys, Mathias, Yves, Van Nieuwenhove, Colleoni, Ramiro, Slavchev, Mihail, Elbahrawy, Aly, Luc, Jessica G. Y., Milford, Karen, Romic, Ivan, Monti, Alessio, Haydal, Ashraf, Klein, Mads Falk, Ocklind, Miranda E. K., Hadi, Sabah Anwar, Alqasaby, Abdallah, Elganash, Abdelazim, Daibes, Adel Goda Hussein, Elsaied, Adham, Elhattab, Ahmad, Lotfy, Ahmad, Alnashar, Ahmed, Elnour, Ahmed Abd Elbaset Elsayed Abu, Abdelhalim, Ahmed, Abdelhamid, Ahmed, Abdellatif, Ahmed, Abdelmohsen, Ahmed, Abdelrafee, Ahmed, Elhawary, Ahmed Adel, Zidan, Ahmed Azmy, Eleshra, Ahmed, Elkafoury, Ahmed, Ezz, Ahmed, Abdelmomen, Ahmed Ezzat Elghrieb, Elkased, Ahmed Farag, Fawzy, Ahmed, Elkhouly, Ahmed G., Hemidan, Ahmed Gamal Abouelfetouh Ibrahim, Abbas, Ahmed Hosam Eldin Hasan, Ismail, Ahmed Mahmoud Ahmed, Attia, Ahmed Mohamed, Farid, Ahmed Mohammed, Elnakash, Ahmed Mostafa, Negida, Ahmed, Soliman, Ahmed, Taki-Eldin, Ahmed, Albadry, Ali Almahdy Ali, Sanad, Aly, Elbatal, Amira Alsayed Abdelhai, Elgazar, Amr, Saleh, Amr, Fahiem, Andrew, Mohamed, Anwar Yahya A., Nageeb, Ashraf, Elmetwally, Ashraf S., Alkhalegy, Ayman, El-Wakeel, Ayman, Shemes, Ayman, Fadel, Bashir A., Lutfi, Basma Waseem, Ali, Doaa, Abolnasr, Khaled Samir, Gamal, Ehab, Abdallah, Emad, Ahmed, Emad Ali, Salem, Eman Abdalla Mohamed, Hamed, Esmael Ali, Elshikh, Essam, Enad, Farazdaq, Sarhan, Fetoh Alaaeldin Fetoh, Abouelnagah, Galal, Tagg, Gamal Hassan El, Atef, Gehad, Shaker, George Samir Habib, Beshir, Hatem, Zakaria, Hazem M., Barbary, Hesham, Elgendy, Hesham, Sharaf, Hesham, Elnaghi, Hisham, Elghadban, Hosam, Elzayat, Ibrahim, Fakhr, Ibrahim, Sallam, Ibrahim, Abdelmoneim, Ibrahim Tharwat Mohamed, Elnemr, Islam, Zewar, Karem Shahin Mohamed, Elalfy, Khaled, Sabet, Khaled, Mansour, Khaled Yousery Ibrahim, Osman, Khalid Abdalla Abdelgadir, Elgaly, Maher Elesawi Kamel, Shams, Maher, Abozeid, Mahmoud, Mohammed, Mahmoud M., Elkatt, Mahmoud Mohamed, Samaha, Mahmoud Yahia, Mikhael, Marolla Maher Eskander, Khalil, Medhat M. H. A., Alhendawey, Moaaz, Elrefai, Mohamad, Gabr, Mohamed A., Fayed, Mohamed Abdelaziz Mohamed Abdalla M, Abdelmaksoud, Mohamed, Salem, Mohamed Abouelmagd, Mohamed Mohamed, Mohamed Adel, Nabeeh, Mohamed Adel, Elsayed, Mohamed Ahmed Abdelhalim Ahmed, Abdelmonem, Mohamed Ahmed, Ali, Mohamed Anwar Abdel Razik, Eldemery, Mohamed, Elmesery, Mohamed, Fikry, Mohamed, Gharbia, Mohamed, Omar, Mohamed I., Elmoghazy, Mohamed Ibrahim, Ghazala, Mohamed Jomma, Hamed, Mohamed Korayem Fattouh, Metwally, Mohamed, Arnouse, Mohamed Mohamed Hamdy, Amen, Mohamed Mohsen, Amary, Mohamed Mokhtar, Kandel, Mohamed Mosaad, Abuzeid, Mohamed Mostafa, Rabea, Mohamed, Sobh, Mohamed Ramadan, Taman, Mohamed, Fathy, Mohammad, Moustafa, Mohammad Montaser Hassan, Zuhdy, Mohammad, Adel, Mohammed, Alaa, Mohammed, Alawady, Mohammed, Edassy, Mohammed El, Mohammed, Mohammed Mustafa Hassan, Eldesouki, Mohammed Nabil, Salim, Mohammed Said Mahmoud, Sanad, Mohammed, Khalaf, Mohsen George, Henes, Mohsen Michael, Abdelglil, Momen, Mohmmed, Mona Mhmoud, Abdelkhalik, Morsi Mohamed Morsi, Shetiwy, Mosab, Elshazli, Mostafa, Hegazy, Mostafa, Ahmed, Mostafa Mahmoud, Abdelhalim, Mostafa Mohammed, Shahein, Mostafa, Sofan, Mostafa, Hammad, Muhammed Alaa Moukhtar, Ahmad, Mustafa, Milad, Nader, Farouk, Nehal, Eldesouky, Omnia, Mohamed, Omnia Y., Mahadel, Osama Abdel Salam, Gaarour, Osama, Torky, Radwan Abdelsabour, Elhafez, Raheem El-Gohary Abd, Adly, Ramy Magdy, Nageeb, Ramy Mikhael, Hamdi, Salah, Gamal, Sameh, Emile, Sameh Hany, Regal, Samer, Abdelrasheed, Sayed, Elzeftawy, Shady Ahmed, Khashshan, Sohib Mohammed Mohammed, Ashraf, Tamer, Khafagy, Tamer, Nabil, Tamer, Abdelazim, Tarek, Rizk, Tarek Taher, Amr, Wesam, Yousef, Yousef Mohamed, Youssef, Youssef Abdel Aziz, Castaldi, Antonio, Fiore, Antonio, Hasani, Ariola, Mariani, Aurora, Dagorno, Claire, Antonio, D'Alessandro, Izzo, Giuliano, Addari, Giulio, Mangiameli, Giuseppe, Rea, Lo Dico, Pio, Luca, Paci, Marco, Andrea, Police, De Fatico, GSerena, Elvira, Tartaglia, Schuldes, Alejandro Daniel Lira, Rihan, Eslam, Moeslein, Gabriela, Lederhuber, Hans, Botros, Ibram, Jaman, Ismail, Doerner, Johannes, Elseberbihy, John Rezk Hanna, Sherbiny, Kareem El, Ghonim, Mostafa, Mikrish, Amir, Aziz, Mina, Hatm, Mohamed, Archid, Rami, Gendy, Samuel Elkess Morcos, Ahmad, Sufian, Charalabopoulos, Alexandros, Prodromidou, Anastasia, Ioannidis, Argyrios, Mpaili, Eustratia, Boukorou, Garyfallia, Papadopoulos, Georgios, Liakakos, Theodore, Styliani, Vasileiadou, Agrawal, Abhishek, Jain, Amita, Rashid, Arshad, Mehraj, Asif, Brahmachari, Swagata, Lakshmi, Harish Neelamraju, Vishwakarma, Kushagra, Parida, Lalit, Sharma, Meenakshi, Zaieem, Mohammad, Makasarwala, Murtaza, Nittala, Rigved, Kumar, Sanjeev, Vikrantmr, Sharma, Junaid, Sheikh, Khuller, Somyaa, More, Vinal, Ahmed, Abeer Abdul Hameed, Alomieri, Adil, Alhamdany, Arkan Shubber, Del, Muslim Ka, Najm, Ghadah, Lateef, Nawras Falah, Mcnamara, Deborah, Abdelmageed, Mohammed Elkassaby, Majeed, Mudassar, Troci, Albert, Porcu, Alberto, Marano, Alessandra, Di Bartolomeo, Alessandro, Giani, Alessandro, Giardino, Alessandro, Canfora, Alfonso, Balla, Andrea, Barberis, Andrea, Belli, Andrea, Borasi, Andrea, Manetti, Andrea, Mingoli, Andrea, Morini, Andrea, Maurizi, Angela, Marra, Angelo Alessandro, Epifani, Angelo Gabriele, Iossa, Angelo, Parello, Angelo, Guida, Anna, Maffioli, Anna, Scafa, Anthony Kevin, Spinelli, Antonino, Matarangolo, Antonio, Picciariello, Arcangelo, Pirozzi, Brunella, Cirillo, Bruno, Gazia, Carlo, Ratto, Carlo, Foppa, Caterina, Marafante, Chiara, Andrea, Chierici, Tanda, Cinzia, Guerci, Claudio, Don, Cristine, Zigiotto, Daniele, Coniglio, Denise, Sasia, Diego, Visconti, Diego, Altomare, Donato F., Guaitoli, Eleonora, Botteri, Emanuele, Pinotti, Enrico, Martinelli, Fabio, Uggeri, Fabio, Bàmbina, Fabrizio, Falaschi, Federica, Costanzo, Federico, La Torre, Filippo, Milana, Flavio, Abbatini, Francesca, De Lucia, Francesca, Tropeano, Francesca Paola, Colombo, Francesco, Ferrara, Francesco, 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Grace, Abiyere, Henry, Oluwasuyi, Ige, George, Ihediwa, Njokanma, Iloba Gabriel, Aremu, Isiaka, Dare, Julius Kolajo, Abdur-Rahman, Lukman, Ahmad, Misbahu Haruna, Oludara, Mobolaji Adewale, Mohammad, Mohammad Aminu, Adeoluwa, Ojajuni, Situ, Oladele, Agbonrofo, Peter, Kewulere, Raji Taofiq, Aliyu, Yakubu, Adebowale, Yusuf, Galala, Ahmed, Rao, Satish, Waleed, Aasma, Inam, Aatif, Shaikh, Abdul Razaque, Qureshi, Ahmad Uzair, Muhammad, Aneeqah Din, Ahmed, Arooj, Kerawala, Asad Ali, Aslam, Mohammad, Mehr, Asma, Javed, Ayesha, Ahmad, Farooq, Majid, Haroon Javaid, Ahmed, Hassan, Daudi, Irfan, Akhtar, Khalid, Niaz, Khurram, Anwer, Mariyah, Amir, Mohammed, Hanif, Muhammad Amir, Asif, Muhammad, Raza, Muhammad Asif, Khokhar, Muhammad Imran, Jameel, Muhammad Khurram, Nasir, Muhammad, Shafique, Muhammad Salman, Ateeb, Mujammad, Nadeem, Munawar, Shah, Rahmat Ullah, Waqar, Shahzad Hussain, Shah, Shahzad Alam, Waseem, Talat, Ghafoor, Tariq, Fatima, Tauseef, Bashir, Umar, Gonzales, Erick Ivan Huaman, Ruiz, Luis Angel Garcia, Freitas, Carla, De Sousa, Xavier, Al-Bahrani, Ahmed, Portela, Carlos Antonio Sanchez, Elgazar, Elsayed Aly, Robles, Eloy Morasen, Khan, Irfan Jan, Jarboa, Lutfi, Khawar, Mahwish, Echevarria, Miguel Jose Pinto, Bashah, Moataz M., Dawdi, Salahaldeen, Musthafa, Shameel, Ali, Syed Muhammad, Ciubotaru, Cezar, Bonci, Eduard-Alexandru, Muresan, Mihai-Stefan, Bogdan, Stoica, Ioan, Tanase, Zubayraeva, Albina, Derinov, Aleksandr, Zakharenko, Alexander, Novikova, Anastasia, Bashlachev, Andrey, Kaldarov, Ayrat, Stanislav, Berelavichus, Gorin, David, Puzenko, Dmitriy, Kazachenko, Ekaterina, Ashimov, Erkin, Medkova, Iuliia, Ignatov, Ivan, Sergeevich, Kochetkov Viktor, Sidorova, Lyudmila, Kiselev, Michail, Danilov, Michail, Aleksandr, Ogoreltsev, Rodimov, Sergey, Garmanovs, Tatiana, Kitsenko, Yury, Valery, Nekoval, Japhet, Ntezamizero, Sibiany, Abdulrahman, Saadeldin, Abdelhalim, Abuosba, Abdelrahman, Alawadhi, Abdulbari Mohammed, Alharbi, Abdulhamid, Althumali, Abdullah, Alghuliga, Abdullah, Alotaibi, Abdullah, Abduraboh, Abdullah Fayez, Kateb, Abdullah, Sindy, Abdullah, Al Eisa, Abdulmohsen, Alotaibi, Abdulrahman, Almulhim, Abdulrhman, Aljawhari, Adel Ali, Abozeid, Ahmad Mahmoud, Saad, Ahmad, Alqarni, Ahmed, Alwan, Ahmed, Alwusaibie, Ahmed, Bafaraj, Ahmed, Eldeeb, Ahmed, Tarabay, Ahmed, Mohammed, Mahfoudh, Alhedaithy, Alhanouf, Almaghrabi, Alhassan Hesham, Abed, Ali Ibrahim Eldawy, Abdullah, Alqahtani Ali, Semilan, Anmar, Farag, Mohamed, Khudhayr, Essa, Hussain, Marwah, Abbas, Ghanem, Alqudaihi, Heba, Abualnaja, Yousra, Shaheen, Abelnasser, Mubarak, Ashraf Abdelazeem Mohamed, Ali, Bandar Idrees A., Alhazmi, Barrag, Hijazi, Bilal Ahmed, Abdulrahman, Chadi, Oyedepo, Charles Olajide, Alzamel, Heythem, Tairab, Elsanousi Ibrahim Sabir, Alsuwaimel, Munir A., Hejazi, Soha, Alnoqaidan, Emad, Alhussien, Fade Ahmed, Jallad, Fadi Sami, Khadwardi, Faisal, Alghamdi, Faisal Saleh, Haddad, Feras, Sauri, Fozan, Alafghani, Haitham, Alfalah, Haitham, Gad, Hamada, 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Nouf, Larbi, Noureddine, Alsallum, Ofays, Suliman, Omar AAbu, Elsherbiny, Osama, Abusalem, Osama, Albalawi, Ibrahim Altedlawi, Abutalib, Raid Abdullah, Alarabi, Rayan, Khan, Roaa Ghazi, Alazzam, Saleh, Alghamdi, Saleh, Alsawat, Salem, Salim, Sami, Alshukr, Sarah, Alzahrani, Saud, Golea, Smain, Alowairdhi, Tumadher, Salman, Usama, Abusiam, Wael, Abualkhair, Wael, Saber, Wael, Tashkandi, Wail, Alhazmi, Waleed, Tashkandi, Waleed, Yassine, Wassim Abou, Alshabi, Yaser Ahmad, Ibrahim, Yaser, Shahin, Yasser, Ibrahim, Yassin, Aljathlany, Yousef, Alnahas, Yousef, Alrashidi, Yousef, Wali, Zubair, Ndong, Abdourahmane, Ba, Mamadou, Faye, Papa Mamadou, Arbutina, Dragana, Milic, Ljiljana, Cuk, Vladica, Hussein, Abdinafic Mohamud, Mccaul, Jeannie, Bertels, Laurie, Pohl, Linda, Arnold, Marion, Mbatani, Nomonde, Oosthuizen, Pj, Rayamajhi, Shreya, Vosloo, Susan, Jooma, Uzair, Landaluce-Olavarria, Aitor, Vázquez-Melero, Alba, Marcos, Alberto, Puerto Puerto, Alejandro, De La Hermosa, Alicia Ruiz, 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Tamburrini, A, Parente, A, Light, A, Diamantopoulou, A, Singh, B, Gurung, B, Frauenfelder, C, Leo, C, Raptis, D, Thakrar, D, Madhuri, T, Tsounaki, E, Garreffa, E, Soggiu, F, Stavrou, G, Ng, H, Tabasi, H, Nasef, H, Kostakis, I, Jeffery, J, Warusavitarne, J, Lund, J, Qurashi, K, Sahnan, K, Tong, K, Orecchia, L, Kaur, M, Zaidi, M, Ganau, M, Curtis, N, Bhatt, N, Machairas, N, Zafar, N, Toma, O, Sarmah, P, Bassuni, M, Davies, J, Shawer, S, Lewis, S, Subramanian, S, Nadeem, U, Njau, A, Tohamy, A, Pakula, A, Simioni, A, Jarvis, B, Skandalakis, G, Hesham, H, Isaiah, I, Villwock, J, Martin, L, Kress, M, Sebelik, M, Lathan, S, Towfigh, S, Holubar, S, Demeester, S, Alshehari, M, Ghabisha, S, Abdulatef, S, Al-Kubati, W, Obadiel, Y, Gots, A, Nakazwe, M, Chipaila, J, Mazingi, D, Shalaby, Mostafa, ElSheikh, Ahmed M., Hamed, Hosam, null, null, Elsheik, Ahmed, Sakr, Ahmad, Fouad, Amgad, Kassem, Amr, Elfeki, Hossam, Madbouly, Khaled, Alzahrani, Khalid H., Marzouk, Khalid, Ali, Mahmoud, Helal, Mohamed Alaa Abdelmoez, Elsorogy, Mohamed, Farid, Mohamed, Di Lorenzo, Nicola, Sileri, Pierpaolo, Wexner, Steven, Khafagy, Wael, Adeyeye, Ademola, El-Hussuna, Alaa, Frontali, Alice, Saklani, Avanish, Lelpo, Benedettao, Molena, Daniela, Pandey, Diwakar, Karbovnichaya, Elena, Pata, Francesco, Van Ramshor, Gabrielle H., Gallo, Gaetano, Spolverato, Gaya, Pellino, Gianluca, Bagaglini, Giulia, Rubio-Perez, Ines, Negoi, Ionut, Frigerio, Isabella, Juloski, Jovan, Ninkovic, Marijana, Franceschilli, Marzia, Azer, Mina, Efetov, Sergey, Ippoliti, Simona, Garoufalia, Zoe, Fazli, Mohammad Rafi, Dogjani, Agron, Cherfa, Harieche Abdennour Abderahim, Omar, Tilioua, Minoldo, Javier, Alvarez Gallesio, José Maria, Quesada, Matias, Bacher, Annica, Kropshofer, Stephan, Ponholzer, Florian, Tesik, Philip, Gehwolf, Philipp, Isci, Sevim, Uranitsch, Stefan, Berchtold, Valeria, Samadov, Elgun, Abualsel, Abdulmenem, Mitul, Ashrarur Rahman, Islam, S. M. Nazmul, Vanlander, Aude, Van Praet, Charles, Van Daele, Elke, Vanommeslaeghe, Hanne, Stijns, Jasper, Abosi-Appeadu, Kessewa, Depuydt, Martijn, Allaeys, Mathias, Yves, Van Nieuwenhove, Colleoni, Ramiro, Slavchev, Mihail, Elbahrawy, Aly, Luc, Jessica G. Y., Milford, Karen, Romic, Ivan, Monti, Alessio, Haydal, Ashraf, Klein, Mads Falk, Ocklind, Miranda E. K., Hadi, Sabah Anwar, Alqasaby, Abdallah, Elganash, Abdelazim, Daibes, Adel Goda Hussein, Elsaied, Adham, Elhattab, Ahmad, Lotfy, Ahmad, Alnashar, Ahmed, Elnour, Ahmed Abd Elbaset Elsayed Abu, Abdelhalim, Ahmed, Abdelhamid, Ahmed, Abdellatif, Ahmed, Abdelmohsen, Ahmed, Abdelrafee, Ahmed, Elhawary, Ahmed Adel, Zidan, Ahmed Azmy, Eleshra, Ahmed, Elkafoury, Ahmed, Ezz, Ahmed, Abdelmomen, Ahmed Ezzat Elghrieb, Elkased, Ahmed Farag, Fawzy, Ahmed, Elkhouly, Ahmed G., Hemidan, Ahmed Gamal Abouelfetouh Ibrahim, Abbas, Ahmed Hosam Eldin Hasan, Ismail, Ahmed Mahmoud Ahmed, Attia, Ahmed Mohamed, Farid, Ahmed Mohammed, Elnakash, Ahmed Mostafa, Negida, Ahmed, Soliman, Ahmed, Taki-Eldin, Ahmed, Albadry, Ali Almahdy Ali, Sanad, Aly, Elbatal, Amira Alsayed Abdelhai, Elgazar, Amr, Saleh, Amr, Fahiem, Andrew, Mohamed, Anwar Yahya A., Nageeb, Ashraf, Elmetwally, Ashraf S., Alkhalegy, Ayman, El-Wakeel, Ayman, Shemes, Ayman, Fadel, Bashir A., Lutfi, Basma Waseem, Ali, Doaa, Abolnasr, Khaled Samir, Gamal, Ehab, Abdallah, Emad, Ahmed, Emad Ali, Salem, Eman Abdalla Mohamed, Hamed, Esmael Ali, Elshikh, Essam, Enad, Farazdaq, Sarhan, Fetoh Alaaeldin Fetoh, Abouelnagah, Galal, Tagg, Gamal Hassan El, Atef, Gehad, Shaker, George Samir Habib, Beshir, Hatem, Zakaria, Hazem M., Barbary, Hesham, Elgendy, Hesham, Sharaf, Hesham, Elnaghi, Hisham, Elghadban, Hosam, Elzayat, Ibrahim, Fakhr, Ibrahim, Sallam, Ibrahim, Abdelmoneim, Ibrahim Tharwat Mohamed, Elnemr, Islam, Zewar, Karem Shahin Mohamed, Elalfy, Khaled, Sabet, Khaled, Mansour, Khaled Yousery Ibrahim, Osman, Khalid Abdalla Abdelgadir, Elgaly, Maher Elesawi Kamel, Shams, Maher, Abozeid, Mahmoud, Mohammed, Mahmoud M., Elkatt, Mahmoud Mohamed, Samaha, Mahmoud Yahia, Mikhael, Marolla Maher Eskander, Khalil, Medhat M. H. A., Alhendawey, Moaaz, Elrefai, Mohamad, Gabr, Mohamed A., Fayed, Mohamed Abdelaziz Mohamed Abdalla M, Abdelmaksoud, Mohamed, Salem, Mohamed Abouelmagd, Mohamed Mohamed, Mohamed Adel, Nabeeh, Mohamed Adel, Elsayed, Mohamed Ahmed Abdelhalim Ahmed, Abdelmonem, Mohamed Ahmed, Ali, Mohamed Anwar Abdel Razik, Eldemery, Mohamed, Elmesery, Mohamed, Fikry, Mohamed, Gharbia, Mohamed, Omar, Mohamed I., Elmoghazy, Mohamed Ibrahim, Ghazala, Mohamed Jomma, Hamed, Mohamed Korayem Fattouh, Metwally, Mohamed, Arnouse, Mohamed Mohamed Hamdy, Amen, Mohamed Mohsen, Amary, Mohamed Mokhtar, Kandel, Mohamed Mosaad, Abuzeid, Mohamed Mostafa, Rabea, Mohamed, Sobh, Mohamed Ramadan, Taman, Mohamed, Fathy, Mohammad, Moustafa, Mohammad Montaser Hassan, Zuhdy, Mohammad, Adel, Mohammed, Alaa, Mohammed, Alawady, Mohammed, Edassy, Mohammed El, Mohammed, Mohammed Mustafa Hassan, Eldesouki, Mohammed Nabil, Salim, Mohammed Said Mahmoud, Sanad, Mohammed, Khalaf, Mohsen George, Henes, Mohsen Michael, Abdelglil, Momen, Mohmmed, Mona Mhmoud, Abdelkhalik, Morsi Mohamed Morsi, Shetiwy, Mosab, Elshazli, Mostafa, Hegazy, Mostafa, Ahmed, Mostafa Mahmoud, Abdelhalim, Mostafa Mohammed, Shahein, Mostafa, Sofan, Mostafa, Hammad, Muhammed Alaa Moukhtar, Ahmad, Mustafa, Milad, Nader, Farouk, Nehal, Eldesouky, Omnia, Mohamed, Omnia Y., Mahadel, Osama Abdel Salam, Gaarour, Osama, Torky, Radwan Abdelsabour, Elhafez, Raheem El-Gohary Abd, Adly, Ramy Magdy, Nageeb, Ramy Mikhael, Hamdi, Salah, Gamal, Sameh, Emile, Sameh Hany, Regal, Samer, Abdelrasheed, Sayed, Elzeftawy, Shady Ahmed, Khashshan, Sohib Mohammed Mohammed, Ashraf, Tamer, Khafagy, Tamer, Nabil, Tamer, Abdelazim, Tarek, Rizk, Tarek Taher, Amr, Wesam, Yousef, Yousef Mohamed, Youssef, Youssef Abdel Aziz, Castaldi, Antonio, Fiore, Antonio, Hasani, Ariola, Mariani, Aurora, Dagorno, Claire, Antonio, D'Alessandro, Izzo, Giuliano, Addari, Giulio, Mangiameli, Giuseppe, Rea, Lo Dico, Pio, Luca, Paci, Marco, Andrea, Police, De Fatico, GSerena, Elvira, Tartaglia, Schuldes, Alejandro Daniel Lira, Rihan, Eslam, Moeslein, Gabriela, Lederhuber, Hans, Botros, Ibram, Jaman, Ismail, Doerner, Johannes, Elseberbihy, John Rezk Hanna, Sherbiny, Kareem El, Ghonim, Mostafa, Mikrish, Amir, Aziz, Mina, Hatm, Mohamed, Archid, Rami, Gendy, Samuel Elkess Morcos, Ahmad, Sufian, Charalabopoulos, Alexandros, Prodromidou, Anastasia, Ioannidis, Argyrios, Mpaili, Eustratia, Boukorou, Garyfallia, Papadopoulos, Georgios, Liakakos, Theodore, Styliani, Vasileiadou, Agrawal, Abhishek, Jain, Amita, Rashid, Arshad, Mehraj, Asif, Brahmachari, Swagata, Lakshmi, Harish Neelamraju, Vishwakarma, Kushagra, Parida, Lalit, Sharma, Meenakshi, Zaieem, Mohammad, Makasarwala, Murtaza, Nittala, Rigved, Kumar, Sanjeev, Vikrantmr, Sharma, Junaid, Sheikh, Khuller, Somyaa, More, Vinal, Ahmed, Abeer Abdul Hameed, Alomieri, Adil, Alhamdany, Arkan Shubber, Del, Muslim Ka, Najm, Ghadah, Lateef, Nawras Falah, Mcnamara, Deborah, Abdelmageed, Mohammed Elkassaby, Majeed, Mudassar, Troci, Albert, Porcu, Alberto, Marano, Alessandra, Di Bartolomeo, Alessandro, Giani, Alessandro, Giardino, Alessandro, Canfora, Alfonso, Balla, Andrea, Barberis, Andrea, Belli, Andrea, Borasi, Andrea, Manetti, Andrea, Mingoli, Andrea, Morini, Andrea, Maurizi, Angela, Marra, Angelo Alessandro, Epifani, Angelo Gabriele, Iossa, Angelo, Parello, Angelo, Guida, Anna, Maffioli, Anna, Scafa, Anthony Kevin, Spinelli, Antonino, Matarangolo, Antonio, Picciariello, Arcangelo, Pirozzi, Brunella, Cirillo, Bruno, Gazia, Carlo, Ratto, Carlo, Foppa, Caterina, Marafante, Chiara, Andrea, Chierici, Tanda, Cinzia, Guerci, Claudio, Don, Cristine, Zigiotto, Daniele, Coniglio, Denise, Sasia, Diego, Visconti, Diego, Altomare, Donato F., Guaitoli, Eleonora, Botteri, Emanuele, Pinotti, Enrico, Martinelli, Fabio, Uggeri, Fabio, Bàmbina, Fabrizio, Falaschi, Federica, Costanzo, Federico, La Torre, Filippo, Milana, Flavio, Abbatini, Francesca, De Lucia, Francesca, Tropeano, Francesca Paola, Colombo, Francesco, Ferrara, Francesco, Litta, Francesco, Carrano, Francesco Maria, Orlando, Francesco, Roscio, Francesco, Selvaggi, Francesco, Giarratano, Gabriella, Pagano, Gianluca, Lisi, Giorgio, Argenio, Giulio, Zancana, Giuseppa, Cavallaro, Giuseppe, Frazzetta, Giuseppe, Mariateresa, Grasso, Sciaudone, Guido, Vella, Ivan, Siragusa, Leandro, Santurro, Letizia, Ferri, Lorenzo, Petagna, Lorenzo, Ferrario, Luca, Pitoni, Ludovica, Pignatelli, Marcello Filograna, Angrisani, Marco, Giugliano, Marco, Inama, Marco, Marino, Marco V., Veltri, Marco, Giuffrida, Maria Carmela, Menna, Maria Paola, Valente, Marina, Rottoli, Matteo, Sacchi, Matteo, Uccelli, Matteo, Rho, Maurizio, Garino, Mauro, Montuori, Mauro, Campanelli, Michela, Zese, Monica, De Falco, Nadia, Cillara, Nicola, Mariani, Nicolò Maria, Tamini, Nicolò, Adorisio, Ottavio, Campennì, Paola, Venturelli, Paolina, Bernante, Paolo, Sapienza, Paolo, Cianci, Pasquale, Marsanic, Patrizia, Lapolla, Pierfrancesco, Tecchio, Piero, Familiari, Pietro, Fransvea, Pietro, Bruzzaniti, Placido, Hassan, Redan, Pirovano, Riccardo, Rimonda, Roberto, Di Saverio, Salomone, Di Carlo, Sara, Perra, Teresa, Campagnaro, Tommaso, Testa, Valentina, Andriola, Valeria, Grappelli, Virgilio Michael Ambrosi, Capizzi, Vita, Chiarella, Vito, Bellato, Vittoria, Yanaga, Katsuhiko, Farouk, Mohamed, Uraiqat, Ahmad, Almasri, Mahmoud, Nabwana, Ambrose, Siboe, Mark M. W., W, Njoroge P., Njoroge, Githu, Ilkul, Jh., Obure, Ralph Ombati, Palkhi, Yusuf, Alkhayat, Ali, Ali, Ali Sayed, Malek, Amgad Nashaat Abdel, Abdelsayed, Emad Fahim, Zahra, Tarek, Ayoub, Larissa, Sleilati, Fadi, Aoun, Rany, Algatanesh, Nassib, Fieturi, Nura Ahmed, Ng, Jen Siang, Díaz, Andrés Vega, Duran, Erik Efrain Sosa, Guerrero, José Eaazim Flores, Jasso, Manuel Meza, Flores, Manuel SSalas, Felix, Marcos José Serrato, Angulo, Victor Manuel Pinto, Mejdane, Abdelhadi, Aitali, Abdelmounaim, Amal, Benzakour, Zentar, Aziz, Bensaad, Ahmed, Yacir, El Alami, Jawad, Fassi Fihri Mohamed, Rachid, Mohamed Ghassane, Maliki-Alaoui, Mohamed, Ouadii, Mouaqit, Mohammed, Ouazni, Thein, Nyan, Koirala, Dinesh Prasad, Hilling, Denise, Pouwels, Sjaak, Okunlola, Abiodun Idowu, Adejumo, Adeyinka, Akinmade, Akinola, Shittu, Asimiyu Adekunle, Oluyomi, Ayodele Samuel, Abiodun, Azeez Lateef, Lawal, Bashir, Odion, Clement, Popoola, Ademola, Jolayemi, Edward, Shomoye, El-Zaki, Wuraola, Funmilola Olanike, Eke, Grace, Abiyere, Henry, Oluwasuyi, Ige, George, Ihediwa, Njokanma, Iloba Gabriel, Aremu, Isiaka, Dare, Julius Kolajo, Abdur-Rahman, Lukman, Ahmad, Misbahu Haruna, Oludara, Mobolaji Adewale, Mohammad, Mohammad Aminu, Adeoluwa, Ojajuni, Situ, Oladele, Agbonrofo, Peter, Kewulere, Raji Taofiq, Aliyu, Yakubu, Adebowale, Yusuf, Galala, Ahmed, Rao, Satish, Waleed, Aasma, Inam, Aatif, Shaikh, Abdul Razaque, Qureshi, Ahmad Uzair, Muhammad, Aneeqah Din, Ahmed, Arooj, Kerawala, Asad Ali, Aslam, Mohammad, Mehr, Asma, Javed, Ayesha, Ahmad, Farooq, Majid, Haroon Javaid, Ahmed, Hassan, Daudi, Irfan, Akhtar, Khalid, Niaz, Khurram, Anwer, Mariyah, Amir, Mohammed, Hanif, Muhammad Amir, Asif, Muhammad, Raza, Muhammad Asif, Khokhar, Muhammad Imran, Jameel, Muhammad Khurram, Nasir, Muhammad, Shafique, Muhammad Salman, Ateeb, Mujammad, Nadeem, Munawar, Shah, Rahmat Ullah, Waqar, Shahzad Hussain, Shah, Shahzad Alam, Waseem, Talat, Ghafoor, Tariq, Fatima, Tauseef, Bashir, Umar, Gonzales, Erick Ivan Huaman, Ruiz, Luis Angel Garcia, Freitas, Carla, De Sousa, Xavier, Al-Bahrani, Ahmed, Portela, Carlos Antonio Sanchez, Elgazar, Elsayed Aly, Robles, Eloy Morasen, Khan, Irfan Jan, Jarboa, Lutfi, Khawar, Mahwish, Echevarria, Miguel Jose Pinto, Bashah, Moataz M., Dawdi, Salahaldeen, Musthafa, Shameel, Ali, Syed Muhammad, Ciubotaru, Cezar, Bonci, Eduard-Alexandru, Muresan, Mihai-Stefan, Bogdan, Stoica, Ioan, Tanase, Zubayraeva, Albina, Derinov, Aleksandr, Zakharenko, Alexander, Novikova, Anastasia, Bashlachev, Andrey, Kaldarov, Ayrat, Stanislav, Berelavichus, Gorin, David, Puzenko, Dmitriy, Kazachenko, Ekaterina, Ashimov, Erkin, Medkova, Iuliia, Ignatov, Ivan, Sergeevich, Kochetkov Viktor, Sidorova, Lyudmila, Kiselev, Michail, Danilov, Michail, Aleksandr, Ogoreltsev, Rodimov, Sergey, Garmanovs, Tatiana, Kitsenko, Yury, Valery, Nekoval, Japhet, Ntezamizero, Sibiany, Abdulrahman, Saadeldin, Abdelhalim, Abuosba, Abdelrahman, Alawadhi, Abdulbari Mohammed, Alharbi, Abdulhamid, Althumali, Abdullah, Alghuliga, Abdullah, Alotaibi, Abdullah, Abduraboh, Abdullah Fayez, Kateb, Abdullah, Sindy, Abdullah, Al Eisa, Abdulmohsen, Alotaibi, Abdulrahman, Almulhim, Abdulrhman, Aljawhari, Adel Ali, Abozeid, Ahmad Mahmoud, Saad, Ahmad, Alqarni, Ahmed, Alwan, Ahmed, Alwusaibie, Ahmed, Bafaraj, Ahmed, Eldeeb, Ahmed, Tarabay, Ahmed, Mohammed, Mahfoudh, Alhedaithy, Alhanouf, Almaghrabi, Alhassan Hesham, Abed, Ali Ibrahim Eldawy, Abdullah, Alqahtani Ali, Semilan, Anmar, Farag, Mohamed, Khudhayr, Essa, Hussain, Marwah, Abbas, Ghanem, Alqudaihi, Heba, Abualnaja, Yousra, Shaheen, Abelnasser, Mubarak, Ashraf Abdelazeem Mohamed, Ali, Bandar Idrees A., Alhazmi, Barrag, Hijazi, Bilal Ahmed, Abdulrahman, Chadi, Oyedepo, Charles Olajide, Alzamel, Heythem, Tairab, Elsanousi Ibrahim Sabir, Alsuwaimel, Munir A., Hejazi, Soha, Alnoqaidan, Emad, Alhussien, Fade Ahmed, Jallad, Fadi Sami, Khadwardi, Faisal, Alghamdi, Faisal Saleh, Haddad, Feras, Sauri, Fozan, Alafghani, Haitham, Alfalah, Haitham, Gad, Hamada, Aboelmagid, Hamdy Haggag Ebrahim, Ibrahim, Hamed, Elzayady, Hany M., Sharafeldin, Hatem Abdelrahman Ahmed, Sembawa, Hatem A., Alabbas, Haytham, Abbas, Hazem, Elgamal, Hesham, Alawfi, Homoud, Al-Sadery, Humood, Abdelmotaleb, Hussien Ali, Al Hassn, Ibrahim, Mudawi, Ishag M., Nekhala, Islam, Elsanhoury, Kareem, Said, Khalid Babieker, Albeshri, Khalid A., Albahooth, Khalid, Mohammed, Khalid Fathelrahman Bakier, Asar, Khalid Mohammad Ibrahim, Osman, Luqman, Alzamanan, Mahdi, Alnabarawi, Mahmoud, Althobaiti, Majid, Elsayed, Mohamed Abdelmoneim, Al Naeb, Mohamed, Hassan, Mohamed Salah Eldin, Abdelhamid, Mohamed Sayed, Alyami, Mohammad, Mirza, Mohammad Amin, Sayouh, Mohammad, Alkhayat, Mohammed Amer, Basendowah, Mohammed, Ghunaim, Mohammed, Alhussaini, Mohammed Khalid, Khoj, Mohammed, Sbaih, Mohammed, Saeed, Muhammad Ahmad, Ali, Muhammad Zulfiqar, Abdelaziz, Nabil Yassin Tammam, Malibary, Nadim, Abdo, Nael, Amer, Nasser Mohammed, Al Turki, Neamat Ahmed Ali, Durayb, Norah, Yassin, Nouf, Akeel, Nouf, Larbi, Noureddine, Alsallum, Ofays, Suliman, Omar AAbu, Elsherbiny, Osama, Abusalem, Osama, Albalawi, Ibrahim Altedlawi, Abutalib, Raid Abdullah, Alarabi, Rayan, Khan, Roaa Ghazi, Alazzam, Saleh, Alghamdi, Saleh, Alsawat, Salem, Salim, Sami, Alshukr, Sarah, Alzahrani, Saud, Golea, Smain, Alowairdhi, Tumadher, Salman, Usama, Abusiam, Wael, Abualkhair, Wael, Saber, Wael, Tashkandi, Wail, Alhazmi, Waleed, Tashkandi, Waleed, Yassine, Wassim Abou, Alshabi, Yaser Ahmad, Ibrahim, Yaser, Shahin, Yasser, Ibrahim, Yassin, Aljathlany, Yousef, Alnahas, Yousef, Alrashidi, Yousef, Wali, Zubair, Ndong, Abdourahmane, Ba, Mamadou, Faye, Papa Mamadou, Arbutina, Dragana, Milic, Ljiljana, Cuk, Vladica, Hussein, Abdinafic Mohamud, Mccaul, Jeannie, Bertels, Laurie, Pohl, Linda, Arnold, Marion, Mbatani, Nomonde, Oosthuizen, Pj, Rayamajhi, Shreya, Vosloo, Susan, Jooma, Uzair, Landaluce-Olavarria, Aitor, Vázquez-Melero, Alba, Marcos, Alberto, Puerto Puerto, Alejandro, De La Hermosa, Alicia Ruiz, Senent-Boza, Ana, Ugarte-Sierra, Bakarne, Montalbán, Beatriz Cros, Martin-Perez, Beatriz, Gomez, Caroina Gonzalez, Colás-Ruiz, Enrique, Santos, Esther Garcia, Senra, Fatima, Mora-Guzmán, Ismael, Dziakova, Jana, Díaz, Jeancarlos J. Trujillo, Silva, Jesús, Laina, Juan Luis Blas, Tallon-Aguilar, Luis, Di Martino, Marcello, Chacón, Mario Franco, Frasson, Matteo, Calvo, Mikel Prieto, Millan, Monica, Tejedoe, Patricia, Pérez-Bertólez, Sonia, Turrado-Rodríguez, Víctor, Elsanosi, Abdelrhman Azhari Mohammed, Abdalbakheet, Duaa, Ahmed, Mohamed, Salim, Omer El Faroug H., Youssef, Mohamed, Barbon, Carlotta, Bouchrika, Amal, Maghrebi, Houcine, Loukil, Issam, Yildiz, Alp, Dursun, Ayberk, Gulcu, Baris, Calik, Bulent, Eral, Burak, Yeşilyurt, Değercan, Yakar, Fatih, Akin, Furkan Atakan, Kilinc, Gizem, Uslu, Gülberk, Tuncer, Korhan, Koc, Mehmet Ali, Leventoğlu, Sezai, Sokmen, Selman, Atici, Semra Demirli, Kaya, Tayfun, Dere, Ümit Akın, Kırmızı, Yasemin, Ssenono, Kavuma Daniel, Lule, Herman, Mbiine, Ronald, Hamza, Ahmed, Ali, Shabeer, Peediyakkal, Saidalavi Padinhare, Varma, Gopala Pillay, Mussa, Haidar Aal, Al-Masari, Hayder Makki, Shehata, Mina, Seiam, Moham, Aziz, Muhammad Akram Abdul, Nimir, Nessrein, Khare, Ritu, Rashid, Shahid, Kazim, Shuiab, Gondal, Zafar, Sherif, Ahmed Elshawadfy, Ghanem, Ahmed, Helmy, Ahmed Hazem I., Ibrahim, Ahmed, Elshaer, Ahmed Mohammed, Marzouk, Ahmed Msm, Tamburrini, Alessandro Paolo, Parente, Alessandro, Light, Alexander, Diamantopoulou, Angela, Singh, Baljit, Gurung, Binay, Frauenfelder, Claire, Leo, Cosimo Alex, Raptis, Dimitri, Thakrar, Dixa, Madhuri, Thumuluru Kavitha, Tsounaki, Efthymia, Garreffa, Emanuele, Soggiu, Fiammetta, Stavrou, George, Ng, Hwei Jene, Tabasi, Hani, Nasef, Hazem, Kostakis, Ioannis D., Jeffery, James, Warusavitarne, Janindra, Lund, Jon, Qurashi, Kamran, Sahnan, Kapil, Tong, Kin Seng, Orecchia, Luca, Kaur, Mandeep, Zaidi, Mariam, Ganau, Mario, Hassan, Mohamed Ali Gad, Curtis, Nathan, Bhatt, Nikita, Machairas, Nikolaos, Zafar, Noman, Toma, Omar, Sarmah, Panchali, Bassuni, Majid, Davies, Justin, Shawer, Sami, Shawer, Sherif, Lewis, Sophia, Subramanian, Sivaraman, Ahmad, Suhaib, Nadeem, Uqba, Njau, Aidan, Tohamy, Aley Eldin, Pakula, Andrea M., Simioni, Andrea, Jarvis, Bennie L., Skandalakis, Georgios P., Hesham, Hosai Todd, Isaiah, Isaac A., Villwock, Jennifer, Martin, Linda W., Kress, Melissa, Sebelik, Merry, Lathan, Sanaz, Towfigh, Shirin, Holubar, Stefan D., Demeester, Steve, Alshehari, Mohammed Mohammed Hasan, Ghabisha, Saif Ali, Abdulatef, Shehab Ahmed Ali, Al-Kubati, Waheeb, Obadiel, Yasser Abdurabo, Gots, Alexander, Nakazwe, Mildred, Chipaila, Jackson, and Mazingi, Dennis
- Abstract
BackgroundSARS-CoV-2 pandemic has had many significant impacts within the surgical realm, and surgeons have been obligated to reconsider almost every aspect of daily clinical practice.MethodsThis is a cross-sectional study reported in compliance with the CHERRIES guidelines and conducted through an online platform from June 14th to July 15th, 2020. The primary outcome was the burden of burnout during the pandemic indicated by the validated Shirom-Melamed Burnout Measure.ResultsNine hundred fifty-four surgeons completed the survey. The median length of practice was 10 years; 78.2% included were male with a median age of 37 years old, 39.5% were consultants, 68.9% were general surgeons, and 55.7% were affiliated with an academic institution. Overall, there was a significant increase in the mean burnout score during the pandemic; longer years of practice and older age were significantly associated with less burnout.There were significant reductions in the median number of outpatient visits, operated cases, on-call hours, emergency visits, and research work, so, 48.2% of respondents felt that the training resources were insufficient. The majority (81.3%) of respondents reported that their hospitals were included in the management of COVID-19, 66.5% felt their roles had been minimized; 41% were asked to assist in non-surgical medical practices, and 37.6% of respondents were included in COVID-19 management.ResultsNine hundred fifty-four surgeons completed the survey. The median length of practice was 10 years; 78.2% included were male with a median age of 37 years old, 39.5% were consultants, 68.9% were general surgeons, and 55.7% were affiliated with an academic institution. Overall, there was a significant increase in the mean burnout score during the pandemic; longer years of practice and older age were significantly associated with less burnout.There were significant reductions in the median number of outpatient visits, operated cases, on-call hours, emergency visits
- Published
- 2024
5. What can We Learn From High-Performing Screening Programs to Increase Bowel Cancer Screening Participation in Australia?
- Author
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Flander, L, Dekker, E, Andersen, B, Larsen, MB, Steele, RJ, Malila, N, Sarkeala, T, van der Vlugt, M, de Klerk, C, Knottnerus, B, Bertels, L, Woudstra, A, Spaander, MCW, Fransen, M, Heinavaara, S, Dillon, M, Ouakrim, DA, Jenkins, M, Flander, L, Dekker, E, Andersen, B, Larsen, MB, Steele, RJ, Malila, N, Sarkeala, T, van der Vlugt, M, de Klerk, C, Knottnerus, B, Bertels, L, Woudstra, A, Spaander, MCW, Fransen, M, Heinavaara, S, Dillon, M, Ouakrim, DA, and Jenkins, M
- Abstract
BACKGROUND: Colorectal cancer (CRC) is the second most diagnosed cancer in men and women and second most common cause of cancer death in Australia; Australia's CRC incidence and mortality are among the world's highest. The Australian National Bowel Cancer Screening Program began in 2006; however, only 33% of those approached for the first time by the Program between 2018 and 2019 returned the kit. Of the 5.7 million kits sent during this period, only 44% were returned. Our aim was to identify practices and features of national bowel cancer screening programs in countries with similar programs but higher screening participation, to identify potential interventions for optimising Australian CRC screening participation. METHODS: We searched published and grey literature for CRC screening programs reporting at least 50% screening participation using postal invitation and free return of iFOBT home kits. Interviews were conducted with cancer registry staff and academic researchers, focused on participant and practitioner engagement in screening. RESULTS: National programs in Netherlands, Scotland, Denmark, and Finland reported over 50% screening participation rates for all invitation rounds. Shared characteristics include small populations within small geographic areas relative to Australia; relatively high literacy; a one-sample iFOBT kit; national registration systems for population cancer screening research; and screening program research including randomised trials of program features. CONCLUSIONS: Apart from the one-sample kit, we identified no single solution to persistent Australian low uptake of screening. Research including randomised trials within the program promises to increase participation. IMPACT: This screening program comparison suggests that within-program intervention trials will lead to increased Australian screening participation.
- Published
- 2022
6. Estimating the surgical backlog from the COVID-19 lockdown in South Africa: A retrospective analysis of six government hospitals
- Author
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Chu, K M, primary, Marco, J, additional, Bougard, H, additional, Strauss, C P, additional, Bertels, L, additional, Victor, A E, additional, Van der Walt, L, additional, Goliath, A, additional, and Duvenage, R, additional
- Published
- 2021
- Full Text
- View/download PDF
7. Copernicus Global Land Service: Land Cover 100m: collection 3: epoch 2017: Globe
- Author
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Buchhorn, M., Smets, B., Bertels, L., de Roo, B., Lesiv, M., Tsendbazar, N.-E., Herold, M., Fritz, S., Buchhorn, M., Smets, B., Bertels, L., de Roo, B., Lesiv, M., Tsendbazar, N.-E., Herold, M., and Fritz, S.
- Abstract
Consolidated epoch 2017 from the Collection 3 of annual, global 100m land cover maps. Other available epochs: 2015 2016 2018 2019 Produced by the global component of the Copernicus Land Service, derived from PROBA-V satellite observations and ancillary datasets. The maps include a main discrete classification with 23 classes aligned with UN-FAO's Land Cover Classification System, a set of versatile cover fractions: percentage (%) of ground cover for the 10 main classes a forest type layer quality layers on input data density and on the confidence of the detected land cover change
- Published
- 2020
8. Copernicus Global Land Cover Layers—Collection 2
- Author
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Buchhorn, M., Lesiv, M., Tsendbazar, N.-Er., Herold, M., Bertels, L., Smets, B., Buchhorn, M., Lesiv, M., Tsendbazar, N.-Er., Herold, M., Bertels, L., and Smets, B.
- Abstract
In May 2019, Collection 2 of the Copernicus Global Land Cover layers was released. Next to a global discrete land cover map at 100 m resolution, a set of cover fraction layers is provided depicting the percentual cover of the main land cover types in a pixel. This additional continuous classification scheme represents areas of heterogeneous land cover better than the standard discrete classification scheme. Overall, 20 layers are provided which allow customization of land cover maps to specific user needs or applications (e.g., forest monitoring, crop monitoring, biodiversity and conservation, climate modeling, etc.). However, Collection 2 was not just a global up-scaling, but also includes major improvements in the map quality, reaching around 80% or more overall accuracy. The processing system went into operational status allowing annual updates on a global scale with an additional implemented training and validation data collection system. In this paper, we provide an overview of the major changes in the production of the land cover maps, that have led to this increased accuracy, including aligning with the Sentinel 2 satellite system in the grid and coordinate system, improving the metric extraction, adding better auxiliary data, improving the biome delineations, as well as enhancing the expert rules. An independent validation exercise confirmed the improved classification results. In addition to the methodological improvements, this paper also provides an overview of where the different resources can be found, including access channels to the product layer as well as the detailed peer-review product documentation.
- Published
- 2020
9. Proba-V cloud detection Round Robin: Validation results and recommendations
- Author
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Iannone, R.Q., primary, Niro, F., additional, Goryl, P., additional, Dransfeld, S., additional, Hoersch, B., additional, Stelzer, K., additional, Kirches, G., additional, Paperin, M., additional, Brockmann, C., additional, Gomez-Chova, L., additional, Mateo-Garcia, G., additional, Preusker, R., additional, Fischer, J., additional, Amato, U., additional, Serio, C., additional, Gangkofner, U., additional, Berthelot, B., additional, Iordache, M. -D., additional, Bertels, L., additional, Wolters, E., additional, Dierckx, W., additional, Benhadj, I., additional, and Swinnen, E., additional
- Published
- 2017
- Full Text
- View/download PDF
10. Remote sensing, an important tool for Integrated Coastal Zone Management
- Author
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Bertels, L., Knaeps, E., Kempeneers, P., Deronde, B., and Houthuys, R.
- Published
- 2012
11. An object-based approach to heath land habitat quantity and quality assessment in the framework of NATURA 2000 using hyperspectral airborne AHS images
- Author
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Haest, B., Guy Thoonen, Vanden Borre, J., Spanhove, T., Delalieux, S., Bertels, L., Kooistra, L., Mücher, C. A., and Paul Scheunders
- Subjects
Physics ,Biology - Abstract
Straightforward mapping of detailed heathland habitat patches and their quality using remote sensing is hampered by (1) the intrinsic property of a high heterogeneity in habitat species composition (i.e. high intra-variability), and (2) the occurrence of the same species in multiple habitat types (i.e. low inter-variability). Mapping accuracy of detailed habitat objects can however be improved by using an advanced approach that specifically takes into account and exploits these inherent patch characteristics. To demonstrate the idea, we developed and applied a multi-step mapping framework on a protected semi-natural heathland area in the north of Belgium. The method consecutively consists of (1) a 4-level hierarchical land cover classification of hyperspectral airborne AHS image data, and (2) a kernel-based structural re-classification algorithm in combination with habitat patch object composition definitions. Detailed land cover composition data were collected in 1325 field plots. Multi-variate analysis (Wards clustering; TWINSPAN) of these data led to the design of meaningful land cover classes in a dedicated classification scheme. Subsequently, the data were used as reference for the classification of hyperspectral AHS image data. Linear Discriminant Analysis in combination with Sequential-Floating-Forward-Selection (SFFS-LDA) was applied to classify the hyperspectral images. Classification accuracies of these maps are in the order of 74-93% (Kappa= 0.81-0.92) depending on the classification detail. To subsequently obtain habitat patch (object) maps, the land cover classifications were used as input for a kernel-based spatial re-classification process, in combination with a rule-set that relates specific Natura 2000 habitats with a composition range of the land cover classes. The resulting habitat patch maps illustrate the methodologys potential for detailed heathland habitat characterization using hyperspectral image data, and hence contribute to the improved mapping and understanding of heathland habitat, essential for the EU member states reporting obligations under the Habitats Directive.
- Published
- 2010
12. Improving urban characterization using hyperspectral remote sensing (Woluwe-Brussels)
- Author
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Salvadore, Elga, Demarchi, Luca, T'sas, A., Chormanski, J., Chan, Jonathan Cheung-Wai, Bertels, L., Canters, Frank, Triest, Ludwig, Bronders, J., Batelaan, Okke, Hydrology and Hydraulic Engineering, Cartography and Geographical Information Science, Geography, and Biology
- Subjects
hyperspectral remote sensing - Abstract
no abstract
- Published
- 2010
13. Zusammenhänge zwischen den thermischen und prozesstechnischen Eigenschaften von Flussmitteln und der Porenbildung in Lötstellen
- Author
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Hutter, M., Thomas, T., Bertels, L., Poech, M., and Publica
- Published
- 2009
14. Monitoring of coral reefs using hyperspectral data; a case study: Fordata, Tanimbar, Indonesia
- Author
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Bertels, L., Knaeps, E., Sterckx, S., Deronde, B., Vanderstraete, T., Van Coillie, S., and Goossens, R.
- Subjects
Coral reefs ,Mapping ,ISEW, Indonesia - Published
- 2006
15. Linking biochemical and biophysical variables derived from imaging spectrometers to ecological models - The HyEco'04 Group Shoot
- Author
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Kooistra, L., Clevers, J.G.P.W., Schaepman, M.E., van Dobben, H.F., Sykora, K.V., Holtland, J., Batelaan, O., Debruyn, W., Bogaert, J., Schmidt, A.M., Clement, J., Bloemmen, M.H.I., Muecher, C.A., van den Hoof, C., de Bruin, S., Stuiver, H.J., Zurita Milla, R., Malenovsky, Z., Wenting, P.F.M., Mengesha, T., van Oort, P.A.J., Liras Laita, E., Wamelink, G.W.W., Schaepman-Strub, G., Hung, L.Q., Verbeiren, B., Bertels, L., Sterckx, S., and Hydrology and Hydraulic Engineering
- Subjects
WIMEK ,Landscape Centre ,Laboratory of Geo-information Science and Remote Sensing ,Alterra - Centrum Geo-informatie ,Alterra - Centrum Landschap ,Life Science ,Plantenecologie en Natuurbeheer ,Plant Ecology and Nature Conservation ,Laboratorium voor Geo-informatiekunde en Remote Sensing ,Wageningen Environmental Research ,Centre Geo-information ,PE&RC - Published
- 2005
16. Assessing a river floodplain status using airborne imaging spectrometer data and ground validation - The HyEco'04 project
- Author
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Kooistra, L., Batelaan, Okke, Clevers, J., Sykora, K., Bertels, L., Bogaert, J., Holtland, J., Debruyn, W., Schaepman, M., Van Dobben, H., Wamelink, W., Hyeco Group, 2005, and Hydrology and Hydraulic Engineering
- Published
- 2005
17. Hyperspectral monitoring of coral reefs, a case study: Fordate, Tanimbar, Indonesia
- Author
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Vanderstraete, T., Goossens, R., Sterckx, S., Bertels, L., Debruyn, W., and van der Heijden, P.R.
- Subjects
Coral reefs ,Monitoring ,ISEW, Indonesia ,Spectral analysis ,Remote sensing - Abstract
The overall aim of this study is to monitor coral reefs and associated ecosystems by integrating different remote sensing data with spectral libraries and field measurements. To assess and verify the technical feasibility of a spaceborne hyperspectral sensor, a preliminary bottomtype classification was made based on hyperspectral data from the CHRIS/PROBA sensor. The atmospheric correction was performed with in-house atmospheric correction software, WATCOR, which takes into account atmospheric and air/water-interface effects. Due to the lack of bathymetric information, a water column correction could not be performed. As ground-truth data was available neither, the classification was based on automatic endmember selection using data-inherent spectral and spatial information. After the endmember selection, a Spectral Angle Mapper (SAM) procedure was followed to classify the dataset. Although some problems remain to be solved, the preliminary result presented, shows the potential of spaceborne hyperspectral data for detail coral reef studies.
- Published
- 2005
18. Potentials of airborne hyperspectral remote sensing for vegetation mapping of spatially heterogeneous dynamic dunes, a case study along the Belgian coastline
- Author
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Bertels, L., Deronde, B., Kempeneers, S., and Tortelboom, E.
- Subjects
Vegetation mapping ,Classification - Abstract
The coastal defence and nature conservation authorities from the Ministry of the Flemish Community need detailed vegetation maps of the Belgian coast for policy planning and evaluation. From an Integrated Coastal Zone Management point of view, the development of efficient tools serving both authorities is desirable. Therefore new methods for objective, detailed and cost-efficient vegetation mapping are under investigation. This paper focuses on the application of airborne hyperspectral imagery. Two classification methods are used. The standard Spectral Angle Mapper, performed after a Minimum Noise Fraction transform, gives an overall accuracy of 59% with 15 vegetation classes. When using the Optimized Spectral Angle Mapper, the overall accuracy can be increased to 67% using the same 15 classes.
- Published
- 2005
19. 25 jaar leeg! Het verhaal van de Wilhelmina, de Spoelerij en de Schoorsteen. Een onderzoek naar de factoren die de leegstand en de herbestemming van de Tricotfabriek in Winterswijk hebben beïnvloed.
- Author
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Bertels, L., Denslagen, prof. dr. W.F. (Thesis Advisor), Bertels, L., and Denslagen, prof. dr. W.F. (Thesis Advisor)
- Published
- 2011
20. An object-based approach to quantity and quality assessment of heathland habitats in the framework of natura 2000 using hyperspectral airborne ahs images
- Author
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Haest, B., Thoonen, G., Vanden Borre, J., Spanhove, T., Delalieux, S., Bertels, L., Kooistra, L., Mücher, C.A., Scheunders, P., Haest, B., Thoonen, G., Vanden Borre, J., Spanhove, T., Delalieux, S., Bertels, L., Kooistra, L., Mücher, C.A., and Scheunders, P.
- Published
- 2010
21. Geometric Errors of Remote Sensing Images Over Forest and Their Propagation to Bidirectional Studies
- Author
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Kempeneers, P., primary, Bertels, L., additional, Vreys, K., additional, and Biesemans, J., additional
- Published
- 2013
- Full Text
- View/download PDF
22. Monitoring inland waters with the APEX sensor, a wavelet approach
- Author
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Knaeps, E., primary, Raymaekers, D., additional, Sterckx, S., additional, Bertels, L., additional, and Odermatt, D., additional
- Published
- 2010
- Full Text
- View/download PDF
23. Mapping of coral reefs using hyperspectral CASI data; a case study: Fordata, Tanimbar, Indonesia
- Author
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Bertels, L., primary, Vanderstraete, T., additional, Van Coillie, S., additional, Knaeps, E., additional, Sterckx, S., additional, Goossens, R., additional, and Deronde, B., additional
- Published
- 2008
- Full Text
- View/download PDF
24. Carbon mass fluxes of forests in Belgium determined with low resolution optical sensors
- Author
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Veroustraete, F., primary, Sabbe, H., additional, Rasse, D. P., additional, and Bertels, L., additional
- Published
- 2004
- Full Text
- View/download PDF
25. Automatic generation of land-use maps for a spatial decision support system for Puerto Rico.
- Author
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van der Kwast, J., Delrue, J., Bertels, L., Uljee, I., Van Looy, S., Schepens, J., Engelen, G., Gutie?rrez, E., and Roma?n, G.
- Published
- 2011
- Full Text
- View/download PDF
26. An object-based approach to quantity and quality assessment of heathland habitats in the framework of NATURA 2000 using hyperspectral airborne AHS images
- Author
-
Haest, B., Thoonen, G., Jeroen Vanden Borre, Toon Spanhove, Delalieux, S., Bertels, L., Kooistra, L., Mücher, C. A., Scheunders, P., Addink, E. A., and Van Coillie, F. M. B.
- Subjects
Vegetation ,CGI - Aardobservatie ,Economics ,Application ,Physics ,Contextual ,PE&RC ,Classification ,Natura 2000 monitoring ,Laboratory of Geo-information Science and Remote Sensing ,image analysis ,Object ,Landscape ,Laboratorium voor Geo-informatiekunde en Remote Sensing ,Wageningen Environmental Research ,CGI - Earth Observation ,B290-phytogeography ,Hyper spectral ,Engineering sciences. Technology ,Ecosystem - Abstract
Straightforward mapping of detailed heathland habitat patches and their quality using remote sensing is hampered by (1) the intrinsic property of a high heterogeneity in habitat species composition (i.e. high intra-variability), and (2) the occurrence of the same species in multiple habitat types (i.e. low inter-variability). Mapping accuracy of detailed habitat objects can however be improved by using an advanced approach that specifically takes into account and exploits these inherent patch characteristics. To demonstrate the idea, we developed and applied a multi-step mapping framework on a protected semi-natural heathland area in the north of Belgium. The method consecutively consists of (1) a 4-level hierarchical land cover classification of hyperspectral airborne AHS image data, and (2) a kernel-based structural re-classification algorithm in combination with habitat patch object composition definitions. Detailed land cover composition data were collected in 1325 field plots. Multi-variate analysis (Ward's clustering; TWINSPAN) of these data led to the design of meaningful land cover classes in a dedicated classification scheme. Subsequently, the data were used as reference for the classification of hyperspectral AHS image data. Linear Discriminant Analysis in combination with Sequential-Floating-Forward-Selection (SFFS-LDA) was applied to classify the hyperspectral images. Classification accuracies of these maps are in the order of 74-93% (Kappa=0.81-0.92) depending on the classification detail. To subsequently obtain habitat patch (object) maps, the land cover classifications were used as input for a kernel-based spatial re-classification process, in combination with a rule-set that relates specific Natura 2000 habitats with a composition range of the land cover classes. The resulting habitat patch maps illustrate the methodology's potential for detailed heathland habitat characterization using hyperspectral image data, and hence contribute to the improved mapping and understanding of heathland habitat, essential for the EU member states reporting obligations under the Habitats Directive.
27. Classifying hyperspectral airborne imagery for vegetation survey along coastlines
- Author
-
Kempeneers, P., primary, Deronde, B., additional, Bertels, L., additional, Debruyn, W., additional, De Backer, S., additional, and Scheunders, P., additional
- Full Text
- View/download PDF
28. Classifying hyperspectral airborne imagery for vegetation survey along coastlines.
- Author
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Kempeneers, P., Deronde, B., Bertels, L., Debruyn, W., de Backer, S., and Scheunders, P.
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- 2004
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29. Clinical and Histopathological Findings in HIV-positive to HIV-positive Kidney Transplant Recipients.
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Wearne N, Botha F, Manning K, Price B, Barday Z, Post FA, Freercks R, Bertels L, Mtingi-Nkonzombi L, and Muller E
- Abstract
Background: The spectrum of histological findings in transplanted kidneys from HIV-positive donors to HIV-positive recipients is relatively unexplored. This study describes the type and timing of histological diagnoses observed in this unique cohort., Methods: Adequate biopsies were analyzed at implantation and posttransplant between September 2008 and May 2022. Histological disease spectrum, distributions over time, and relevant clinical characteristics and management were reported for both for-cause and protocol biopsies., Results: Twenty-four implantation biopsies from 31 deceased donors and 179 allograft biopsies (100 for-cause, 79 protocol) from 50 recipients were analyzed. Most rejection episodes occurred in the first year posttransplant. Eighteen recipients (36%) had at least 1 episode of biopsy-confirmed acute/chronic T cell-mediated rejection (TCMR) or active antibody-mediated rejection (AMR). Protocol biopsies showed no active AMR or acute/chronic TCMR. However, 9 of 79 biopsies identified borderline/suspicious TCMR. Common nonrejection diagnoses were interstitial fibrosis and tubular atrophy, ascending pyelonephritis, and calcineurin inhibitor toxicity. Classic and suspected HIV-associated nephropathy (HIVAN) were identified in 3 and 6 patients, respectively. Protocol biopsies diagnosed 1 case of classic HIVAN and 6 cases of suspected HIVAN. AMR most adversely affected kidney function and significantly contributed to graft failure., Conclusions: The histological findings in this cohort of HIV-positive kidney transplant recipients who received grafts from unmatched HIV-positive donors revealed a spectrum of abnormalities. Protocol biopsies added to surveillance on borderline rejection and assisted in the recognition of HIVAN. Confirmed rejection occurred in 18 recipients (36%). Understanding the factors contributing to this may assist in the optimization of immunosuppressive protocols in the future., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)
- Published
- 2024
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30. Autism-friendly public bus transport: A personal experience-based perspective.
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Dirix H, Ross V, Brijs K, Bertels L, Alhajyaseen W, Brijs T, Wets G, and Spooren A
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- Humans, Transportation, Emotions, Adaptation, Psychological, Autistic Disorder, Autism Spectrum Disorder
- Abstract
Lay Abstract: Transportation plays an essential role in daily life, allowing people to participate in the community and form social relationships. Many autistic people rely on public transportation to meet their mobility needs. However, research shows that it is not always easy for them to use it. The exact issues autistic individuals face when traveling with public transportation and how public transportation can be made more autism-friendly have yet to be researched. The current study allowed autistic individuals to express themselves regarding issues they face while traveling by public bus transportation, to raise awareness for making public transportation more autism-friendly. We interviewed 17 autistic individuals about their experiences riding the bus. Three main themes emerged from the results: creating predictability, limiting stimuli, and open and accessible communication. If transport companies take initiatives related to these themes, autistic people traveling by bus can have a more pleasant experience. Participants also described coping strategies for stressful or uncomfortable situations while using public bus transportation, such as using noise-cancelling headphones or digital applications for real-time route tracking, etc. These findings may lead to a more autism-friendly public transportation.
- Published
- 2023
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31. The role of general practitioners in the work guidance of cancer patients: views of general practitioners and occupational physicians.
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Sarfo MC, Bertels L, Frings-Dresen MHW, de Jong F, Blankenstein AH, van Asselt KM, and de Boer AGEM
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- Humans, Return to Work, Communication, Focus Groups, Qualitative Research, General Practitioners, Cancer Survivors, Neoplasms therapy
- Abstract
Purpose: To explore views of general practitioners (GPs) and occupational physicians (OPs) on the role of GPs in work guidance of cancer patients., Methods: Between 2016 and 2019, two focus groups with GPs (N = 17) and two focus groups with OPs (N = 10) were conducted. Focus group discussions were audiotaped and transcribed verbatim. Transcripts were analysed by data-driven analysis., Results: GPs generally indicated that they inquire about patients' occupations but do not structurally document these. GPs described offering support and advice to patients regarding their work, while other GPs stated they do not interfere with their patients' work or return to work (RTW) process. In general, GPs stated that they do not aspire a professional role in the work guidance of patients, due to lack of expertise and not having sufficient knowledge in work regulations and legislation. In contrast, OPs anticipated a proactive role from GPs concerning work guidance in cancer patients, and they expected GPs to refer cancer patients to the OP, when required. Moreover, they emphasised the importance of communication between GPs and OPs about patients' work-related problems to achieve common goals., Conclusions: GPs can contribute to cancer patients' RTW process by supporting patients, giving advice and providing referral to other health professionals. Better cooperation between GPs and OPs may improve work guidance in cancer patients., Implications for Cancer Survivors: When cancer patients with work-related issues get appropriate advice and support from GPs and referred in time to OPs, the RTW process and staying at work of cancer patients may be positively affected., (© 2022. The Author(s).)
- Published
- 2023
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32. Retrospective Review of ART Regimens in HIV-Positive to HIV-Positive Kidney Transplant Recipients.
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Barday Z, Manning K, Freercks R, Bertels L, Wearne N, and Muller E
- Abstract
Introduction: The management of complex interactions between antiretroviral therapy (ART) and calcineurin inhibitor (CNI) immunosuppression regimens in HIV-positive to HIV-positive renal transplant recipients can be challenging. Literature describing ART regimens and indications for regimen switching in these patients is limited., Methods: This retrospective review included 53 HIV-positive to HIV-positive renal transplant recipients. Data on ART regimens, reasons for ART switching, and timing of switches were described from day of transplant to study endpoint (end of study date, death, or graft failure). The association between rejection and ART regimen (protease inhibitor [PI] -based vs. non-PI-based regimen) was analyzed using negative binomial regression., Results: There were a total of 46 switches in 31 of 53 patients (58%). Protocol switches ( n = 17 of 46, 37%) accounted for most switches, of which the majority were from non-nucleoside reverse transcriptase inhibitors (NNRTIs) to PIs. Other common reasons for switching include cytochrome P450 enzyme induction from efavirenz (EFV) (9 of 46, 20%), tenofovir disoproxil fumarate (TDF) nephrotoxicity (8 of 46, 17%) or side effects (6 of 46, 13%). Of the 46 switches, nearly half ( n = 21, 46%) occurred during the transplant admission period, and approximately two-thirds ( n = 28, 62%) were during the first year post-transplantation. There was an association between rejection and being maintained on a PI-based regimen (incidence rate ratio 2.77 (95% confidence interval 1.03-7.48), P = 0.044)., Conclusion: Despite frequent switching of ART regimens, HIV viral loads remained supressed and graft function remained stable in most HIV-positive kidney transplant recipients in our cohort. There was however a concerning signal for increased rejection rates in those on a PI-based regimen., (© 2022 International Society of Nephrology. Published by Elsevier Inc.)
- Published
- 2022
- Full Text
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33. A benchmark dataset for Hydrogen Combustion.
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Guan X, Das A, Stein CJ, Heidar-Zadeh F, Bertels L, Liu M, Haghighatlari M, Li J, Zhang O, Hao H, Leven I, Head-Gordon M, and Head-Gordon T
- Abstract
The generation of reference data for deep learning models is challenging for reactive systems, and more so for combustion reactions due to the extreme conditions that create radical species and alternative spin states during the combustion process. Here, we extend intrinsic reaction coordinate (IRC) calculations with ab initio MD simulations and normal mode displacement calculations to more extensively cover the potential energy surface for 19 reaction channels for hydrogen combustion. A total of ∼290,000 potential energies and ∼1,270,000 nuclear force vectors are evaluated with a high quality range-separated hybrid density functional, ωB97X-V, to construct the reference data set, including transition state ensembles, for the deep learning models to study hydrogen combustion reaction., (© 2022. The Author(s).)
- Published
- 2022
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34. NewtonNet: a Newtonian message passing network for deep learning of interatomic potentials and forces.
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Haghighatlari M, Li J, Guan X, Zhang O, Das A, Stein CJ, Heidar-Zadeh F, Liu M, Head-Gordon M, Bertels L, Hao H, Leven I, and Head-Gordon T
- Abstract
We report a new deep learning message passing network that takes inspiration from Newton's equations of motion to learn interatomic potentials and forces. With the advantage of directional information from trainable force vectors, and physics-infused operators that are inspired by Newtonian physics, the entire model remains rotationally equivariant, and many-body interactions are inferred by more interpretable physical features. We test NewtonNet on the prediction of several reactive and non-reactive high quality ab initio data sets including single small molecules, a large set of chemically diverse molecules, and methane and hydrogen combustion reactions, achieving state-of-the-art test performance on energies and forces with far greater data and computational efficiency than other deep learning models., Competing Interests: The authors declare no competing interests., (This journal is © The Royal Society of Chemistry.)
- Published
- 2022
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35. Decision-making in screening positive participants who follow up with colonoscopy in the Dutch colorectal cancer screening programme: A mixed-method study.
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Bertels L, Knottnerus B, Bastiaans L, Danquah A, van H Weert, Dekker E, and van K Asselt
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- Colonoscopy, Follow-Up Studies, Humans, Mass Screening methods, Occult Blood, Colorectal Neoplasms diagnosis, Colorectal Neoplasms psychology, Early Detection of Cancer psychology
- Abstract
Objective: To explore worry and decision-making processes used by faecal immunochemical test (FIT)-positive participants in the Dutch national screening programme for colorectal cancer., Methods: A mixed-methods study consisting of 22 semi-structured interviews in FIT-positive participants who underwent the recommended colonoscopy within 4-6 months after the FIT result, followed by a widespread questionnaire in a larger target population (N = 1495)., Results: In the interviews, we recognised two different decision-making processes. The first is an affective heuristic decision process where the decision to participate is made instantly and is paired with high-risk perception, worry and (severe) emotional turmoil. The second is a more time-consuming analytical decision process in which participants describe discussing options with others. In the questionnaire, high levels of cancer worry (CWS > 9) were reported by 34% of respondents. Decisional difficulties were reported by 15% of respondents, and 34% of respondents reported discussing the positive FIT result with their GP. Individuals with high levels of cancer worry contacted their GP less often than those with low levels., Conclusions: The Dutch two-step screening programme may result in high levels of cancer worry in a non-cancer population. More research is needed to monitor worry and its role in decision-making in cancer screening, as well as ways to facilitate decision-making for participants., (© 2021 The Authors. Psycho-Oncology published by John Wiley & Sons Ltd.)
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- 2022
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36. What can We Learn From High-Performing Screening Programs to Increase Bowel Cancer Screening Participation in Australia?
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Flander L, Dekker E, Andersen B, Larsen MB, Steele RJ, Malila N, Sarkeala T, van der Vlugt M, de Klerk C, Knottnerus B, Bertels L, Woudstra A, Spaander MCW, Fransen M, Heinavaara S, Dillon M, Ait Ouakrim D, and Jenkins M
- Subjects
- Australia, Female, Humans, Male, Mass Screening, Occult Blood, Colorectal Neoplasms diagnosis, Colorectal Neoplasms epidemiology, Early Detection of Cancer
- Abstract
Background: Colorectal cancer (CRC) is the second most diagnosed cancer in men and women and second most common cause of cancer death in Australia; Australia's CRC incidence and mortality are among the world's highest. The Australian National Bowel Cancer Screening Program began in 2006; however, only 33% of those approached for the first time by the Program between 2018 and 2019 returned the kit. Of the 5.7 million kits sent during this period, only 44% were returned. Our aim was to identify practices and features of national bowel cancer screening programs in countries with similar programs but higher screening participation, to identify potential interventions for optimising Australian CRC screening participation., Methods: We searched published and grey literature for CRC screening programs reporting at least 50% screening participation using postal invitation and free return of iFOBT home kits. Interviews were conducted with cancer registry staff and academic researchers, focused on participant and practitioner engagement in screening., Results: National programs in Netherlands, Scotland, Denmark, and Finland reported over 50% screening participation rates for all invitation rounds. Shared characteristics include small populations within small geographic areas relative to Australia; relatively high literacy; a one-sample iFOBT kit; national registration systems for population cancer screening research; and screening program research including randomised trials of program features., Conclusions: Apart from the one-sample kit, we identified no single solution to persistent Australian low uptake of screening. Research including randomised trials within the program promises to increase participation., Impact: This screening program comparison suggests that within-program intervention trials will lead to increased Australian screening participation.
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- 2022
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37. Motives for non-adherence to colonoscopy advice after a positive colorectal cancer screening test result: a qualitative study.
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Bertels L, Lucassen P, van Asselt K, Dekker E, van Weert H, and Knottnerus B
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- Aged, Early Detection of Cancer, Female, Humans, Male, Mass Screening, Occult Blood, Qualitative Research, Colonoscopy, Colorectal Neoplasms, Motivation, Patient Compliance
- Abstract
Setting: Participants with a positive faecal immunochemical test (FIT) in screening programs for colorectal cancer (CRC) have a high risk for colorectal cancer and advanced adenomas. They are therefore recommended follow-up by colonoscopy. However, more than ten percent of positively screened persons do not adhere to this advice., Objective: To investigate FIT-positive individuals' motives for non-adherence to colonoscopy advice in the Dutch CRC screening program., Subjects: Non-adherent FIT-positive participants of the Dutch CRC screening program., Design: We conducted semi structured in-depth interviews with 17 persons who did not undergo colonoscopy within 6 months after a positive FIT. Interviews were undertaken face-to-face and data were analysed thematically with open coding and constant comparison., Results: All participants had multifactorial motives for non-adherence. A preference for more personalised care was described with the following themes: aversion against the design of the screening program, expectations of personalised care, emotions associated with experiences of impersonal care and a desire for counselling where options other than colonoscopy could be discussed. Furthermore, intrinsic motives were: having a perception of low risk for CRC (described by all participants), aversion and fear of colonoscopy, distrust, reluctant attitude to the treatment of cancer and cancer fatalism. Extrinsic motives were: having other health issues or priorities, practical barriers, advice from a general practitioner (GP) and financial reasons., Conclusion: Personalised screening counselling might have helped to improve the interviewees' experiences with the screening program as well as their knowledge on CRC and CRC screening. Future studies should explore whether personalised screening counselling also has potential to increase adherence rates. Key points Participants with a positive FIT in two-step colorectal cancer (CRC) screening programs are at high risk for colorectal cancer and advanced adenomas. Non-adherence after an unfavourable screening result happens in all CRC programs worldwide with the consequence that many of the participants do not undergo colonoscopy for the definitive assessment of the presence of colorectal cancer. Little qualitative research has been done to study the reasons why individuals participate in the first step of the screening but not in the second step. We found a preference for more personalised care, which was not reported in previous literature on this subject. Furthermore, intrinsic factors, such as a low risk perception and distrust, and extrinsic factors, such as the presence of other health issues and GP advice, may also play a role in non-adherence. A person-centred approach in the form of a screening counselling session may be beneficial for this group of CRC screening participants.
- Published
- 2020
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38. Gas phase formation of c-SiC 3 molecules in the circumstellar envelope of carbon stars.
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Yang T, Bertels L, Dangi BB, Li X, Head-Gordon M, and Kaiser RI
- Abstract
Complex organosilicon molecules are ubiquitous in the circumstellar envelope of the asymptotic giant branch (AGB) star IRC+10216, but their formation mechanisms have remained largely elusive until now. These processes are of fundamental importance in initiating a chain of chemical reactions leading eventually to the formation of organosilicon molecules-among them key precursors to silicon carbide grains-in the circumstellar shell contributing critically to the galactic carbon and silicon budgets with up to 80% of the ejected materials infused into the interstellar medium. Here we demonstrate via a combined experimental, computational, and modeling study that distinct chemistries in the inner and outer envelope of a carbon star can lead to the synthesis of circumstellar silicon tricarbide (c-SiC
3 ) as observed in the circumstellar envelope of IRC+10216. Bimolecular reactions of electronically excited silicon atoms (Si(1 D)) with allene (H2 CCCH2 ) and methylacetylene (CH3 CCH) initiate the formation of SiC3 H2 molecules in the inner envelope. Driven by the stellar wind to the outer envelope, subsequent photodissociation of the SiC3 H2 parent operates the synthesis of the c-SiC3 daughter species via dehydrogenation. The facile route to silicon tricarbide via a single neutral-neutral reaction to a hydrogenated parent molecule followed by photochemical processing of this transient to a bare silicon-carbon molecule presents evidence for a shift in currently accepted views of the circumstellar organosilicon chemistry, and provides an explanation for the previously elusive origin of circumstellar organosilicon molecules that can be synthesized in carbon-rich, circumstellar environments., Competing Interests: The authors declare no conflict of interest.- Published
- 2019
- Full Text
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39. GPs' perspectives on colorectal cancer screening and their potential influence on FIT-positive patients: an exploratory qualitative study from a Dutch context.
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Bertels L, van der Heijden S, Hoogsteyns M, Dekker E, van Asselt K, van Weert H, and Knottnerus B
- Abstract
Background: In the Dutch colorectal cancer (CRC) screening programme, individuals receive a faecal immunochemical test (FIT) to do at home. After a positive FIT result, a follow-up colonoscopy is recommended to identify CRC or advanced adenomas (AA). GPs may influence their patients' decisions on adherence to follow-up by colonoscopy., Aim: To explore GPs' perspectives on the CRC screening programme and their potential influence on FIT-positive patients to follow up with the recommended colonoscopy., Design & Setting: Semi-structured interviews among GPs in Amsterdam, the Netherlands., Method: GPs were approached using purposive sampling. Analysis was performed on 11 interviews using open coding and constant comparison., Results: All interviewed GPs would recommend FIT-positive patients without obvious contraindications to adhere to a follow-up colonoscopy. If patients were likely to be distressed by a positive FIT result, most GPs described using reassurance strategies emphasising a low cancer probability. Most GPs stressed the probability of false-positive FIT results. Some described taking a positive screening result in CRC screening less seriously than one in breast cancer screening. Most GPs underestimated CRC and AA probabilities after a positive FIT result. When told the actual probabilities, some stated that this knowledge might change the way they would inform patients., Conclusion: These results imply that some of the interviewed GPs have too low a perception of the risk associated with a positive FIT result, which might influence their patients' decision-making. Simply informing GPs about the actual rates of CRC and AA found in the screening programme might improve this risk perception.
- Published
- 2019
- Full Text
- View/download PDF
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