116 results on '"Palagi L"'
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2. A Mathematical Programming Approach for the Solution of the Railway Yield Management Problem
- Author
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CIANCIMINO, A., INZERILLO, G., LUCIDI, S., and PALAGI, L.
- Published
- 1999
3. Decomposition Algorithm Model for Singly Linearly-Constrained Problems Subject to Lower and Upper Bounds
- Author
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Lin, C. J., Lucidi, S., Palagi, L., Risi, A., and Sciandrone, M.
- Published
- 2009
- Full Text
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4. Elective cancer surgery in COVID-19–Free surgical pathways during the SARS-cov-2 pandemic: An international, multicenter, comparative cohort study
- Author
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James C Glasbey, Dmitri Nepogodiev, Joana Ff Simoes, Omar Omar, Elizabeth Li, Mary L Venn, Mohammad Abou Chaar, Vita Capizzi, Daoud Chaudhry, Anant Desai, Jonathan G Edwards, Jonathan P Evans, Marco Fiore, Jose Flavio Videria, Samuel J Ford, Ian Ganyli, Ewen A Griffiths, Rohan R Gujjuri, Angelos G Kolias, Haytham Ma Kaafarani, Ana Minaya-Bravo, Siobhan C McKay, Helen M Mohan, Keith Roberts, Carlos San Miguel-Méndez, Peter Pockney, Richard Shaw, Neil J Smart, Grant D Stewart, Sudha Sundar, Raghavan Vidya, Aneel A Bhangu, James C Glasbey, Omar Omar, Aneel A Bhangu, Kwabena Siaw-Acheampong, Ruth A Benson, Edward Bywater, Daoud Chaudhry, Brett E Dawson, Jonathan P Evans, James C Glasbey, Rohan R Gujjuri, Emily Heritage, Conor S Jones, Sivesh K Kamarajah, Chetan Khatri, Rachel A Khaw, James M Keatley, Andrew Knight, Samuel Lawday, Elizabeth Li, Harvinder S Mann, Ella J Marson, Kenneth A McLean, Siobhan C McKay, Emily C Mills, Dmitri Nepogodiev, Gianluca Pellino, Maria Picciochi, Elliott H Taylor, Abhinav Tiwari, Joana Ff Simoes, Isobel M Trout, Mary L Venn, Richard Jw Wilkin, Aneel A Bhangu, James C Glasbey, Neil J Smart, Ana Minaya-Bravo, Jonathan P Evans, Gaetano Gallo, Susan Moug, Francesco Pata, Peter Pockney, Salomone Di Saverio, Abigail Vallance, Dale Vimalchandran, Ewen A Griffiths, Sivesh K Kamarajah, Richard Pt Evans, Philip Townend, Keith Roberts, Siobhan McKay, John Isaac, Sohei Satoi, John Edwards, Aman S Coonar, Adrian Marchbank, Edward J Caruana, Georgia R Layton, Akshay Patel, Alessandro Brunelli, Samuel Ford, Anant Desai, Alessandro Gronchi, Marco Fiore, Max Almond, Fabio Tirotta, Sinziana Dumitra, Angelos Kolias, Stephen J Price, Daniel M Fountain, Michael D Jenkinson, Peter Hutchinson, Hani J Marcus, Rory J Piper, Laura Lippa, Franco Servadei, Ignatius Esene, Christian Freyschlag, Iuri Neville, Gail Rosseau, Karl Schaller, Andreas K Demetriades, Faith Robertson, Alex Alamri, Richard Shaw, Andrew G Schache, Stuart C Winter, Michael Ho, Paul Nankivell, Juan Rey Biel, Martin Batstone, Ian Ganly, Raghavan Vidya, Alex Wilkins, Jagdeep K Singh, Dinesh Thekinkattil, Sudha Sundar, Christina Fotopoulou, Elaine Leung, Tabassum Khan, Luis Chiva, Jalid Sehouli, Anna Fagotti, Paul Cohen, Murat Gutelkin, Rahel Ghebre, Thomas Konney, Rene Pareja, Rob Bristow, Sean Dowdy, T S Shylasree, R Kottayasamy Seenivasagam, Joe Ng, Keiiji Fujiwara, Grant D Stewart, Benjamin Lamb, Krishna Narahari, Alan McNeill, Alexandra Colquhoun, John McGrath, Steve Bromage, Ravi Barod, Veeru Kasivisvanathan, Tobias Klatte, Joana Ff Simoes, Tom Ef Abbott, Sadi Abukhalaf, Michel Adamina, Adesoji O Ademuyiwa, Arnav Agarwal, Murat Akkulak, Ehab Alameer, Derek Alderson, Felix Alakaloko, Markus Albertsmeiers, Osaid Alser, Muhammad Alshaar, Sattar Alshryda, Alexis P Arnaud, Knut Magne Augestad, Faris Ayasra, José Azevedo, Brittany K Bankhead-Kendall, Emma Barlow, David Beard, Ruth A Benson, Ruth Blanco-Colino, Amanpreet Brar, Ana Minaya-Bravo, Kerry A Breen, Chris Bretherton, Igor Lima Buarque, Joshua Burke, Edward J Caruana, Mohammad Chaar, Sohini Chakrabortee, Peter Christensen, Daniel Cox, Moises Cukier, Miguel F Cunha, Giana H Davidson, Anant Desai, Salomone Di Saverio, Thomas M Drake, John G Edwards, Muhammed Elhadi, Sameh Emile, Shebani Farik, Marco Fiore, J Edward Fitzgerald, Samuel Ford, Tatiana Garmanova, Gaetano Gallo, Dhruv Ghosh, Gustavo Mendonça Ataíde Gomes, Gustavo Grecinos, Ewen A Griffiths, Madalegna GrÜndl, Constantine Halkias, Ewen M Harrison, Intisar Hisham, Peter J Hutchinson, Shelley Hwang, Arda Isik, Michael D Jenkinson, Pascal Jonker, Haytham Ma Kaafarani, Debby Keller, Angelos Kolias, Schelto Kruijff, Ismail Lawani, Hans Lederhuber, Sezai Leventoglu, Andrey Litvin, Andrew Loehrer, Markus W Löffler, Maria Aguilera Lorena, Maria Marta Modolo, Piotr Major, Janet Martin, Hassan N Mashbari, Dennis Mazingi, Symeon Metallidis, Ana Minaya-Bravo, Helen M Mohan, Rachel Moore, David Moszkowicz, Susan Moug, Joshua S Ng-Kamstra, Mayaba Maimbo, Ionut Negoi, Milagros Niquen, Faustin Ntirenganya, Maricarmen Olivos, Kacimi Oussama, Oumaima Outani, Marie Dione Parreno-Sacdalanm, Francesco Pata, Carlos Jose Perez Rivera, Thomas D Pinkney, Willemijn van der Plas, Peter Pockney, Ahmad Qureshi, Dejan Radenkovic, Antonio Ramos-De la Medina, Keith Roberts, April C Roslani, Martin Rutegård, Juan José Segura-Sampedro, Irène Santos, Sohei Satoi, Raza Sayyed, Andrew Schache, Andreas A Schnitzbauer, Justina O Seyi-Olajide, Neil Sharma, Richard Shaw, Sebastian Shu, Kjetil Soreide, Antonino Spinelli, Grant D Stewart, Malin Sund, Sudha Sundar, Stephen Tabiri, Philip Townend, Georgios Tsoulfas, Gabrielle H van Ramshorst, Raghavan Vidya, Dale Vimalachandran, Oliver J Warren, Duane Wedderburn, Naomi Wright, C Allemand, L Boccalatte, M Figari, M Lamm, J Larrañaga, C Marchitelli, F Noll, D Odetto, M Perrotta, J Saadi, L Zamora, C Alurralde, E L Caram, D Eskinazi, J P Mendoza, M Usandivaras, R Badra, A Esteban, J S García, P M García, J I Gerchunoff, S M Lucchini, M A NIgra, L Vargas, T Hovhannisyan, A Stepanyan, T Gould, R Gourlay, B Griffiths, S Gananadha, M McLaren, J Cecire, N Joshi, S Salindera, A Sutherland, J H Ahn, G Charlton, S Chen, N Gauri, R Hayhurst, S Jang, F Jia, C Mulligan, W Yang, G Ye, H Zhang, M Ballal, D Gibson, D Hayne, J Moss, T Richards, P Viswambaram, U G Vo, J Bennetts, T Bright, M Brooke-Smith, R Fong, B Gricks, Y H Lam, B S Ong, M Szpytma, D Watson, K Bagraith, S Caird, E Chan, C Dawson, D Ho, E Jeyarajan, S Jordan, A Lim, G J Nolan, A Oar, D Parker, H Puhalla, A Quennell, L Rutherford, P Townend, M Von Papen, M Wullschleger, A Blatt, D Cope, N Egoroff, M Fenton, J Gani, N Lott, P Pockney, N Shugg, M Elliott, D Phung, D Phan, D Townend, C Bong, J Gundara, A Frankel, S Bowman, G R Guerra, J Bolt, K Buddingh, N N Dudi-Venkata, S Jog, H M Kroon, T Sammour, R Smith, C Stranz, M Batstone, K Lah, W McGahan, D Mitchell, A Morton, A Pearce, M Roberts, G Sheahan, B Swinson, N Alam, S Banting, L Chong, P Choong, S Clatworthy, D Foley, A Fox, M W Hii, B Knowles, J Mack, M Read, A Rowcroft, S Ward, G Wright, M Lanner, I Königsrainer, M Bauer, C Freyschlag, M Kafka, F Messner, D Öfner, I Tsibulak, K Emmanuel, M Grechenig, R Gruber, M Harald, L Öhlberger, J Presl, A Wimmer, I Namazov, E Samadov, D Barker, R Boyce, S Corbin, A Doyle, A Eastmond, R Gill, A Haynes, S Millar, M O'Shea, G Padmore, N Paquette, E Phillips, S St John, K Walkes, N Flamey, P Pattyn, W Oosterlinck, J Van den Eynde, R Van den Eynde, A Gatti, C Nardi, R Oliva, R De Cicco, I Cecconello, P Gregorio, L Pontual Lima, U Ribeiro Junior, F Takeda, R M Terra, M Sokolov, B Kidane, S Srinathan, M Boutros, N Caminsky, G Ghitulescu, G Jamjoum, J Moon, J Pelletier, T Vanounou, S Wong, M Boutros, S Dumitra, A Kouyoumdjian, B Johnston, C Russell, M Boutros, S Demyttenaere, R Garfinkle, J Abou-Khalil, C Nessim, J Stevenson, F Heredia, A Almeciga, A Fletcher, A Merchan, L O Puentes, J Mendoza Quevedo, G Bacic, D Karlovic, D Krsul, M Zelic, I Luksic, M Mamic, B Bakmaz, I Coza, E Dijan, Z Katusic, J Mihanovic, I Rakvin, K Frantzeskou, N Gouvas, G Kokkinos, P Papatheodorou, I Pozotou, O Stavrinidou, A Yiallourou, L Martinek, M Skrovina, I Szubota, J Žatecký, V Javurkova, J Klat, T Avlund, P Christensen, J L Harbjerg, L H Iversen, D W Kjaer, Hø Kristensen, M Mekhael, A L Ebbehøj, P Krarup, N Schlesinger, H Smith, A Abdelsamed, A Y Azzam, H Salem, A Seleim, A Abdelmajeed, M Abdou, N E Abosamak, M Al Sayed, F Ashoush, R Atta, E Elazzazy, M Elhoseiny, M Elnemr, M S Elqasabi, M E Elsayed Hewalla, I Elsherbini, E Essam, M Eweda, I Ghallab, E Hassan, M Ibrahim, M Metwalli, M Mourad, M S Qatora, M Ragab, A Sabry, H Saifeldin, M Saleh Mesbah Mohamed Elkaffas, A Samih, A Samir Abdelaal, S Shehata, K Shenit, D Attia, N Kamal, N Osman, A M Abbas, Has Abd Elazeem, M M Abdelkarem, S Alaa, A K Ali, A Ayman, M G Azizeldine, H Elkhayat, S M Elghazaly, F A Monib, M A Nageh, M M Saad, M Salah, M Shahine, E A Yousof, A Youssef, A Eldaly, M ElFiky, A Nabil, G Amira, I Sallam, M Sherief, A Sherif, A Abdelrahman, H Aboulkassem, G Ghaly, R Hamdy, A Morsi, H Salem, G Sherif, H Abdeldayem, I Abdelkader Salama, M Balabel, Y Fayed, A E Sherif, D Bekele, J Kauppila, E Sarjanoja, O Helminen, H Huhta, J H Kauppila, C Beyrne, L Jouffret, L Lugans, L Marie-Macron, E Chouillard, B De Simone, J Bettoni, S Dakpé, B Devauchelle, N Lavagen, S Testelin, S Boucher, R Breheret, A Gueutier, A Kahn, J KÜn-Darbois, A Barrabe, Z Lakkis, A Louvrier, S Manfredelli, P Mathieu, A Chebaro, V Drubay, M El Amrani, C Eveno, K Lecolle, G Legault, L Martin, G Piessen, F R Pruvot, S Truant, P Zerbib, Q Ballouhey, B Barrat, J Laloze, H Salle, A Taibi, J Usseglio, D Bergeat, A Merdrignac, Roy B Le, L O Perotto, A Scalabre, A Aimé, A Ezanno, B Malgras, P Bouche, S Tzedakis, E Cotte, O Glehen, V Kepenekian, J Lifante, G Passot, A D'Urso, E Felli, D Mutter, P Pessaux, B Seeliger, J Bardet, R Berry, G Boddaert, S Bonnet, E Brian, C Denet, D Fuks, D Gossot, M Grigoroiu, A Laforest, Y Levy-Zauberman, C Louis-Sylvestre, A Moumen, G Pourcher, A Seguin-Givelet, E Tribillon, E Duchalais, F Espitalier, C Ferron, O Malard, U Bork, M Distler, J Fritzmann, J Kirchberg, C Praetorius, C Riediger, J Weitz, T Welsch, P Wimberger, K Beyer, C Kamphues, J Lauscher, F N Loch, C Schineis, M Albertsmeier, M Angele, A Kappenberger, H Niess, T Schiergens, J Werner, R Becker, J Jonescheit, I Pergolini, D Reim, C Boeker, I Hakami, J Mall, P Liokatis, W Smolka, K Nowak, T Reinhard, F Hölzle, A Modabber, P Winnand, M Knitschke, P Kauffmann, S Wolfer, J Kleeff, K Lorenz, C Michalski, U Ronellenfitsch, R Schneider, E Bertolani, A Königsrainer, M W Löffler, M Quante, C Steidle, L ÜberrÜck, C Yurttas, C S Betz, J Bewarder, A Böttcher, S Burg, C Busch, M Gosau, A Heuer, J Izbicki, T O Klatte, D Koenig, N Moeckelmann, C Nitschke, M Priemel, R Smeets, U Speth, S Thole, F G Uzunoglu, T Vollkommer, N Zeller, M J Battista, K Gillen, A Hasenburg, S Krajnak, V Linz, R Schwab, K Angelou, D Haidopoulos, A Rodolakis, P Antonakis, K Bramis, L Chardalias, I Contis, N Dafnios, D Dellaportas, G Fragulidis, A Gklavas, M Konstadoulakis, N Memos, I Papaconstantinou, A Polydorou, T Theodosopoulos, A Vezakis, M I Antonopoulou, D K Manatakis, N Tasis, N Arkadopoulos, N Danias, P Economopoulou, P Kokoropoulos, A Larentzakis, N Michalopoulos, J Selmani, T Sidiropoulos, V Tsaousis, P Vassiliu, K Bouchagier, S Klimopoulos, D Paspaliari, G Stylianidis, K Baxevanidou, K Bouliaris, P Chatzikomnitsa, M Efthimiou, A Giaglaras, C Kalfountzos, G Koukoulis, A M Ntziovara, K Petropoulos, K Soulikia, I Tsiamalou, K Zervas, S Zourntou, I Baloyiannis, A Diamantis, E Gkrinia, J Hajiioannou, C Korais, O Koukoura, K Perivoliotis, A Saratziotis, C Skoulakis, D Symeonidis, K Tepetes, G Tzovaras, D Zacharoulis, V Alexoudi, K Antoniades, I Astreidis, P Christidis, D Deligiannidis, T Grivas, O Ioannidis, I Kalaitsidou, L Loutzidou, A Mantevas, D Michailidou, K Paraskevopoulos, S Politis, A Stavroglou, D Tatsis, I Tilaveridis, K Vahtsevanos, G Venetis, I Karaitianos, T Tsirlis, A Charalabopoulos, T Liakakos, E Mpaili, D Schizas, E Spartalis, A Syllaios, C Zografos, C Anthoulakis, C Christou, V Papadopoulos, A Tooulias, D Tsolakidis, G Tsoulfas, D Zouzoulas, E Athanasakis, E Chrysos, J Tsiaoussis, S Xenaki, E Xynos, K Futaba, M F Ho, S F Hon, Twc Mak, Ssm Ng, C C Foo, B Banky, N Suszták, M Aremu, A Canas-Martinez, O Cullivan, C Murphy, P Owens, L Pickett, L Akmenkalne, J Byrne, M Corrigan, C Cullinane, A Daly, C Fleming, P Jordan, S Killeen, N Lynch, A McCarthy, H Mustafa, S O'Brien, P O'Leary, Was Syed, L Vernon, D Callanan, L Huang, A Ionescu, P Sheahan, I Balasubramanian, M Boland, K Conlon, D Evoy, N Fearon, T Gallagher, J Geraghty, H Heneghan, N Kennedy, D Maguire, D McCartan, E W McDermott, R S Prichard, D Winter, D Alazawi, C Barry, T Boyle, W Butt, E M Connolly, N Donlon, C Donohue, B A Fahey, R Farrell, C Fitzgerald, J Kinsella, J O Larkin, P Lennon, P J Maguire, P Mccormick, B J Mehigan, H Mohan, T Nugent, H O'Sullivan, N Ravi, J V Reynolds, A Rogers, P Shokuhi, J Smith, L A Smith, C Timon, Y Bashir, G Bass, T Connelly, B Creavin, H Earley, J A Elliott, A Gillis, D Kavanagh, P Neary, J O'riordan, I S Reynolds, D Rice, P Ridgway, M Umair, M Whelan, P Carroll, C Collins, K Corless, L Finnegan, A Fowler, A Hogan, M Kerin, A Lowery, P McAnena, K McKevitt, K Nizami, É Ryan, A Samy, J C Coffey, R Cunningham, M Devine, D Nally, C Peirce, S Tormey, N Hardy, P Neary, S O'Malley, M Ryan, S Macina, N M Mariani, E Opocher, A Pisani Ceretti, F Ferrari, F Odicino, E Sartori, C Cotsoglou, S Granieri, F Bianco, A Camillo, M Colledan, S Tornese, M F Zambelli, G Bissolotti, S Fusetti, F Lemma, M V Marino, A Mirabella, G Vaccarella, C Agostini, G Alemanno, I Bartolini, C Bergamini, A Bruscino, C Checcucci, R De Vincenti, A Di Bella, M Fambrini, L Fortuna, G Maltinti, P Muiesan, F Petraglia, P Prosperi, M N Ringressi, M Risaliti, F Sorbi, A Taddei, R Tucci, C Bassi, L Bortolasi, T Campagnaro, L Casetti, M De Pastena, A Esposito, M Fontana, A Guglielmi, L Landoni, G Malleo, G Marchegiani, S Nobile, S Paiella, C Pedrazzani, S Rattizzato, A Ruzzenente, R Salvia, G Turri, M Tuveri, P Bellora, G D'Aloisio, M Ferrari, E Francone, S Gentilli, H Nikaj, M Bianchini, M Chiarugi, F Coccolini, G Di Franco, N Furbetta, D Gianardi, S Guadagni, L Morelli, M Palmeri, D Tartaglia, G Anania, P Carcoforo, M Chiozza, A De Troia, M Koleva Radica, M Portinari, M G Sibilla, A Urbani, N Fabbri, C V Feo, S Gennari, S Parini, E Righini, L Ampollini, L Bellanti, M Bergonzani, G Bertoli, G Bocchialini, G D'Angelo, D Lanfranco, L Musini, T Poli, G P Santoro, A Varazzani, L Aguzzoli, G Borgonovo, C Castro Ruiz, S Coiro, G Falco, V D Mandato, V Mastrofilippo, M T Montella, V Annessi, M Zizzo, U Grossi, S Novello, M Romano, S Rossi, G Zanus, G Esposito, F Frongia, A Pisanu, M Podda, C Belluco, A Lauretta, G Montori, L Moras, M Olivieri, F Bussu, A G Carta, M L Cossu, P Cottu, A Fancellu, C F Feo, G C Ginesu, G Giuliani, M Madonia, T Perra, A Piras, A Porcu, D Rizzo, A M Scanu, A Tedde, M Tedde, P Delrio, D Rega, G Badalamenti, G Campisi, A Cordova, M Franza, G Maniaci, G Rinaldi, F Toia, M Calabrò, F Farnesi, E G Lunghi, A Muratore, N S Pipitone Federico, F Bàmbina, G D'Andrea, P Familiari, V Picotti, G De Palma, G Luglio, G Pagano, F P Tropeano, L Baldari, G A Beltramini, L Boni, E Cassinotti, A Gianni, L Pignataro, S Torretta, C Abatini, M Baia, D Biasoni, G Bogani, P Cadenelli, V Capizzi, Spb Cioffi, D Citterio, L V Comini, M Cosimelli, M Fiore, S Folli, M Gennaro, L Giannini, A Gronchi, M Guaglio, A Macchi, F Martinelli, V Mazzaferro, A Mosca, S Pasquali, C Piazza, F Raspagliesi, L Rolli, R Salvioni, G Sarpietro, C Sarre, L Sorrentino, A Agnes, S Alfieri, F Belia, A Biondi, V Cozza, A D'Amore, D D'Ugo, V De Simone, A Fagotti, G Gasparini, L Gordini, F Litta, C P Lombardi, L Lorenzon, A A Marra, F Marzi, A Moro, A Parello, E Perrone, R Persiani, C Ratto, F Rosa, G Saponaro, G Scambia, O Scrima, G Sganga, R Tudisco, A Belli, V Granata, F Izzo, R Palaia, R Patrone, F M Carrano, M M Carvello, A De Virgilio, F Di Candido, F Ferreli, F Gaino, G Mercante, V Rossi, A Spinelli, G Spriano, D M Donati, T Frisoni, E Palmerini, A Aprile, F Barra, P Batistotti, S Ferrero, P Fregatti, S Scabini, M Sparavigna, E Asti, D Bernardi, L Bonavina, A Lovece, L Adamoli, M Ansarin, S Cenciarelli, F Chu, R De Berardinis, U Fumagalli Romario, F Mastrilli, G Pietrobon, M Tagliabue, E Badellino, A Ferrero, R Massobrio, A De Manzoni Garberini, P Federico, P Maida, E Marra, G Marte, A Petrillo, T Tammaro, A Tufo, M Berselli, G Borroni, E Cocozza, L Conti, M Desio, L Livraghi, V Quintodei, A Rizzi, A Zullo, C Baldi, C Corbellini, G M Sampietro, P Cellerino, E Baldini, P Capelli, L Conti, S M Isolani, M Ribolla, A Bondurri, F Colombo, L Ferrario, C Guerci, A Maffioli, T Armao, M Ballabio, P Bisagni, A Gagliano, M Longhi, M Madonini, P PizziCni, A M Baietti, M Biasini, P Maremonti, F Neri, G M Prucher, S Ricci, F Ruggiero, A G Zarabini, R Barmasse, S Mochet, L Morelli, A Usai, F Bianco, P Incollingo, S Mancini, L Marino Cosentino, A Sagnotta, R Fruscio, T Grassi, L C Nespoli, N Tamini, A Anastasi, B Bartalucci, A Bellacci, G Canonico, L Capezzuoli, C Di Martino, P Ipponi, C Linari, M Montelatici, T Nelli, G Spagni, L Tirloni, A Vitali, E Abate, M Casati, T Casiraghi, L Laface, M Schiavo, A Arminio, A Cotoia, V Lizzi, F Vovola, R Vergari, S D'Ugo, N Depalma, M G Spampinato, P Bartolucci, G Brachini, P Bruzzaniti, A Chiappini, V Chiarella, F Ciccarone, P M Cicerchia, B Cirillo, G De Toma, A Di Bartolomeo, E Fiori, G B Fonsi, G Franco, A Frati, M Giugliano, I Iannone, F La Torre, P Lapolla, C Leonardo, G Marruzzo, S Meneghini, A Mingoli, D Ribuffo, M Salvati, A Santoro, P Sapienza, A K Scafa, L Simonelli, M Zambon, G T Capolupo, F Carannante, M Caricato, G Mascianà, E Mazzotta, A Gattolin, M Migliore, R Rimonda, D Sasia, E Travaglio, M Cervellera, A Gori, L Sartarelli, V Tonini, M Giacometti, S Zonta, A Chessa, A Fiorini, C Norcini, G Colletti, M Confalonieri, A Costanzi, C Frattaruolo, G Mari, M Monteleone, A Bandiera, L Bocciolone, G Bonavina, M Candiani, G Candotti, P De Nardi, F Gagliardi, M Medone, P Mortini, G Negri, P Parise, M Piloni, P Sileri, A Vignali, A Belvedere, P Bernante, P Bertoglio, S Boussedra, E Brunocilla, R Cipriani, G Cisternino, E De Crescenzo, P De Iaco, G Dondi, F Frio, E Jovine, F Mineo Bianchi, J Neri, D Parlanti, A M Perrone, A P Pezzuto, M Pignatti, V Pinto, G Poggioli, M Ravaioli, M Rottoli, R Schiavina, M Serenari, M Serra, P Solli, M Taffurelli, M Tanzanu, M Tesei, T Violante, S Zanotti, F Borghi, D Cianflocca, S Di Maria Grimaldi, D Donati, E Gelarda, P Geretto, G Giraudo, M C Giuffrida, A Marano, S Palagi, L Pellegrino, C Peluso, V Testa, F Agresta, D Prando, M Zese, F Aquila, C Gambacciani, L Lippa, F Pieri, O S Santonocito, G Armatura, G Bertelli, A Frena, P Marinello, F Notte, S Patauner, G Scotton, S F Fulginiti, G Gallo, G Sammarco, G Vescio, P Balercia, L Catarzi, G Consorti, F Di Marzo, T Fontana, H Daiko, M Ishikawa, K Ishiyama, S Iwata, K Kanematsu, Y Kanemitsu, T Kato, A Kawai, E Kobayashi, M Kobayashi Kato, K Moritani, F Nakatani, J Oguma, Y Tanase, M Uno, M Al Abdallah, F Ayasra, Y Ayasra, A Qasem, F J Abu Za'nouneh, T Fahmawee, A Hmedat, A Ibrahim, K Obeidat, S Abdel Al, R Abdel Jalil, M K Abou Chaar, M Al-Masri, H Al-Najjar, F Alawneh, O Alsaraireh, M Elayyan, R Ghanem, I Lataifeh, G Z Alkadeeki, F S Al Maadany, N Aldokali, O Senossi, M T Subhi, D Burgan, E Kamoka, A I Kilani, A Salamah, M Salem, A Shuwayyah, E Abdulwahed, E Alshareea, N Aribi, S Aribi, M Biala, R Ghamgh, M Morgom, Z Aldayri, I Ellojli, A Kredan, S Bradulskis, E Dainius, E Kubiliute, J Kutkevicius, A Parseliunas, A Subocius, D Venskutonis, F Rasoaherinomenjanahary, J B Razafindrahita, L H Samison, E C Ong, K H Hamdan, M R Ibrahim, J A Tan, M R Thanapal, N Amin Sahid, F Hayati, J 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Walker, S Waseem, S Yordanov, T Jones, A Kattakayam, C Loh, R Lunevicius, S Pringle, A Schache, R Shaw, A Sheel, C Rossborough, D Angelou, M Choynowski, B McAree, A McCanny, D Neely, G Tutoveanu, S Ahad, Mfi De La Cruz Monroy, F Mosley, V Oktseloglou, A Alanbuki, M Patel, A Shabana, E Perera, D Raveendran, K Ravi-Shankar, J Thiruchelvam, L Arrowsmith, W Campbell, T Grove, C Kontovounisios, O Warren, P Rolland, A Aggarwal, S Brown, C Jelley, N Neal, R Clifford, N Eardley, E Krishnan, N Manu, E Martin, S Roy Mahapatra, O L Serevina, C Smith, D Vimalachandran, M Bordenave, R Houston, G Putnam, A Robson, H Tustin, K Emslie, P L Labib, A Marchbank, D Miller, G Minto, J Natale, H Nwinee, P Panahi, L Rogers, A Abubakar, M M Akhter Rahman, E Chan, Kyk Ko, H O'Brien, K Sasapu, H Woodun, R Inglis, H J Ng, A De Gea Rico, N Ghazali, J Lambert, G Markose, S Math, I Sarantitis, D Shrestha, A Sultana, M Taggarsi, S Timbrell, O P Vaz, L Vitone, A Day, H Dent, M Fahim, S Waheed, A Hunt, N Laskar, A Gupta, J Steinke, S Thrumurthy, E Massie, K McGivern, D Rutherford, M Wilson, J Hardie, S Kazzaz, S Handa, M Kaushal, A Kler, P Patel, J Redfern, S Tezas, Y Aawsaj, S Amonkar, C Barry, L Blackwell, D Blake, J Carter, H Emerson, A Fisher, M Katory, P Korompelis, W McCormick, A Mustafa, L Pearce, N Ratnavelu, R Reehal, L Kretzmer, L Lalou, B Manku, I Parwaiz, J Stafford, M Abdelkarim, A Asqalan, T Gala, S Ibrahim, A Maw, R Mithany, R Morgan, G Sundaram Venkatesan, K Ang, E J Caruana, M F Chowdhry, A Mohammad, A Nakas, S Rathinam, M Boal, O Brown, S Dwerryhouse, S Higgs, A Vallance, E Boyd, V Irvine, A Kirk, G Bakolas, A Boulton, A Chandock, T Khan, M Kumar, P Agoston, A Bille, B Challacombe, S Fraser, K Harrison-Phipps, J King, G Mehra, L Mills, M Najdy, R Nath, L Okiror, J Pilling, V Rizzo, T Routledge, A Sayasneh, L Stroman, A Wali, M Fehervari, C Fotopoulou, N Habib, S Hamrang-Yousefi, Z Jawad, L Jiao, M Pai, J Ploski, P Rajagopal, S Saso, M Sodergren, D Spalding, S Laws, C Hardie, C McNaught, R Alam, A Budacan, J Cahill, M Kalkat, S Karandikar, L Kenyon, D Naumann, A Patel, J Ayorinde, T Chase, T Cuming, A Ghanbari, L Humphreys, S Tayeh, A Aboelkassem Ibrahim, R Bichoo, H Cao, Akw Chai, J Choudhury, C Evans, H Fitzjohn, H Ikram, M Langstroth, M Loubani, A McMillan, S Nazir, Ssa Qadri, A Robinson, E Ross, T Sehgal, A Wilkins, J Dixon, J Dunning, K Freystaetter, M Jha, S Lester, A Madhavan, S V Thulasiraman, Y Viswanath, T Curl-Roper, C Delimpalta, Ccl Liao, V Velchuru, E Westwood, E Belcher, G Bond-Smith, S Chidambaram, F Di Chiara, K Fasanmade, L Fraser, H Fu, M Ganau, S Gore, J Graystone, D Jeyaretna, H Khatkar, M Lami, M Maher, S Mastoridis, R Mihai, R Piper, S Prabhu, Obf Risk, U Selbong, K Shah, R Smillie, H Soleymani Majd, S Sravanam, D Stavroulias, G D Tebala, M Vatish, C Verberne, K Wallwork, S Winter, M I Bhatti, H Boyd-Carson, E Elsey, E Gemmill, P Herrod, M Jibreel, E Lenzi, T Saafan, D Sapre, T Sian, N Watson, A Athanasiou, G Bourke, L Bradshaw, A Brunelli, J Burke, P Coe, F Costigan, H Elkadi, M Ho, J Johnstone, A Kanatas, V Kantola, A Kaufmann, A Laios, S Lam, E MacInnes, S Munot, C Nahm, M Otify, C Pompili, I Smith, G Theophilou, G Toogood, R Wade, D Ward, C West, S Annamalai, C Ashmore, A Boddy, T Hossain, A Kourdouli, A Gvaramadze, A Jibril, L Prusty, D Thekkinkattil, A Harky, M Shackcloth, A Askari, C Chan, N Cirocchi, S Kudchadkar, K Patel, J Sagar, S Shaw, R Talwar, M Abdalla, R Edmondson, O Ismail, D Jones, K Newton, N Stylianides, A Aderombi, U Andaleeb, O Bajomo, K Beatson, W Garrett, M Mehmood, V Ng, R Al-Habsi, G S Divya, B Keeler, B Al-Sarireh, R Egan, R Harries, A Henry, M Kittur, Z Li, K Parkins, F Soliman, N Spencer, D Thompson, C Burgess, C Gemmell, C Grieco, M Hollyman, L Hunt, J Morrison, S Ojha, N Ryan, F Abbadessa, S Barnard, C Chan, N Dawe, J Hammond, Ali F Mahmoud, I McPherson, C Mellor, J Moir, S Pandanaboyana, J Powell, B Rai, A Rogers, C Roy, A Sachdeva, C Saleh, S Tingle, T Williams, J Manickavasagam, C McDonald, N McGrath, N McSorley, K Ragupathy, L Ramsay, A Solth, O Kakisi, K Seebah, I Shaikh, L Sreedharan, M Youssef, J Shah, P Ameerally, N McLarty, S Mills, A Shenfine, K Sahnan, J Abu, E Addae-Boateng, D Bratt, L Brock, N Burnside, S Cadwell-Sneath, K Gajjar, C Gan, C Grundy, K Hallam, K Hassell, M Hawari, A Joshi, H Khout, K Konstantinidi, Rxn Lee, D Nunns, R Schiemer, T Walton, H Weaver, L Whisker, K Williamson, J McVeigh, R Myatt, M A Williams, R Kaur, E Leung, S Sundar, M Michel, S Patil, S Ravindran, J Sarveswaran, L Scott, M Edmond, E King, M Almond, A Bhangu, O Breik, L D Cato, A Desai, S Ford, E Griffiths, M Idle, M Kamal, A Kisiel, R Kulkarni, Jkc Mak, T Martin, P Nankivell, A Parente, S Parmar, A M Pathanki, L Phelan, P Praveen, S Saeed, N Sharma, J Singh, F Tirotta, D Vijayan, A Geddes, J McCaul, J McMahon, A H Khan, F Khan, A Mansuri, S Mukherjee, M Patel, M Sarigul, S Singh, K L Tan, A Woodham, A Adiamah, H Brewer, A Chowdhury, J Evans, D Humes, J Jackman, A Koh, C Lewis-Lloyd, O Oyende, J Reilly, D Worku, P Cool, G Cribb, K Shepherd, C Bisset, S Moug, N Elson, G Faulkner, P Saleh, C Underwood, G Brixton, L Findlay, T Klatte, A Majkowska, J Manson, R Potter, A Bhalla, Z Chia, P Daliya, A Goyal, E Grimley, A Hamad, A Kumar, F L Malcolm, E Theophilidou, J Bowden, N Campain, I Daniels, C Evans, G Fowler, J John, L Massey, F McDermott, J McGrath, A McLennan, M Ng, J Pascoe, N Rajaretnam, S Bulathsinhala, B Davidson, G Fusai, C Hidalgo Salinas, N Machairas, T Pissanou, J M Pollok, D A Raptis, F Soggiu, H Tzerbinis, S E Xyda, A Beamish, E Davies, R Foulkes, D Magowan, H Nassa, R Ooi, C Price, L Smith, F Solari, A Tang, G Williams, Y Al-Tamimi, A Bacon, N Beasley, D Chew, M Crank, N Ilenkovan, M Macdonald, B Narice, O Rominiyi, A Thompson, I Varley, T Drake, E Harrison, G Linder, J Mayes, R McGregor, R Skipworth, V Zamvar, E Davies, P Hawkin, T Raymond, O Ryska, R Baron, D Dunne, S Gahunia, C Halloran, N Howes, R McKinney, F McNicol, J Russ, P Szatmary, J R Tan, A Thomas, P Whelan, A Anzak, A Banerjee, O Fuwa, F Hughes, J D Jayasinghe, C Knowles, H Kocher, I Leal Silva, F S Ledesma, A Minicozzi, L Navaratne, R Rahman, R Ramamoorthy, C Sohrabi, M Thaha, B Thakur, M Venn, V Yip, R Baumber, J Parry, S Evans, L Jeys, G Morris, M Parry, J Stevenson, N Ahmadi, G Aresu, Z M Barrett-Brown, A S Coonar, H Durio Yates, D Gearon, J Hogan, M King, A Peryt, I S Pradeep, C Smith, M Adishesh, R Atherton, K Baxter, M Brocklehurst, M Chaudhury, N Krishnamohan, J McAleer, G Owens, E Parkin, P Patkar, I Phang, A Aladeojebi, M Ali, B Barmayehvar, A Gaunt, M Gowda, E Halliday, M Kitchen, F Mansour, M Thomas, D Zakai, N Abbassi-Ghadi, H Assalaarachchi, A Currie, M Flavin, A Frampton, M Hague, C Hammer, J Hopper, J Horsnell, S Humphries, A Kamocka, T K Madhuri, S Preston, P Singh, J Stebbing, A Tailor, D Walker, F Aljanadi, M Jones, P Mhandu, C O'Donnell, R Turkington, Z Al-Ishaq, S Bhasin, A S Bodla, A Burahee, A Crichton, R Fossett, N Pigadas, S Pickford, E Rahman, D Snee, R Vidya, N Yassin, F Colombo, D Fountain, M T Hasan, K Karabatsou, R Laurente, O Pathmanaban, A Al-Mukhtar, S Brown, J Edwards, A Giblin, C Kelty, M Lee, G Lye, T Newman, A Sharkey, C Steele, N Sureshkumar Shah, E Whitehall, R Athwal, A Baker, L Jones, C Konstantinou, S Ramcharan, S Singh, J Vatish, R Wilkin, M Ethunandan, G K Sekhon, H Shields, R Singh, F Wensley, S Lawday, A Lyons, T Abbott, S Anwar, K Ghufoor, C Sohrabi, E Chung, R Hagger, A Hainsworth, A Karim, H Owen, A Ramwell, K Williams, C Baker, A Davies, J Gossage, M Kelly, W Knight, J Hall, G Harris, G James, C Kang, D J Lin, A D Rajgor, T Royle, R Scurrah, B Steel, L J Watson, D Choi, R Hutchison, A Jain, V Luoma, H Marcus, R May, A Menon, B Pramodana, L Webber, I A Aneke, P Asaad, B Brown, J Collis, S Duff, A Khan, F Moura, B Wadham, H Warburton, T Elmoslemany, M Jenkinson, C Millward, R Zakaria, S Mccluney, C Parmar, S Shah, J Allison, M S Babar, B Collard, S Goodrum, K Lau, A Patel, R Scott, E Thomas, H Whitmore, D Balasubramaniam, B Jayasankar, S Kapoor, A Ramachandran, A Elhamshary, Smb Imam, K Kapriniotis, V Kasivisvanathan, J Lindsay, S Rakhshani-Moghadam, N Beech, M Chand, L Green, N Kalavrezos, H Kiconco, R McEwen, C Schilling, D Sinha, J Pereca, J Singh, S Chopra, D Egbeare, R Thomas, T Combellack, Sef Jones, M Kornaszewska, M Mohammed, A Sharma, G Tahhan, V Valtzoglou, J Williams, P Eskander, K Gash, L Gourbault, M Hanna, T Maccabe, C Newton, J Olivier, S Rozwadowski, E Teh, D West, H Al-Omishy, M Baig, H Bates, G Di Taranto, K Dickson, N Dunne, C Gill, D Howe, D Jeevan, A Khajuria, K Martin-Ucar AMcEvoy, P Naredla, V Ng, S Robertson, M Sait, D R Sarma, S Shanbhag, T Shortland, S Simmonds, J Skillman, N Tewari, G Walton, M A Akhtar, A Brunt, J McIntyre, K Milne, M M Rashid, A Sgro, K E Stewart, A Turnbull, M Aguilar Gonzalez, S Talukder, C Boyle, D Fernando, K Gallagher, A Laird, D Tham, M Bath, P Patki, C Sohrabi, C Tanabalan, T Arif, C Magee, T Nambirajan, S Powell, R Vinayagam, I Flindall, A Hanson, V Mahendran, S Green, M Lim, L MacDonald, V Miu, L Onos, K Sheridan, R Young, F Alam, O Griffiths, C Houlden, R Jones, V S Kolli, A K Lala, S Leeson, R Peevor, Z Seymour, L Chen, E Henderson, A Loehrer, K Brown, D Fleming, A Haynes, C Heron, C Hill, H Kay, E Leede, K McElhinney, K Olson, E C Osterberg, C Riley, P Srikanth, M Thornhill, D Blazer, G DiLalla, E S Hwang, W Lee, M Lidsky, J Plichta, L Rosenberger, R Scheri, K Shah, K Turnage, J Visgauss, S Zani, J Farma, J Clark, D Kwon, E Etchill, H E Gabre-Kidan AJenny, A Kent, M Ladd, C Long, H Malapati, A Margalit, S Rapaport, J Rose, K Stevens, L Tsai, D Vervoort, P Yesantharao, A Dehal, D Klaristenfeld, K Huynh, L Brown, I Ganly, J Mullinax, N Gusani, J Hazelton, J Maines, J S Oh, A Ssentongo, P Ssentongo, M Azam, A Choudhry, W Marx, J Fleming, A Fuson, J Gigliotti, A Ovaitt, Y Ying, M K Abel, V Andaya, K Bigay, M A Boeck, L Chen, H Chern, C Corvera, I El-Sayed, A Glencer, P Ha, Bcs Hamilton, C Heaton, K Hirose, D M Jablons, K Kirkwood, L Z Kornblith, J R Kratz, R Lee, P N Miller, E Nakakura, B Nunez-Garcia, R O'Donnell, D Ozgediz, P Park, B Robinson, A Sarin, B Sheu, M Varma, K Wai, R Wustrack, M J Xu, D Beswick, J Goddard, J Manor, J Song, T Fullmer, C Gaskill, N Gross, K Kiong, C L Roland, S N Zafar, M Abdallah, A Abouassi, M Almasri, G Kulkarni, H Marwan, M Mehdi, S Aoun, V S Ban, H H Batjer, J Caruso, D Abbott, A Acher, T Aiken, J Barrett, E Foley, P Schwartz, S N Zafar, A Hawkins, A Maiga, J Laufer, S Scasso
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Aged, 80 and over ,Male ,Critical Care ,SARS-CoV-2 ,International Cooperation ,COVID-19 ,Middle Aged ,Cohort Studies ,Logistic Models ,Postoperative Complications ,Elective Surgical Procedures ,Neoplasms ,Outcome Assessment, Health Care ,Humans ,Female ,Epidemics ,Aged - Abstract
PURPOSE As cancer surgery restarts after the first COVID-19 wave, health care providers urgently require data to determine where elective surgery is best performed. This study aimed to determine whether COVID-19–free surgical pathways were associated with lower postoperative pulmonary complication rates compared with hospitals with no defined pathway. PATIENTS AND METHODS This international, multicenter cohort study included patients who underwent elective surgery for 10 solid cancer types without preoperative suspicion of SARS-CoV-2. Participating hospitals included patients from local emergence of SARS-CoV-2 until April 19, 2020. At the time of surgery, hospitals were defined as having a COVID-19–free surgical pathway (complete segregation of the operating theater, critical care, and inpatient ward areas) or no defined pathway (incomplete or no segregation, areas shared with patients with COVID-19). The primary outcome was 30-day postoperative pulmonary complications (pneumonia, acute respiratory distress syndrome, unexpected ventilation). RESULTS Of 9,171 patients from 447 hospitals in 55 countries, 2,481 were operated on in COVID-19–free surgical pathways. Patients who underwent surgery within COVID-19–free surgical pathways were younger with fewer comorbidities than those in hospitals with no defined pathway but with similar proportions of major surgery. After adjustment, pulmonary complication rates were lower with COVID-19–free surgical pathways (2.2% v 4.9%; adjusted odds ratio [aOR], 0.62; 95% CI, 0.44 to 0.86). This was consistent in sensitivity analyses for low-risk patients (American Society of Anesthesiologists grade 1/2), propensity score–matched models, and patients with negative SARS-CoV-2 preoperative tests. The postoperative SARS-CoV-2 infection rate was also lower in COVID-19–free surgical pathways (2.1% v 3.6%; aOR, 0.53; 95% CI, 0.36 to 0.76). CONCLUSION Within available resources, dedicated COVID-19–free surgical pathways should be established to provide safe elective cancer surgery during current and before future SARS-CoV-2 outbreaks.
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- 2021
5. A machine-learning-based bio-psycho-social model for the prediction of non-obstructive and obstructive coronary artery disease
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Raparelli, V, Proietti, M, Romiti, G. F., Seccia, R, Di Teodoro, G., Tanzilli, G, Marrapodi, R, Flego, D, Corica, B, Cangemi, R, Palagi, L, Basili, S, Stefanini, L, and Eva, Group
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- 2021
6. A convergent decomposition algorithm for support vector machines
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Lucidi, S., Palagi, L., Risi, A., and Sciandrone, M.
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- 2007
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7. Le tendinopatie nello sportivo: la gestione del dolore attraverso l’esercizio terapeutico
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Mozzachiodi, E., primary and Palagi, L., additional
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- 2020
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8. One-Way Free Floating Car-Sharing: Applying Vehicle-Generated Data to Assess the Market Demand Potential of Urban Zones
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Chianese, Y. M., primary, Avenali, A., additional, Gambuti, R., additional, and Palagi, L., additional
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- 2017
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9. A LP and MILP methodology to support the planning of transmission power systems
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Capasso, A., primary, Cervone, A., additional, Lamedica, R., additional, and Palagi, L., additional
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- 2016
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10. SpeeDP: An algorithm to compute SDP bounds for very large Max-Cut instances
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Grippo, L., Palagi, L., Piacentini, M., Piccialli, V., and Rinaldi, G.
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- 2011
11. A reinforcement learning approach for QoS/QoE model identification
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Canale, S., primary, Delli Priscoli, F., additional, Monaco, S., additional, Palagi, L., additional, and Suraci, V., additional
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- 2015
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12. Computational approaches to Max-Cut
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Palagi L., Piccialli V., Rendl F., Rinaldi G., and Wiegele A.
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- 2010
13. A Truncated Newton Method in an Augmented Lagrangian Framework for Nonlinear Programming
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Di Pillo, G., Liuzzi, G., Lucidi, S., and Palagi, L.
- Published
- 2007
14. On the convergence of hybrid decomposition methods for SVM training
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Lucidi, S., Palagi, L., Risi, A., and Sciandrone, M.
- Published
- 2006
15. Convergence to 2-nd order stationary points of a primal-dual algorithm model for nonlinear programming
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Di Pillo, G., Lucidi, S., and Palagi, L.
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second-order stationary point ,nonlinear programming ,primal-dual algorithm ,augmented Lagrangian function - Published
- 2005
16. On the convergence of a modified version of the SVMlight algorithm
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Palagi, L. and Sciandrone, M.
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Decomposition methods ,Proximal point ,Support vector machines ,SVMlight algorithm - Abstract
In thiswork, we consider the convex quadratic programming problem arising in support vector machine (SVM), which is a technique designed to solve a variety of learning and pattern recognition problems. Since the Hessian matrix is dense and real applications lead to large-scale problems, several decomposition methods have been proposed, which split the original problem into a sequence of smaller subproblems.SVMlight algorithm is a commonly used decomposition method for SVM, and its convergence has been proved only recently under a suitable block-wise convexity assumption on the objective function. In SVMlight algorithm, the size q of the working set, i.e. the dimension of the subproblem, can be any even number. In the present paper, we propose a decomposition method on the basis of a proximal point modification of the subproblem and the basis of a working set selection rule that includes, as a particular case, the one used by the SVMlight algorithm. We establish the asymptotic convergence of the method, for any size q >= 2 of the working set, and without requiring any further block-wise convexity assumption on the objective function. Furthermore, we show that the algorithm satisfies in a finite number of iterations a stopping criterion based on the violation of the optimality conditions.
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- 2005
17. Quartic formulation of standard quadratic optimization
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Bomze, I. and Palagi, L.
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max clique ,exact merit function ,standard quadratic optimization - Published
- 2005
18. A convergent decomposition algorithm for support vector machines
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Lucidi, S., Palagi, L., Risi, A., and Sciandrone, M.
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- 2004
19. Quartic formulation of standard quadratic optimization problems
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Bomze, M.I. and Palagi, L.
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- 2003
20. EVIDENCE THAT A SALT BRIDGE IN THE LIGHT CHAIN CONTRIBUTES TO THE PHYSICAL STABILITY DIFFERENCE BETWEEN HEAVY AND LIGHT HUMAN FERRITINS
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SANTAMBROGIO P, AROSIO P, PALAGI L, VECCHIO G, LAWSON DM, YEWDALL SJ, ARTYMIUK PJ, HARRISON PM, JAPPELLI R, CESARENI G., LEVI , SONIA MARIA ROSA, Santambrogio, P, Levi, SONIA MARIA ROSA, Arosio, P, Palagi, L, Vecchio, G, Lawson, Dm, Yewdall, Sj, Artymiuk, Pj, Harrison, Pm, Jappelli, R, and Cesareni, G.
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- 1992
21. On the convergence of a modified version of SVM (light) algorithm
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Palagi, L. and Sciandrone, M.
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- 2002
22. Exact penalty function methods for training support vector machines
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Lucidi, S., Palagi, L., and Sciandrone, M.
- Published
- 2001
23. An Exact Algorithm for Nonconvex Quadratic Integer Minimization Using Ellipsoidal Relaxations
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Buchheim, C., primary, De Santis, M., additional, Palagi, L., additional, and Piacentini, M., additional
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- 2013
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24. Effetti emodinamici del Nicorandil e dell’Isosorbide-5-Mononitrato nel trattamento di pazienti ipertesi con angina stabile da sforzo
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DI SOMMA, Salvatore, Micheletti, M. T., Bona, M., Palagi, L., Cuocolo, A., Imbriaco, M., de Divitiis, M., Carotenuto, A., and de Divitiis, O.
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- 1994
25. A Convergent Hybrid Decomposition Algorithm Model for SVM Training
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Lucidi, S., primary, Palagi, L., additional, Risi, A., additional, and Sciandrone, M., additional
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- 2009
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26. Doppler echocardiographic assessment of left ventricular diastolic fuction in acromegaly
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Vitarelli, Antonino, Paoletti, Ml, Baldelli, R, and Palagi, L.
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- 1992
27. On the convergence of a modified version of SVMlightalgorithm
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Palagi, L., primary and Sciandrone, M., additional
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- 2005
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28. A superlinearly convergent primal — dual algorithm model for constrained optimization problems with bounded variables
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Di Pillo, G., primary, Lucid1, S., additional, and Palagi, L., additional
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- 2000
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29. An exact penalty-lagrangian approach for a class of constrained optimization problems with bounded variables
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Di Pillo, G., primary, Lucidi, S., additional, and Palagi, L., additional
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- 1993
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30. Evidence that a salt bridge in the light chain contributes to the physical stability difference between heavy and light human ferritins.
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Santambrogio, P, primary, Levi, S, additional, Arosio, P, additional, Palagi, L, additional, Vecchio, G, additional, Lawson, D.M., additional, Yewdall, S.J., additional, Artymiuk, P.J., additional, Harrison, P.M., additional, and Jappelli, R, additional
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- 1992
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31. Decomposition Algorithm Model for Singly Linearly-Constrained Problems Subject to Lower and Upper Bounds.
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C. J. Lin, Lucidi, S., Palagi, L., Risi, A., and Sciandrone, M.
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ALGORITHMS ,PROBLEM solving ,MATHEMATICAL decomposition ,MATHEMATICAL variables ,ANALYSIS of variance ,PHASE equilibrium - Abstract
Many real applications can be formulated as nonlinear minimization problems with a single linear equality constraint and box constraints. We are interested in solving problems where the number of variables is so huge that basic operations, such as the evaluation of the objective function or the updating of its gradient, are very time consuming. Thus, for the considered class of problems (including dense quadratic programs), traditional optimization methods cannot be applied directly. In this paper, we define a decomposition algorithm model which employs, at each iteration, a descent search direction selected among a suitable set of sparse feasible directions. The algorithm is characterized by an acceptance rule of the updated point which on the one hand permits to choose the variables to be modified with a certain degree of freedom and on the other hand does not require the exact solution of any subproblem. The global convergence of the algorithm model is proved by assuming that the objective function is continuously differentiable and that the points of the level set have at least one component strictly between the lower and upper bounds. Numerical results on large-scale quadratic problems arising in the training of support vector machines show the effectiveness of an implemented decomposition scheme derived from the general algorithm model. [ABSTRACT FROM AUTHOR]
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- 2009
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32. Experimental Study of the Pathogenesis of Ventricular Extrasystolia.
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Baschieri, L., Palagi, L., and Puletti, M.
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- 1967
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33. An exact penalty-lagrangian approach for a class of constrained optimization problems with bounded variables
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Pillo, G. Di, Lucidi, S., and Palagi, L.
- Abstract
In this paper we consider a class of equality constrained optimization problems with box constraints on a part of its variablesThe study of non linear programming problems with such a structure is justified by the existence of practical problems in many fields as, for example, optimal control or economic modelling. Typically, the dimension of these problems are very large and, in such situation, the classical methods to solve NLP problems may have serious drawbacks. In this paper we define a new continuosly differentiable exact penalty function which transforms the original constrained problem into an unconstrained one and it is well suited to tackle large scale problems. In particular this new function is based on a mixed exact penalty-Lagrangian approach and this allows us to take full advantage of the particular structure of the considered class of problems. We show that there is a one to one correspondence between Kuhn-Tucker point (local and global minimum points) of the constrained problem and stationary point (local and global minimum points) of the merit function. Thus, the unconstrained minimization of the exact penalty-Lagrangian function yields the solution of the original constrained problem
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- 1993
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34. Modelling the Electrostatic Fluidised Bed (EFB) coating process using Support Vector Machines (SVMs)
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Luigi Silvestri, Annamaria Gisario, Massimiliano Barletta, Laura Palagi, Barletta, Massimiliano, Gisario, A., Palagi, L., and Silvestri, L.
- Subjects
Engineering ,Support vector machine ,Artificial neural network ,Support vector machines svms ,business.industry ,General Chemical Engineering ,Process (computing) ,Analytical modelling ,Coating proce ,engineering.material ,Automation ,Powder paint ,Coating ,Settore ING-IND/16 - Tecnologie e Sistemi di Lavorazione ,Electrostatic fluidised bed ,Process control ,support vector machine ,powder paints ,coating process ,analytical modelling ,electrostatic fluidised bed ,business ,Process engineering ,Simulation - Abstract
An Electrostatic Fluidised Bed (EFB) coating process is used as an eco-friendly alternative to an electrostatic spraying process to coat components of particularly complex shapes with powder paints. Although fluidised beds are well known systems and are widespread throughout several industrial domains, the implementation of appropriate process control procedures is still extremely difficult. Fluidised bed processes are governed by the hydrodynamic behaviour of the suspended powders. The solution of the hydrodynamic laws in closed form is often not realisable because they are complicated or require large amounts of computational time. In contrast, empirical or simplified analytical models as well as learning machine techniques are often used for the control and automation of fluidised bed processes. Therefore, the current study proposes modelling an EFB coating process using Support Vector Machines (SVMs). SVMs were determined to appropriately match the experimental coating thicknesses and demonstrate good prediction capability. The SVMs were compared with both empirical and Artificial Neural Network (ANN) models to demonstrate how an SVM could be a particularly interesting alternative for modelling "in service" and high-duty equipment. © 2014 Elsevier B.V.
- Published
- 2014
35. Incorporating temporal dynamics of mutations to enhance the prediction capability of antiretroviral therapy's outcome for HIV-1.
- Author
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Di Teodoro G, Pirkl M, Incardona F, Vicenti I, Sönnerborg A, Kaiser R, Palagi L, Zazzi M, and Lengauer T
- Subjects
- Humans, Drug Resistance, Viral genetics, Viral Load, Anti-HIV Agents therapeutic use, Anti-HIV Agents pharmacology, Treatment Outcome, HIV-1 genetics, HIV Infections drug therapy, HIV Infections virology, Mutation
- Abstract
Motivation: In predicting HIV therapy outcomes, a critical clinical question is whether using historical information can enhance predictive capabilities compared with current or latest available data analysis. This study analyses whether historical knowledge, which includes viral mutations detected in all genotypic tests before therapy, their temporal occurrence, and concomitant viral load measurements, can bring improvements. We introduce a method to weigh mutations, considering the previously enumerated factors and the reference mutation-drug Stanford resistance tables. We compare a model encompassing history (H) with one not using this information (NH)., Results: The H-model demonstrates superior discriminative ability, with a higher ROC-AUC score (76.34%) than the NH-model (74.98%). Wilcoxon test results confirm significant improvement of predictive accuracy for treatment outcomes through incorporating historical information. The increased performance of the H-model might be attributed to its consideration of latent HIV reservoirs, probably obtained when leveraging historical information. The findings emphasize the importance of temporal dynamics in acquiring mutations. However, our result also shows that prediction accuracy remains relatively high even when no historical information is available., Availability and Implementation: This analysis was conducted using the Euresist Integrated DataBase (EIDB). For further validation, we encourage reproducing this study with the latest release of the EIDB, which can be accessed upon request through the Euresist Network., (© The Author(s) 2024. Published by Oxford University Press.)
- Published
- 2024
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36. Seeking for Innovation with Magnetic Resonance Imaging Paramagnetic Contrast Agents: Relaxation Enhancement via Weak and Dynamic Electrostatic Interactions with Positively Charged Groups on Endogenous Macromolecules.
- Author
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Stefania R, Palagi L, Di Gregorio E, Ferrauto G, Dinatale V, Aime S, and Gianolio E
- Subjects
- Humans, Static Electricity, Magnetic Resonance Imaging methods, Pyrenes, Gadolinium, Contrast Media chemistry, Organometallic Compounds chemistry, Heterocyclic Compounds
- Abstract
Gd-L1 is a macrocyclic Gd-HPDO3A derivative functionalized with a short spacer to a trisulfonated pyrene. When compared to Gd-HPDO3A, the increased relaxivity appears to be determined by both the higher molecular weight and the occurrence of an intramolecularly catalyzed prototropic exchange of the coordinated OH moiety. In water, Gd-L1 displayed a relaxivity of 7.1 mM
-1 s-1 (at 298 K and 0.5 T), slightly increasing with the concentration likely due to the onset of intermolecular aggregation. A remarkably high and concentration-dependent relaxivity was measured in human serum (up to 26.5 mM-1 s-1 at the lowest tested concentration of 0.005 mM). The acquisition of1 H-nuclear magnetic relaxation dispersion (NMRD) and17 O- R2 vs T profiles allowed to get an in-depth characterization of the system. In vitro experiments in the presence of human serum albumin, γ-globulins, and polylysine, as well as using media mimicking the extracellular matrix, provided strong support to the view that the trisulfonated pyrene fosters binding interactions with the exposed positive groups on the surface of proteins, responsible for a remarkable in vivo hyperintensity in T1w MR images. The in vivo MR images of the liver, kidneys, and spleen showed a marked contrast enhancement in the first 10 min after the i.v. injection of Gd-L1, which was 2-6-fold higher than that for Gd-HPDO3A, while maintaining a very similar excretion behavior. These findings may pave the way to an improved design of MRI GBCAs, for the first time, based on the setup of weak and dynamic interactions with abundant positive groups on serum and ECM proteins.- Published
- 2024
- Full Text
- View/download PDF
37. Deep Neural Network Regression to Assist Non-Invasive Diagnosis of Portal Hypertension.
- Author
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Baldisseri F, Wrona A, Menegatti D, Pietrabissa A, Battilotti S, Califano C, Cristofaro A, Di Giamberardino P, Facchinei F, Palagi L, Giuseppi A, and Delli Priscoli F
- Abstract
Portal hypertension is a complex medical condition characterized by elevated blood pressure in the portal venous system. The conventional diagnosis of such disease often involves invasive procedures such as liver biopsy, endoscopy, or imaging techniques with contrast agents, which can be uncomfortable for patients and carry inherent risks. This study presents a deep neural network method in support of the non-invasive diagnosis of portal hypertension in patients with chronic liver diseases. The proposed method utilizes readily available clinical data, thus eliminating the need for invasive procedures. A dataset composed of standard laboratory parameters is used to train and validate the deep neural network regressor. The experimental results exhibit reasonable performance in distinguishing patients with portal hypertension from healthy individuals. Such performances may be improved by using larger datasets of high quality. These findings suggest that deep neural networks can serve as useful auxiliary diagnostic tools, aiding healthcare professionals in making timely and accurate decisions for patients suspected of having portal hypertension.
- Published
- 2023
- Full Text
- View/download PDF
38. A machine-learning based bio-psycho-social model for the prediction of non-obstructive and obstructive coronary artery disease.
- Author
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Raparelli V, Romiti GF, Di Teodoro G, Seccia R, Tanzilli G, Viceconte N, Marrapodi R, Flego D, Corica B, Cangemi R, Pilote L, Basili S, Proietti M, Palagi L, and Stefanini L
- Subjects
- Adult, Humans, Female, Middle Aged, Aged, Male, Artificial Intelligence, Coronary Angiography methods, Machine Learning, Cytokines, Risk Factors, Predictive Value of Tests, Coronary Artery Disease diagnosis, Frailty, Myocardial Ischemia
- Abstract
Background: Mechanisms of myocardial ischemia in obstructive and non-obstructive coronary artery disease (CAD), and the interplay between clinical, functional, biological and psycho-social features, are still far to be fully elucidated., Objectives: To develop a machine-learning (ML) model for the supervised prediction of obstructive versus non-obstructive CAD., Methods: From the EVA study, we analysed adults hospitalized for IHD undergoing conventional coronary angiography (CCA). Non-obstructive CAD was defined by a stenosis < 50% in one or more vessels. Baseline clinical and psycho-socio-cultural characteristics were used for computing a Rockwood and Mitnitski frailty index, and a gender score according to GENESIS-PRAXY methodology. Serum concentration of inflammatory cytokines was measured with a multiplex flow cytometry assay. Through an XGBoost classifier combined with an explainable artificial intelligence tool (SHAP), we identified the most influential features in discriminating obstructive versus non-obstructive CAD., Results: Among the overall EVA cohort (n = 509), 311 individuals (mean age 67 ± 11 years, 38% females; 67% obstructive CAD) with complete data were analysed. The ML-based model (83% accuracy and 87% precision) showed that while obstructive CAD was associated with higher frailty index, older age and a cytokine signature characterized by IL-1β, IL-12p70 and IL-33, non-obstructive CAD was associated with a higher gender score (i.e., social characteristics traditionally ascribed to women) and with a cytokine signature characterized by IL-18, IL-8, IL-23., Conclusions: Integrating clinical, biological, and psycho-social features, we have optimized a sex- and gender-unbiased model that discriminates obstructive and non-obstructive CAD. Further mechanistic studies will shed light on the biological plausibility of these associations., Clinical Trial Registration: NCT02737982., (© 2023. The Author(s).)
- Published
- 2023
- Full Text
- View/download PDF
39. The Use of Machine Learning for Inferencing the Effectiveness of a Rehabilitation Program for Orthopedic and Neurological Patients.
- Author
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Santilli V, Mangone M, Diko A, Alviti F, Bernetti A, Agostini F, Palagi L, Servidio M, Paoloni M, Goffredo M, Infarinato F, Pournajaf S, Franceschini M, Fini M, and Damiani C
- Subjects
- Humans, Algorithms, Patients, Activities of Daily Living, Artificial Intelligence, Machine Learning
- Abstract
Advance assessment of the potential functional improvement of patients undergoing a rehabilitation program is crucial in developing precision medicine tools and patient-oriented rehabilitation programs, as well as in better allocating resources in hospitals. In this work, we propose a novel approach to this problem using machine learning algorithms focused on assessing the modified Barthel index (mBI) as an indicator of functional ability. We build four tree-based ensemble machine learning models and train them on a private training cohort of orthopedic (OP) and neurological (NP) hospital discharges. Moreover, we evaluate the models using a validation set for each category of patients using root mean squared error (RMSE) as an absolute error indicator between the predicted mBI and the actual values. The best results obtained from the study are an RMSE of 6.58 for OP patients and 8.66 for NP patients, which shows the potential of artificial intelligence in predicting the functional improvement of patients undergoing rehabilitation.
- Published
- 2023
- Full Text
- View/download PDF
40. Supervised and unsupervised learning to classify scoliosis and healthy subjects based on non-invasive rasterstereography analysis.
- Author
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Colombo T, Mangone M, Agostini F, Bernetti A, Paoloni M, Santilli V, and Palagi L
- Subjects
- Adolescent, Adult, Female, Humans, Imaging, Three-Dimensional methods, Male, Retrospective Studies, Scoliosis classification, Supervised Machine Learning, Unsupervised Machine Learning, Young Adult, Scoliosis diagnosis
- Abstract
The aim of our study was to classify scoliosis compared to to healthy patients using non-invasive surface acquisition via Video-raster-stereography, without prior knowledge of radiographic data. Data acquisitions were made using Rasterstereography; unsupervised learning was adopted for clustering and supervised learning was used for prediction model Support Vector Machine and Deep Network architectures were compared. A M-fold cross validation procedure was performed to evaluate the results. The accuracy and balanced accuracy of the best supervised model were close to 85%. Classification rates by class were measured using the confusion matrix, giving a low percentage of unclassified patients. Rasterstereography has turned out to be a good tool to distinguish subject with scoliosis from healthy patients limiting the exposure to unnecessary radiations., Competing Interests: The authors have read the journal’s policy and have the following competing interests: TC is an employee of ACT Operations Research IT srl. This does not alter our adherence to PLOS ONE policies on sharing data and materials. There are no patents, products in development or marketed products associated with this research to declare.
- Published
- 2021
- Full Text
- View/download PDF
41. MOSES: A New Approach to Integrate Interactome Topology and Functional Features for Disease Gene Prediction.
- Author
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Petti M, Farina L, Francone F, Lucidi S, Macali A, Palagi L, and De Santis M
- Subjects
- Algorithms, Databases, Genetic, Humans, Molecular Sequence Annotation, Precision Medicine, Computational Biology methods, Genetic Predisposition to Disease genetics, Protein Interaction Maps
- Abstract
Disease gene prediction is to date one of the main computational challenges of precision medicine. It is still uncertain if disease genes have unique functional properties that distinguish them from other non-disease genes or, from a network perspective, if they are located randomly in the interactome or show specific patterns in the network topology. In this study, we propose a new method for disease gene prediction based on the use of biological knowledge-bases (gene-disease associations, genes functional annotations, etc.) and interactome network topology. The proposed algorithm called MOSES is based on the definition of two somewhat opposing sets of genes both disease-specific from different perspectives: warm seeds (i.e., disease genes obtained from databases) and cold seeds (genes far from the disease genes on the interactome and not involved in their biological functions). The application of MOSES to a set of 40 diseases showed that the suggested putative disease genes are significantly enriched in their reference disease. Reassuringly, known and predicted disease genes together, tend to form a connected network module on the human interactome, mitigating the scattered distribution of disease genes which is probably due to both the paucity of disease-gene associations and the incompleteness of the interactome.
- Published
- 2021
- Full Text
- View/download PDF
42. Fe(deferasirox) 2 : An Iron(III)-Based Magnetic Resonance Imaging T 1 Contrast Agent Endowed with Remarkable Molecular and Functional Characteristics.
- Author
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Palagi L, Di Gregorio E, Costanzo D, Stefania R, Cavallotti C, Capozza M, Aime S, and Gianolio E
- Subjects
- Animals, Binding Sites, Cell Line, Tumor, Contrast Media metabolism, Contrast Media pharmacokinetics, Coordination Complexes metabolism, Coordination Complexes pharmacokinetics, Deferasirox metabolism, Deferasirox pharmacokinetics, Female, Humans, Iron chemistry, Magnetic Resonance Imaging, Mice, Inbred BALB C, Protein Binding, Serum Albumin, Human chemistry, Serum Albumin, Human metabolism, Mice, Contrast Media chemistry, Coordination Complexes chemistry, Deferasirox analogs & derivatives
- Abstract
The search for alternatives to Gd-containing magnetic resonance imaging (MRI) contrast agents addresses the field of Fe(III)-bearing species with the expectation that the use of an essential metal ion may avoid the issues raised by the exogenous Gd. Attention is currently devoted to highly stable Fe(III) complexes with hexacoordinating ligands, although they may lack any coordinated water molecule. We found that the hexacoordinated Fe(III) complex with two units of deferasirox, a largely used iron sequestering agent, owns properties that can make it a viable alternative to Gd-based agents. Fe(deferasirox)
2 displays an outstanding thermodynamic stability, a high binding affinity to human serum albumin (three molecules of complex are simultaneously bound to the protein), and a good relaxivity that increases in the range 20-80 MHz. The relaxation enhancement is due to second sphere water molecules likely forming H-bonds with the coordinating phenoxide oxygens. A further enhancement was observed upon the formation of the supramolecular adduct with albumin. The binding sites of Fe(deferasirox)2 on albumin were characterized by relaxometric competitive assays. Preliminary in vivo imaging studies on a tumor-bearing mouse model indicate that, on a 3 T MRI scanner, the contrast ability of Fe(deferasirox)2 is comparable to the one shown by the commercial Gd(DTPA) agent. ICP-MS analyses on blood samples withdrawn from healthy mice administered with a dose of 0.1 mmol/kg of Fe(deferasirox)2 showed that the complex is completely removed in 24 h.- Published
- 2021
- Full Text
- View/download PDF
43. Machine Learning Use for Prognostic Purposes in Multiple Sclerosis.
- Author
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Seccia R, Romano S, Salvetti M, Crisanti A, Palagi L, and Grassi F
- Abstract
The course of multiple sclerosis begins with a relapsing-remitting phase, which evolves into a secondarily progressive form over an extremely variable period, depending on many factors, each with a subtle influence. To date, no prognostic factors or risk score have been validated to predict disease course in single individuals. This is increasingly frustrating, since several treatments can prevent relapses and slow progression, even for a long time, although the possible adverse effects are relevant, in particular for the more effective drugs. An early prediction of disease course would allow differentiation of the treatment based on the expected aggressiveness of the disease, reserving high-impact therapies for patients at greater risk. To increase prognostic capacity, approaches based on machine learning (ML) algorithms are being attempted, given the failure of other approaches. Here we review recent studies that have used clinical data, alone or with other types of data, to derive prognostic models. Several algorithms that have been used and compared are described. Although no study has proposed a clinically usable model, knowledge is building up and in the future strong tools are likely to emerge.
- Published
- 2021
- Full Text
- View/download PDF
44. Liposome-Based Bioassays.
- Author
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Sforzi J, Palagi L, and Aime S
- Abstract
This review highlights the potential of using liposomes in bioassays. Liposomes consist of nano- or micro-sized, synthetically constructed phospholipid vesicles. Liposomes can be loaded with a number of reporting molecules that allow a dramatic amplification of the detection threshold in bioassays. Liposome-based sensors bind or react with the biological components of targets through the introduction of properly tailored vectors anchored on their external surface. The use of liposome-based formulations allows the set-up of bioassays that are rapid, sensitive, and often suitable for in-field applications. Selected applications in the field of immunoassays, as well as recognition/assessment of corona proteins, nucleic acids, exosomes, bacteria, and viruses are surveyed. The role of magnetoliposomes is also highlighted as an additional tool in the armory of liposome-based systems for bioassays.
- Published
- 2020
- Full Text
- View/download PDF
45. Considering patient clinical history impacts performance of machine learning models in predicting course of multiple sclerosis.
- Author
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Seccia R, Gammelli D, Dominici F, Romano S, Landi AC, Salvetti M, Tacchella A, Zaccaria A, Crisanti A, Grassi F, and Palagi L
- Subjects
- Adolescent, Adult, Algorithms, Child, Disease Progression, Female, Humans, Machine Learning, Male, Middle Aged, Neural Networks, Computer, Probability, Rome, Support Vector Machine, Young Adult, Multiple Sclerosis pathology
- Abstract
Multiple Sclerosis (MS) progresses at an unpredictable rate, but predictions on the disease course in each patient would be extremely useful to tailor therapy to the individual needs. We explore different machine learning (ML) approaches to predict whether a patient will shift from the initial Relapsing-Remitting (RR) to the Secondary Progressive (SP) form of the disease, using only "real world" data available in clinical routine. The clinical records of 1624 outpatients (207 in the SP phase) attending the MS service of Sant'Andrea hospital, Rome, Italy, were used. Predictions at 180, 360 or 720 days from the last visit were obtained considering either the data of the last available visit (Visit-Oriented setting), comparing four classical ML methods (Random Forest, Support Vector Machine, K-Nearest Neighbours and AdaBoost) or the whole clinical history of each patient (History-Oriented setting), using a Recurrent Neural Network model, specifically designed for historical data. Missing values were handled by removing either all clinical records presenting at least one missing parameter (Feature-saving approach) or the 3 clinical parameters which contained missing values (Record-saving approach). The performances of the classifiers were rated using common indicators, such as Recall (or Sensitivity) and Precision (or Positive predictive value). In the visit-oriented setting, the Record-saving approach yielded Recall values from 70% to 100%, but low Precision (5% to 10%), which however increased to 50% when considering only predictions for which the model returned a probability above a given "confidence threshold". For the History-oriented setting, both indicators increased as prediction time lengthened, reaching values of 67% (Recall) and 42% (Precision) at 720 days. We show how "real world" data can be effectively used to forecast the evolution of MS, leading to high Recall values and propose innovative approaches to improve Precision towards clinically useful values., Competing Interests: The authors have declared that no competing interests exist.
- Published
- 2020
- Full Text
- View/download PDF
46. Data of patients undergoing rehabilitation programs.
- Author
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Seccia R, Boresta M, Fusco F, Tronci E, Di Gemma E, Palagi L, Mangone M, Agostini F, Bernetti A, Santilli V, Damiani C, Goffredo M, and Franceschini M
- Abstract
In this data article, we present a dataset made up of personal, social and clinical records related to patients undergoing a rehabilitation program. Data refers to records registered in the "Acceptance/Discharge Report for the rehabilitation area" (ADR) which implements the Italian law (DGR 731/2005) and refer to hospitalization at the rehabilitation hospital of Rome "San Raffaele" in the years from 2015 to 2018 of patients suffering from orthopedic and neurological pathologies. For each ADR report, the clinical status of the patient at the date of acceptance and discharge is reported using, among other, the Barthel index as a measure of the Activities Daily Living of the patient. These data can be used to understand the influence of many different factors in the rehabilitation progress of clinical patients., (© 2020 The Authors. Published by Elsevier Inc.)
- Published
- 2020
- Full Text
- View/download PDF
47. Cooperative Iodide Pd(0)-Catalysed Coupling of Alkoxyallenes and N-Tosylhydrazones: A Selective Synthesis of Conjugated and Skipped Dienes.
- Author
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Parisotto S, Palagi L, Prandi C, and Deagostino A
- Abstract
Palladium(0)-catalysed hydro-alkylation or -alkenylation of alkoxyallenes with N-tosylhydrazones gives direct access to conjugated and skipped 1-alkoxydienes with high efficiency and excellent functional-group compatibility. The reaction is proposed to involve the in situ-formed t-butanol as proton source in the key step of the allylpalladium(II) species generation. Moreover, lithium iodide or iodobenzene are employed as an unprecedented iodide (I
- ) reservoir to sustain the catalytic cycle., (© 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.)- Published
- 2018
- Full Text
- View/download PDF
48. Factors affecting insufficiency in activity daily living in the elderly.
- Author
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Palagi L, Paoletti ML, Alma A, Re M, Petrilli GA, Salerno F, and Murgiano S
- Subjects
- Aged, Aged, 80 and over, Case-Control Studies, Female, Humans, Inpatients, Male, Activities of Daily Living, Aging physiology
- Abstract
Background: In a "case-control" study we investigated the correlations among twenty-four clinical signs of "functional impairment" and probability of "activity daily living insufficiency"., Methods: The study involved 788 randomised inpatients, aged 65 years and over, of nineteen long-stay hospitals of an Italian region (Lazio, Rome). We measured self care autonomy, mobility and continence, on a modified Barthel's scale; the score on Barthel's scale, Barthel Index (BI), was correlated to twenty-four signs of "functional impairment" (explicative variables). Of these variables entered in stepwise regression only "cognitive impairment" (coef. B-22), "paralysis" (coef. B-21), "body weight reduction over 10 kg vs ideal weight" (coef. B-12), "joint deformation" (coef. B-7) and "visual impairment" (coef. B-5). Insufficiency in daily living is defined by BI < 100. The presence of these five clinical signs leads to the likelihood of "activity daily living insufficiency" to 0.996. The trend of cognitive impairment to rise with age could be responsible for the inverse regression between age and BI., Results: There was no significant correlation between BI and sex. Hearing impairment, serum creatinine level > or = 4 mg/dl, bronchospasm, obstructive and restrictive ventilation disorders, precordial pain on stress or spontaneous and dyspnea are not significantly correlated to the Barthel Index Score and to the likelihood of insufficiency in daily living activity.
- Published
- 1997
49. Cardiac electrical activity in women doing judo. Vectorcardiographic study.
- Author
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Mottironi P, Lino A, Palagi L, Nardo P, and Palazzuolo C
- Subjects
- Adolescent, Adult, Female, Humans, Vectorcardiography, Heart physiology, Physical Fitness, Sports Medicine
- Published
- 1983
50. [Form and orientation of the cardiac vector in systemic arterial hypertension].
- Author
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Palagi L, Mottironi P, Luongo A, and Mennuni M
- Subjects
- Adolescent, Adult, Aged, Female, Heart Diseases etiology, Humans, Male, Middle Aged, Heart Diseases diagnosis, Hypertension complications, Vectorcardiography
- Published
- 1981
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