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5. Are all people with diabetes and cardiovascular risk factors or microvascular complications at very high risk? Findings from the Risk and Prevention Study

Author

Marzona, Irene, Avanzini, Fausto, Lucisano, Giuseppe, Tettamanti, Mauro, Baviera, Marta, Nicolucci, Antonio, Roncaglioni, Maria Carla, Tombesi, M., Tognoni, G., Massa, E., Marrocco, W., Micalella, M., Caimi, V., Longoni, P., Avanzini, F., Franzosi, M. G., Roncaglioni, M. C., Marzona, I., Baviera, M., Monesi, L., Pangrazzi, I., Barlera, S., Milani, V., Nicolis, E., Casola, C., Clerici, F., Palumbo, A., Sgaroni, G., Marchioli, R., Silletta, M. G., Pioggiarella, R., Scarano, M., Marfisi, R. M., Flamminio, A., Macino, L., Ferri, B., Pera, C., Polidoro, A., Abbatino, D., Acquati, M., Addorisio, G., Adinolfi, D., Adreani, L., Agistri, M. R., Agneta, A., Agnolio, M. L., Agostini, N., Agostino, G., Airò, A., Alaimo, N., Albano, M., Albano, N., Alecci, G., Alemanno, S., Alexanian, A., Alfarano, M., Alfè, L., Alonzo, N., Alvino, S., Ancora, A., Andiloro, S., Andreatta, E., Angeli, S., Angiari, F., Angilletti, V., Annicchiarico, C., Anzivino, M., Aprea, R., Aprile, A., Aprile, E., Aprile, I., Aprile, L., Armellani, V., Arnetoli, M., Aronica, A., Autiero, V., Bacca, G., Baccalaro, A. M., Bacci, M., Baglio, G., Bagnani, M., Baiano, A., Baldari, A., Ballarini, L., Banchi, G., Bandera, R., Bandini, F., Baratella, M., Barbieri, A., Barbieri Vita, A., Bardi, M., Barlocchi, M., Baron, P., Bartoli, M., Basile, A., Basile, F., Basile, S., Battaggia, A., Battaglia, A., Baù, A., Beconcini, G., Beggio, R., Belfiore, P. A., Belicchi, M., Bellamoli, S., Bellini, C., Bellomo, M., Benetollo, C., Benetti, R., Beretta, E., Bertalero, P., Bertaso, F. G., Bertolani, U., Bettelli, G., Biagiotti, G., Bianchi, S., Bianco, G., Biccari, F., Bigioli, F., Bindi, M., Bisanti, G., Bitetti, E. M., Blasetti, M. P., Blesi, F., Boato, V., Boga, S., Boidi, E., Boldrin, G., Bollati, A., Bolzan, L., Bolzonella, S., Bonardi, P., Bonato, G. B., Bonci, M., Bonfitto, G., Bonincontro, E., Boninsegna, F., Bonissone, D., Bono, L., Bonollo, E., Borghi, M., Borioli, N., Borsatto, M., Bosco, T., Bosisio Pioltelli, M., Botarelli, C., Botassis, S., Bottini, F., Bottos, C., Bova, G., Bova, V., Bozzani, A., Bozzetto, R. M., Braga, V. T., Braglia, M., Bramati, E., Brazzoli, C., Breglia, G., Brescia, A., Briganti, D., Brigato, G., Brocchi, A., Brosio, F. A., Bruni, E., Buscaglia, E., Bussini, M. D., Bussotti, A., Buzzaccarini, F., Buzzatti, A., Caccamo, G., Cacciavillani, C., Caggiano, G., Caimi, V., Calciano, F. P., Calderisi, M., Calienno, S., Caltagirone, P., Calzolari, I., Cammisa, M., Campanaro, M., Campanella, G. B., Campese, F., Canali, G., Candiani, D. E. L., Canepa, R., Canini, D., Canino, A., Cantoro, E. A., Capilupi, V., Capotosto, P., Cappelli, B., Capraro, G., Carafa, F. A., Carano, Q., Carcaterra, V., Carriero, D., Carrozzo, G., Cartanese, M., Casalena, M., Casarola, M., Caso, C., Casotto, M., Castaldi, F., Castegnaro, R., Castellani, G., Castri, S., Catalano, E., Catinello, N., Caturano, G., Cavallaro, R., Cavallo, A. M., Cavallo, G., Cavion, M. T., Cavirani, G., Cazzaniga, F., Cazzetta, D., Cecconi, V., Cefalo, A., Celebrano, M., Celora, A., Centonze, P., Cerati, D., Cesaretti, D., Checchia, G., Checchin, A., Cherubini, M., Chianese, L., Chiappa, A., Chiappa, M. V., Chiariello, G., Chiavini, G., Chicco, M., Chiumeo, F., Ciacciarelli, A., Ciaci, D., Ciancaglini, R., Cicale, C., Cicale, S., Cipolla, A., Ciruolo, A., Citeri, A. L., Citterio, G., Clerici, M., Coazzoli, E., Collecchia, G., Colletta, F., Colombo, I., Colorio, P., Coluccia, S., Comerio, M., Comoretto, P., Compagni, M., Conte, O., Contri, S., Contrisciani, A., Coppetti, T., Corasaniti, F., Corradi, M. T., Corsano, A., Corsini, A., Corti, N., Costantini, G., Costantino, A., Cotroneo, S., Cozzi, D., Cravello, M. G., Cristiano, E., Cucchi, R., Cusmai, L., D’Errico, G. B., D’Agostino, P., Dal Bianco, L., Dal Mutto, U., Dal Pozzo, G., Dallapiccola, P., Dallatorre, G., Dalle Molle, G., Dalloni, E., D’Aloiso, A., D’Amicis, G., Danese, R., Danieli, D., Danisi, G., D’Anna, M. A., Danti, G., D’Ascanio, S., Davidde, G., De Angeli, D., De Bastiani, R., De Battisti, A., De Bellis, A., De Berardinis, G., De Carlo, F., De Giorgi, D., De Gobbi, R., De Lorenzis, E., De Luca, P., De Martini, G., De Marzi, M., De Matteis, D., De Padova, S., De Polo, P., De Sabato, N., De Stefano, T., De Vita, M. T., De Vito, U., De Zolt, V., Debernardi, F., Del Carlo, A., Del Re, G., Del Zotti, F., D’Elia, R., Della Giovanna, P., Dell’Acqua, L., Dell’Orco, R. L., Demaria, G., Di Benedetto, M. G., Di Chiara, G., Di Corcia, V., Di Domizio, O., Di Donato, P., Di Donato, S., Di Fermo, G., Di Franco, M., Di Giovannantonio, G., Di Lascio, G., Di Lecce, G., Di Lorenzo, N., Di Maro, T., Di Mattia, Q., Di Michele, E., Di Modica, R. S., Di Murro, D., Di Noi, M. C., Di Paoli, V., Di Santi, M., Di Sanzo, A., Di Turi, C., Diazzi, A., Dileo, I., D’Ingianna, A. P., Dolci, A., Donà, G., Donato, C., Donato, P., Donini, A., Donna, M. E., Donvito, T. V., Esposito, L., Esposito, N., Evangelista, M., Faita, G., Falco, M., Falcone, D. A., Falorni, F., Fanciullacci, A., Fanton, L., Fasolo, L., Fassina, R., Fassone, A., Fatarella, P., Fedele, F., Fera, I., Fera, L., Ferioli, S., Ferlini, M. G., Ferlino, R., Ferrante, G., Ferrara, F. N., Ferrarese, M. F., Ferrari, G., Ferrari, O., Ferreri, A., Ferroni, M., Fezzi, G., Figaroli, C., Fina, M. G., Fioretta, A., Fiorucci, C., Firrincieli, R., Fischetti, M., Fischietti, G., Fiume, D. C., Flecchia, G., Forastiere, G., Fossati, B., Franceschi, P. L., Franchi, L., Franzoso, F., Frapporti, G., Frasca, G., Frisotti, A., Fumagalli, G., Fusco, D., Gabriele, P., Gabrieli, A., Gagliano, D., Galimberti, G., Galli, A., Gallicchio, N., Gallio, F., Gallipoli, T., Gallo, P., Galopin, T., Gambarelli, L., Garbin, A., Garozzo, G. M., Gasparri, R., Gastaldo, M., Gatti, E., Gazzaniga, P., Gennachi, N., Gentile, R. V., Germani, P., Gesualdi, F., Gherardi, E., Ghezzi, C., Ghidini, M. G., Ghionda, F., Giacci, L., Gialdini, D., Giampaolo, C., Giancane, R., Giannanti, A., Giannese, S., Giannini, L., Giaretta, M., Giaretta, R., Giavardi, L., Giordano, P., Giordano, E., Giordano, B., Gioria, G. M., Giugliano, R., Grassi, E. A., Greco, A., Greco, L., Grilletti, N., Grimaldi, N., Grisetti, G., Groppelli, G., Gualtieri, L., Guarducci, M., Guastella, G., Guerra, M., Guerrini, F., Guglielmini, A., Guido, A., Gulotta, P., Iacono, E., Iadarola, G., Ianiro, G., Iarussi, V., Ieluzzi, M. L., Ierardi, C., Ingaldi, F., Interlandi, S., Iocca, M., Iorno, A., Ioverno, E., Iurato, R., La Pace, L., La Piscopia, C., La Selva, R., Lafratta, M., Lamparelli, M., Lanaro, G., Lancerotto, R., Larcher, M., Lassandro, M., Lattuada, G., Laurino, P., Lefons, C., Legrottaglie, F., Lemma, A., Leone, D., Leone, F., Leso, A., Leuzzi, G., Levato, G., Libardi, L., Libralesso, N., Licini, P. I., Licursi, G., Lidonnici, F., Lillo, C., Liveri, L., Livio, A., Loiero, R. A., Loison, M., Lombardo, G., Lombardo, T., Lomunno, V., Lomuscio, S., Lonedo, A., Longo, E., Longoni, P., Lora, L., Lotterio, A., Lucatello, L., Luongo, A., Lupoli, M., Macchia, C., Macri, G., Mafessanti, M., Maggialetti, V., Maggioni, A., Magnani, M., Maiellaro, G., Mancuso, A., Maniglio, A. R., Mannari, G. L., Manni, A., Manocchio, B., Mao, M., Maranò, A., Maraone, E., Marascio, D., Marcheselli, P., Marchetto, B., Marchetto, S., Marchi, A., Marchi, G. L., Mariano, C., Marinacci, S., Marinelli, S., Marini, G., Marra, V. C., Marrali, F., Marseglia, C., Martello, G., Martino, C., Martino, G., Martino, M., Marulli, C. F., Maruzzi, G., Marzotti, A., Mascheroni, G., Mascolo, P., Masoch, G., Masone, R., Massa, E., Massa, L., Massafra, M., Massi, M., Massignani, D. M., Matarese, A. M., Matini, G., Mauro, R., Mazzi, M., Mazzillo, A., Mazzocato, E., Mazzoleni, N. S., Mazzone, A., Melacci, A., Mele, E., Meliota, P., Menaspà, S., Meneghello, F., Merola, G., Merone, L., Metrucci, A., Mezzina, V., Micchi, A., Michielon, A., Migliore, N., Minero, G., Minotta, F., Mirandola, C., Mistrorigo, S., Modafferi, L., Moitre, R., Mola, E., Monachese, C., Mongiardini, C., Montagna, F., Montani, M., Montemurno, I., Montolli, R., Montorsi, S., Montresor, M., Monzani, M. G., Morabito, F., Mori, G., Moro, A., Mosca, M. F., Motti, F., Muddolon, L., Mugnai, M., Muscas, F., Naimoli, F., Nanci, G., Nargi, E., Nasorri, R., Nastrini, G., Negossi, M., Negrini, A., Negroni, A., Neola, V., Niccolini, F., Niro, C. M., Nosengo, C., Novella, G., Nuti, C., Obici, F., Olita, C., Oliverio, S. S., Olivieri, I., Oriente, S., Orlando, G., Paci, C., Pagano, G., Pagliara, C., Paita, G., Paladini, G., Paladino, G., Palano, T., Palatella, A., Palermo, P., Palmisano, M., Pando, P., Panessa, P., Panigo, F., Panozzo, G., Panvini, F., Panzieri, F., Panzino, A., Panzitta, F., Paoli, N., Papagna, R., Papaleo, M. G., Papalia, G., Parisi, R., Parotti, N., Parravicini, D., Passarella, P., Pastore, G. A., Patafio, M., Pavone, P., Pedroli, W., Pedroni, M., Pelligra, G., Pellizzari, M., Penati, A., Perlot, M., Perrone, A., Perrone, G., Peruzzi, P., Peselli, C., Petracchini, L., Petrera, L., Petrone, S., Peverelli, C., Pianorsi, F., Piazza, G. P., Piazzolla, G., Picci, A., Pienabarca, G., Pietronigro, T. P., Pignocchino, P., Pilone, R., Pinto, D., Pirovano, E., Pirrotta, D., Pisante, V., Pitotto, P., Pittari, L., Piva, A., Pizzoglio, A., Plantera, O. R., Plebani, W., Plessi, S., Podrecca, D., Poerio, V., Poggiani, F., Pogliani, W., Poli, L., Poloni, F. G., Porcelli, R., Porto, S., Pranzo, L., Prevedello, C., Profeta, C., Profico, D., Punzi, A., Quaglia, G. M., Racano, M., Raccone, A., Radice, F., Raho, C. A., Raimondi, R., Rainò, M., Ramponi, R., Ramunni, A., Ramunni, A. L., Ravasio, F., Ravera, M., Re Sartò, G., Rebustello, G., Regazzoli, S., Restelli, C., Rezzonico, M., Ricchiuto, F., Rigo, S., Rigon, G., Rigon, R., Rinaldi, O. V., Rinaldi, M., Risplendente, P. G., Rispoli, M., Riundi, R., Riva, M. G., Rizzi, A. L., Rizzi, D., Rizzo, L. D., Rocchi, L., Rondinone, B., Rosa, B., Rosati, F., Roselli, F., Rossetti, A., Rossetti, C., Rossi, R., Rossi, P. R., Rossi, A., Rossi, C. L., Rossitto, A., Ruffini, R., Ruffo, A., Ruggio, S., Ruo, M., Russo, B., Russo, L., Russo, R., Russo, S., Russo, U., Russo, V., Ruta, G., Sacchi, F., Sacco Botto, F., Saia, A., Salladini, G., Salmoiraghi, S., Saluzzo, F., Salvatore, C., Salvatori, E., Salvio, G., Sandri, P., Sandrini, T., Sangermano, V., Santoni, N., Saracino, A. D., Saracino, A., Sarasin, P., Sardo Infirri, C., Sarrì, B., Sartori, G., Sartori, N., Sauro, C., Scaglioni, M., Scalfi, C., Scamardella, A. M., Scandale, G., Scandone, L., Scannavini, G., Scarati, R., Scardi, A., Scarpa, F. M., Scazzi, P., Schifone, A., Schiroso, G., Scigliano, G., Scilla, A., Sciortino, M., Scolaro, G., Scollo, E., Scorretti, G., Sellitti, R., Selmo, A., Selvaggio, G., Sempio, A., Seren, F., Serio, L., Serra, C., Serra, L., Siciliano, D., Sideri, A., Sighele, M., Signore, R., Siliberto, F., Silvestro, M., Simioni, G., Simmini, G., Simonato, L., Sinchetto, F., Sizzano, E., Smajato, G., Smaldone, M., Sola, G., Sordillo, L., Sovran, C. S., Spagnul, P., Spanò, F., Sproviero, S., Squintani, A., Stella, L., Stilo, V., Stocchiero, B., Stornello, M. C., Stracka, G., Strada, S., Stranieri, G., Stucci, N., Stufano, N., Suppa, A., Susca, V. G., Sutti, M., Taddei, M., Tagliabue, E., Tagliente, G., Talato, F., Talerico, P., Talia, R., Taranto, R., Tartaglia, M., Tauro, N., Tedesco, A., Tieri, P., Tirelli, M., Tocci, L., Todesco, P., Tognolo, M., Tomba, A., Tonello, P., Tonon, R., Toscano, L., Tosi, A., Tosi, G., Toso, S., Travaglio, P., Tremul, L., Tresso, C., Triacchini, P., Triggiano, L., Trigilio, A., Trimeloni, J., Tripicchio, G., Tritto, G. S., Trono, F., Trotta, E., Trotta, G., Tubertini, A., Turri, C., Turri, L., Tuttolani, M. P., Urago, M., Ursini, G., Valcanover, F., Valente, L., Valenti, M., Valentini, F., Vallone, G., Valz, P., Valzano, L., Vanin, V., Vatteroni, M., Vegetti, L., Vendrame, D., Veramonti, I., Veronelli, G., Vesco, A., Vicariotto, G., Vignale, G., Villa, P. L., Vinciguerra, R., Visco, A., Visentin, G., Visonà, E., Vitali, E., Vitali, S., Vitti, F., Volpone, D. A., Zambon, N., Zammarrelli, A., Zanaboni, A., Zane, D., Zanetti, B., Zanibellato, R., Zappetti, M., Zappone, P., Zerilli, G., Zirino, V., Zoccali, R., Zuin, F., Altomonte, M., Anelli, N., Angiò, F., Annale, P., Antonacci, S., Anzilotta, R., Bano, F., Basadonna, O., Beduschi, L., Becagli, P., Bellotti, G., Blotta, C., Bruno, G., Cappuccini, A., Caramatti, S., Cariolato, M. P., Castellana, M., Castellani, L., Catania, R., Chielli, A., Chinellato, A., Ciaccia, A., Clerici, E., Cocci, A., Costanzo, G., D’Ercole, F., De Stefano, G., Decè, F., Di Cicco, N., Di Marco, A., Donati Sarti, C., Draghi, E., Dusi, G., Esposito, V., Ferraro, L., Ferretti, A., Ferri, E., Foggetti, L., Foglia, A., Fonzi, E., Frau, G., Fuoco, M. R., Furci, G., Gallo, L., Garra, V., Giannini, A., Gris, A., Iacovino, R., Interrigi, R., Joppi, R., Laner, B., La Fortezza, G., La Padula, A., Lista, M. R., Lupi, G., Maffei, D., Maggioni, G., Magnani, L., Marrazzo, E., Marcon, L., Marinò, V., Maroni, A., Martinelli, C., Mastandrea, E., Mastropierro, F., Meo, A. T., Mero, P., Minesso, E., Moschetta, V., Mosele, E., Nanni, C., Negretti, A., Nisticò, C., Orsini, A., Osti, M., Pacilli, M. C., Pennestre, C., Picerno, G., Piol, K., Pivano, L., Pizzuti, E., Poggi, L., Poidomani, I., Pozzetto, M., Presti, M. L., Ravani, R., Recalenda, V., Romagnuolo, F., Rossignoli, S., Rossin, E., Sabatella, C., Sacco, F., Sanità, F., Sansone, E., Servadei, F., Sisto, M. T., Sorio, A., Sorrentino, A., Spinelli, E., Spolaor, A., Squillacioti, A., Stella, P., Talerico, A., Todisco, C., Vadino, M., Zuliani, C., and Risk & Prevention Collaborative Group

Published
2017
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6. Engaging the Body, Appropriating a Corporate Wellness Programme

Author

Bruni, E, Andrei, F, Tirabeni, L, Bruni, EA, Bruni, E, Andrei, F, Tirabeni, L, and Bruni, EA

Abstract

Purpose –The purpose of this contribution is twofold: at the empirical level, it is shown how in the relationship that subjects are encouraged to construct with their bodies major implications for workers’ well-being can be found; at a theoretical level, attention is drawn to the importance of framing the different practices workers may display towards digital wellness programmes not just in terms of acceptance or resistance, but also in terms of appropriation. Design/methodology/approach – Empirically, this study concentrates on the pilot study conducted by a large manufacturing firm that decided to implement a digitally assisted corporate wellness programme. The experimentation involves a sample of the company’s workers. The 24 participants were interviewed at the beginning, during the programme and at its end, for a total of 69 interviews. Interviews were transcribed and analysed through a template analysis. Findings – This research emphasizes how workers’ well-being manifests in the relationship subjects are fostered to construct with their body and, in parallel, how workers may play an active and unpredictable role in corporate wellness programmes. Originality/value – Differently from the current literature that frames workers’ reactions towards digital corporate well-being initiatives in mainly polarized ways, this contribution leads to a less dichotomic and more nuanced interpretation of the “impacts” wellness programmes may have, showing how workers may display practices not just of acceptance or resistance, but also of appropriation.

Published
2022

7. AB0118 PORPHYROMONAS GINGIVALIS AMOUNT IN THE TONGUE BIOFILM IS ASSOCIATED WITH EROSIVE ARTHRITIS IN SYSTEMIC LUPUS ERYTHEMATOSUS

Author

Ceccarelli, F., primary, Saccucci, M., additional, Natalucci, F., additional, Olivieri, G., additional, Bruni, E., additional, Iacono, R., additional, Colasanti, T., additional, Di Carlo, G., additional, Alessandri, C., additional, Uccelletti, D., additional, Russo, P., additional, Pilloni, A., additional, Conti, F., additional, and Polimeni, A., additional

Published
2022
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9. Professioni educative e pedagogiche

Author

Bruni, E. M., Cerrocchi, L., and Palmieri, C.

Subjects
Conflitto/i, Formazione iniziale e in servizio, Analisi e messa a punto del setting educativo, Pedagogia generale e sociale, Professioni educative e pedagogiche, Conflitto/i, Analisi e messa a punto del setting educativo, Pedagogia generale e sociale, Formazione iniziale e in servizio, Professioni educative e pedagogiche
Published
2021

10. Language Modelling as a Multi-Task Problem

Author

Weber, L., Jumelet, J., Bruni, E., Hupkes, D., Merlo, P., Tiedemann, J., Tsarfaty, R., ILLC (FNWI), Language and Computation (ILLC, FNWI/FGw), Logic and Language (ILLC, FNWI/FGw), and IvI Research (FNWI)

Subjects
Cognitive science, FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Computation and Language, Computer science, Principles of learning, Theoretical linguistics, Language modelling, Language model, Computation and Language (cs.CL), Task (project management), Interpretability, Machine Learning (cs.LG)
Abstract

In this paper, we propose to study language modelling as a multi-task problem, bringing together three strands of research: multi-task learning, linguistics, and interpretability. Based on hypotheses derived from linguistic theory, we investigate whether language models adhere to learning principles of multi-task learning during training. To showcase the idea, we analyse the generalisation behaviour of language models as they learn the linguistic concept of Negative Polarity Items (NPIs). Our experiments demonstrate that a multi-task setting naturally emerges within the objective of the more general task of language modelling.We argue that this insight is valuable for multi-task learning, linguistics and interpretability research and can lead to exciting new findings in all three domains., Accepted for publication at EACL 2021

Published
2021

11. Professioni educative e pedagogiche

Author

Polenghi, S., Cereda, F, Zini, P, Palmieri, C, Bruni, E, Cerrocchi, L, Cerrocchi, L., Polenghi, S., Cereda, F, Zini, P, Palmieri, C, Bruni, E, Cerrocchi, L, and Cerrocchi, L.

Abstract

Il saggio fa il punto sullo "stato dell'arte" delle professioni educative e pedagogiche in Italia, alla luce dei cambiamenti di tipo normativo formativo e sociale (non ultima la pandemia) intercorsi negli ultimi anni. In particolare, sottolinea la complessità del ruolo che le professioni educative e pedagogiche svolgono nei contesti educativi e sociali, correlandola alle competenze specifiche che tali professioni esercitano ed evidenziandone la conseguente responsabilità educativa., The essay takes stock of the "state of the art" of the educational and pedagogical professions in Italy, in light of the changes in educational and social legislation (not least the pandemic) that have taken place in recent years. In particular, it underlines the complexity of the role that educational and pedagogical professions play in educational and social contexts, correlating it with the specific skills that these professions exercise and highlighting their consequent educational responsibility.

Published
2021

12. The intricacies of power relations and digital technologies in organizational processes

Author

Bruni, E, Miele, F, Pittino, D, Tirabeni, L, Bruni, EA, Bruni, E, Miele, F, Pittino, D, Tirabeni, L, and Bruni, EA

Abstract

The relationship between power, technology and organizing is a longstanding theme in organization studies, typically articulated along two polarized positions: a pessimistic and an optimistic one. Both positions assume a deterministic view in which technology “impacts” society and organizations, thus missing the intricate and often ambiguous dynamics that surround power and technology. Accordingly, this Special Issue focuses on the intricacies of power, digital technologies and organizational processes. Presenting the rationale of the papers that compose the Special Issue, we suggest five themes arising when empirically and theoretically approaching these intricacies: 1) digital technologies and power relationships in organizational structure and processes; 2) relationships between technology, power and workers’ participation; 3) digital technologies, algorithmic control and power renegotiation; 4) digital technologies, practices of human resources management and the joint design of technology, work, and organization; 5) hyper-industrialization as a critical lens to approach technology, work, and organizing. Taken all together, the papers help overcoming simplifications as well as polarized representations of the relationship between power, digital technologies and organizing.

Published
2021

13. Can N2 O emissions offset the benefits from soil organic carbon storage?

Author

Guenet, B., Gabrielle, B., Chenu, C., Arrouays, D., Balesdent, J., Bernoux, M., Bruni, E, Caliman, J.-P., Cardinael, R., Chen, S., Ciais, P., Desbois, D., Fouche, J., Frank, S., Henault, C., Lugato, E., Naipal, V., Nesme, T., Obersteiner, M., Pellerin, S., Powlson, D.S., Rasse, D., Rees, F., Soussana, J.-F., Su, Y., Tian, H., Valin, H., Zhou, F., Guenet, B., Gabrielle, B., Chenu, C., Arrouays, D., Balesdent, J., Bernoux, M., Bruni, E, Caliman, J.-P., Cardinael, R., Chen, S., Ciais, P., Desbois, D., Fouche, J., Frank, S., Henault, C., Lugato, E., Naipal, V., Nesme, T., Obersteiner, M., Pellerin, S., Powlson, D.S., Rasse, D., Rees, F., Soussana, J.-F., Su, Y., Tian, H., Valin, H., and Zhou, F.

Abstract

To respect the Paris agreement targeting a limitation of global warming below 2°C by 2100, and possibly below 1.5 °C, drastic reductions of greenhouse gas emissions are mandatory but not sufficient. Large-scale deployment of other climate mitigation strategies are also necessary. Among these, increasing soil organic carbon (SOC) stocks is an important lever because carbon in soils can be stored for long periods and land management options to achieve this already exist and have been widely tested. However, agricultural soils are also an important source of nitrous oxide (N2 O), a powerful greenhouse gas, and increasing SOC may influence N2 O emissions, likely causing an increase in many cases, thus tending to offset the climate change benefit from increased SOC storage. Here, we review the main agricultural management options for increasing SOC stocks. We evaluate the amount of SOC that can be stored as well as resulting changes in N2 O emissions to better estimate the climate benefits of these management options. Based on quantitative data obtained from published meta-analyses and from our current level of understanding, we conclude that the climate mitigation induced by increased SOC storage is generally overestimated if associated N2 O emissions are not considered but, with the exception of reduced tillage, is never fully offset. Some options (e.g, biochar or non-pyrogenic C amendment application) may even decrease N2 O emissions.

Published
2021

16. The Fast and the Flexible: training neural networks to learn to follow instructions from small data

Author

Leonandya, R., Hupkes, D., Bruni, E., Kruszewski, G., Dobnik, S., Chatzikyriakidis, S., Demberg, V., ILLC (FNWI), Language and Computation (ILLC, FNWI/FGw), Brain and Cognition, Logic and Language (ILLC, FNWI/FGw), and Faculty of Science

Subjects
Structure (mathematical logic), FOS: Computer and information sciences, Vocabulary, Computer Science - Computation and Language, Small data, Artificial neural network, Inductive bias, business.industry, Computer science, media_common.quotation_subject, Contrast (statistics), Machine learning, computer.software_genre, Task (project management), Offline learning, Artificial intelligence, business, computer, Computation and Language (cs.CL), media_common
Abstract

Learning to follow human instructions is a long-pursued goal in artificial intelligence. The task becomes particularly challenging if no prior knowledge of the employed language is assumed while relying only on a handful of examples to learn from. Work in the past has relied on hand-coded components or manually engineered features to provide strong inductive biases that make learning in such situations possible. In contrast, here we seek to establish whether this knowledge can be acquired automatically by a neural network system through a two phase training procedure: A (slow) offline learning stage where the network learns about the general structure of the task and a (fast) online adaptation phase where the network learns the language of a new given speaker. Controlled experiments show that when the network is exposed to familiar instructions but containing novel words, the model adapts very efficiently to the new vocabulary. Moreover, even for human speakers whose language usage can depart significantly from our artificial training language, our network can still make use of its automatically acquired inductive bias to learn to follow instructions more effectively.

Published
2019

17. Assessing incrementality in sequence-to-sequence models

Author

Ulmer, D., Hupkes, D., Bruni, E., Augenstein, I., Gella, S., Ruder, S., Kann, K., Can, B., Welbl, J., Conneau, A., Ren, X., Rei, M., ILLC (FNWI), Language and Computation (ILLC, FNWI/FGw), and Brain and Cognition

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, Computer science, Process (engineering), 010501 environmental sciences, computer.software_genre, 01 natural sciences, 050105 experimental psychology, Machine Learning (cs.LG), 0501 psychology and cognitive sciences, 0105 earth and related environmental sciences, Sequence, Computer Science - Computation and Language, Point (typography), business.industry, Mechanism (biology), 05 social sciences, Contrast (statistics), Cognition, Key (cryptography), Artificial intelligence, Computational linguistics, business, computer, Computation and Language (cs.CL), Natural language processing
Abstract

Since their inception, encoder-decoder models have successfully been applied to a wide array of problems in computational linguistics. The most recent successes are predominantly due to the use of different variations of attention mechanisms, but their cognitive plausibility is questionable. In particular, because past representations can be revisited at any point in time, attention-centric methods seem to lack an incentive to build up incrementally more informative representations of incoming sentences. This way of processing stands in stark contrast with the way in which humans are believed to process language: continuously and rapidly integrating new information as it is encountered. In this work, we propose three novel metrics to assess the behavior of RNNs with and without an attention mechanism and identify key differences in the way the different model types process sentences., Comment: Accepted at Repl4NLP, ACL

Published
2019
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18. On the Realization of Compositionality in Neural Networks

Author

Baan, J., Leible, J., Nikolaus, M., Rau, D., Ulmer, D., Baumgärtner, T., Hupkes, D., Bruni, E., Linzen, T., Chrupała, G., Belinkov, Y., ILLC (FNWI), Language and Computation (ILLC, FNWI/FGw), Faculty of Science, and Brain and Cognition

Subjects
Power graph analysis, FOS: Computer and information sciences, Computer Science - Machine Learning, Sequence, Computer Science - Computation and Language, Theoretical computer science, Artificial neural network, Computer Science - Artificial Intelligence, Computer science, Principle of compositionality, Inference, 02 engineering and technology, 010501 environmental sciences, 01 natural sciences, Task (project management), Machine Learning (cs.LG), Artificial Intelligence (cs.AI), Component (UML), 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Computation and Language (cs.CL), 0105 earth and related environmental sciences
Abstract

We present a detailed comparison of two types of sequence to sequence models trained to conduct a compositional task. The models are architecturally identical at inference time, but differ in the way that they are trained: our baseline model is trained with a task-success signal only, while the other model receives additional supervision on its attention mechanism (Attentive Guidance), which has shown to be an effective method for encouraging more compositional solutions (Hupkes et al.,2019). We first confirm that the models with attentive guidance indeed infer more compositional solutions than the baseline, by training them on the lookup table task presented by Li\v{s}ka et al. (2019). We then do an in-depth analysis of the structural differences between the two model types, focusing in particular on the organisation of the parameter space and the hidden layer activations and find noticeable differences in both these aspects. Guided networks focus more on the components of the input rather than the sequence as a whole and develop small functional groups of neurons with specific purposes that use their gates more selectively. Results from parameter heat maps, component swapping and graph analysis also indicate that guided networks exhibit a more modular structure with a small number of specialized, strongly connected neurons., Comment: To appear at BlackboxNLP 2019, ACL

Published
2019
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19. Beyond Task Success: A Closer Look at Jointly Learning to See, Ask, and GuessWhat

Author

Shekhar, R., Venkatesh, A., Baumgärtner, T., Bruni, E., Plank, B., Bernardi, R., Fernández, R., Burstein, J., Doran, C., Solorio, T., ILLC (FNWI), Language and Computation (ILLC, FNWI/FGw), and Brain and Cognition

Abstract

We propose a grounded dialogue state encoder which addresses a foundational issue on how to integrate visual grounding with dialogue system components. As a test-bed, we focus on the GuessWhat?! game, a two-player game where the goal is to identify an object in a complex visual scene by asking a sequence of yes/no questions. Our visually-grounded encoder leverages synergies between guessing and asking questions, as it is trained jointly using multi-task learning. We further enrich our model via a cooperative learning regime. We show that the introduction of both the joint architecture and cooperative learning lead to accuracy improvements over the baseline system. We compare our approach to an alternative system which extends the baseline with reinforcement learning. Our in-depth analysis shows that the linguistic skills of the two models differ dramatically, despite approaching comparable performance levels. This points at the importance of analyzing the linguistic output of competing systems beyond numeric comparison solely based on task success.

Published
2019

20. Adding object detection skills to visual dialogue agents

Author

Bani, G., Belli, D., Dagan, G., Geenen, A., Skliar, A., Venkatesh, A., Baumgärtner, T., Bruni, E., Fernández, R., Leal-Taixé, L., Roth, S., ILLC (FNWI), and Language and Computation (ILLC, FNWI/FGw)

Subjects
business.industry, Computer science, media_common.quotation_subject, 05 social sciences, 010501 environmental sciences, Object (computer science), 01 natural sciences, Object detection, 0502 economics and business, Contrast (vision), Computer vision, Artificial intelligence, 050207 economics, business, 0105 earth and related environmental sciences, media_common
Abstract

Our goal is to equip a dialogue agent that asks questions about a visual scene with object detection skills. We take the first steps in this direction within the GuessWhat?! game. We use Mask R-CNN object features as a replacement for ground-truth annotations in the Guesser module, achieving an accuracy of 57.92%. This proves that our system is a viable alternative to the original Guesser, which achieves an accuracy of 62.77% using ground-truth annotations, and thus should be considered an upper bound for our automated system. Crucially, we show that our system exploits the Mask R-CNN object features, in contrast to the original Guesser augmented with global, VGG features. Furthermore, by automating the object detection in GuessWhat?!, we open up a spectrum of opportunities, such as playing the game with new, non-annotated images and using the more granular visual features to condition the other modules of the game architecture.

Published
2019

21. Transcoding compositionally: using attention to find more generalizable solutions

Author

Korrel, K., Hupkes, D., Dankers, V., Bruni, E., Linzen, T., Chrupała, G., Belinkov, Y., ILLC (FNWI), Language and Computation (ILLC, FNWI/FGw), Faculty of Science, Brain and Cognition, and Logic and Language (ILLC, FNWI/FGw)

Subjects
FOS: Computer and information sciences, Theoretical computer science, Computer Science - Computation and Language, Computer science, Generalization, Principle of compositionality, Computer Science - Artificial Intelligence, 02 engineering and technology, Transcoding, Construct (python library), 010501 environmental sciences, computer.software_genre, 01 natural sciences, Artificial Intelligence (cs.AI), 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, computer, Computation and Language (cs.CL), Natural language, 0105 earth and related environmental sciences
Abstract

While sequence-to-sequence models have shown remarkable generalization power across several natural language tasks, their construct of solutions are argued to be less compositional than human-like generalization. In this paper, we present seq2attn, a new architecture that is specifically designed to exploit attention to find compositional patterns in the input. In seq2attn, the two standard components of an encoder-decoder model are connected via a transcoder, that modulates the information flow between them. We show that seq2attn can successfully generalize, without requiring any additional supervision, on two tasks which are specifically constructed to challenge the compositional skills of neural networks. The solutions found by the model are highly interpretable, allowing easy analysis of both the types of solutions that are found and potential causes for mistakes. We exploit this opportunity to introduce a new paradigm to test compositionality that studies the extent to which a model overgeneralizes when confronted with exceptions. We show that seq2attn exhibits such overgeneralization to a larger degree than a standard sequence-to-sequence model., Comment: to appear at BlackboxNLP 2019, ACL

Published
2019
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22. The PhotoBook Dataset: Building Common Ground through Visually-Grounded Dialogue

Author

Haber, J., Baumgärtner, T., Takmaz, E., Gelderloos, L., Bruni, E., Fernández, R., Korhonen, A., Traum, D., Màrquez, L., Language and Computation (ILLC, FNWI/FGw), ILLC (FNWI), Brain and Cognition, Logic and Language (ILLC, FNWI/FGw), Faculty of Science, and Cognitive Science & AI

Subjects
FOS: Computer and information sciences, Computer Science - Computation and Language, Information retrieval, Computer Science - Artificial Intelligence, Computer science, Computer Vision and Pattern Recognition (cs.CV), media_common.quotation_subject, 05 social sciences, Computer Science - Computer Vision and Pattern Recognition, Common ground, Context (language use), 02 engineering and technology, Resolution (logic), 050105 experimental psychology, Task (project management), Artificial Intelligence (cs.AI), 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, 0501 psychology and cognitive sciences, Conversation, Computation and Language (cs.CL), media_common
Abstract

This paper introduces the PhotoBook dataset, a large-scale collection of visually-grounded, task-oriented dialogues in English designed to investigate shared dialogue history accumulating during conversation. Taking inspiration from seminal work on dialogue analysis, we propose a data-collection task formulated as a collaborative game prompting two online participants to refer to images utilising both their visual context as well as previously established referring expressions. We provide a detailed description of the task setup and a thorough analysis of the 2,500 dialogues collected. To further illustrate the novel features of the dataset, we propose a baseline model for reference resolution which uses a simple method to take into account shared information accumulated in a reference chain. Our results show that this information is particularly important to resolve later descriptions and underline the need to develop more sophisticated models of common ground in dialogue interaction., Updates 26-06-2019: Changed caption sizes to comply with the ACL style guidelines and corrected some references

Published
2019

28. Ask No More: Deciding when to guess in referential visual dialogue

Author

Shekhar, R., Baumgärtner, T., Venkatesh, A., Bruni, E., Bernardi, R., Fernandez, R., Bender, E.M., Derczynski, L., Isabelle, P., ILLC (FNWI), and Language and Computation (ILLC, FNWI/FGw)

Abstract

Our goal is to explore how the abilities brought in by a dialogue manager can be included in end-to-end visually grounded conversational agents. We make initial steps towards this general goal by augmenting a task-oriented visual dialogue model with a decision-making component that decides whether to ask a follow-up question to identify a target referent in an image, or to stop the conversation to make a guess. Our analyses show that adding a decision making component produces dialogues that are less repetitive and that include fewer unnecessary questions, thus potentially leading to more efficient and less unnatural interactions.

Published
2018

29. Biomodulatory Treatment With Azacitidine, All-trans Retinoic Acid and Pioglitazone Induces Differentiation of Primary AML Blasts Into Neutrophil Like Cells Capable of ROS Production and Phagocytosis

Author

Klobuch, S, Steinberg, T, Bruni, E, Mirbeth, C, Heilmeier, B, Ghibelli, L, Herr, W, Reichle, A, and Thomas, S

Subjects
all-trans retinoic acid, azacitidine, ddc:610, azacitidine, all-trans retinoic acid, pioglitazone, acute myeloid leukemia, differentiation, hemic and lymphatic diseases, acute myeloid leukemia, differentiation, pioglitazone, Settore BIO/13, 610 Medizin, neoplasms
Abstract

Effective and tolerable salvage therapies for elderly patients with chemorefractory acute myeloid leukemia (AML) are limited and usually do not change the poor clinical outcome. We recently described in several chemorefractory elderly AML patients that a novel biomodulatory treatment regimen consisting of low-dose azacitidine (AZA) in combination with PPAR gamma agonist pioglitazone (PGZ) and all-trans retinoic acid (ATRA) induced complete remission of leukemia and also triggered myeloid differentiation with rapid increase of peripheral blood neutrophils. Herein, we further investigated our observations and comprehensively analyzed cell differentiation in primary AML blasts after treatment with ATRA, AZA, and PGZ ex vivo. The drug combination was found to significantly inhibit cell growth as well as to induce cell differentiation in about half of primary AML blasts samples independent of leukemia subtype. Notably and in comparison to ATRA/AZA/PGZ triple-treatment, effects on cell growth and myeloid differentiation with ATRA monotherapy was much less efficient. Morphological signs of myeloid cell differentiation were further confirmed on a functional basis by demonstrating increased production of reactive oxygen species as well as enhanced phagocytic activity in AML blasts treated with ATRA/AZA/PGZ. In conclusion, we show that biomodulatory treatment with ATRA/AZA/PGZ can induce phenotypical and functional differentiation of primary AML blasts into neutrophil like cells, which aside from its antileukemic activity may lower neutropenia associated infection rates in elderly AML patients in vivo. Clinical impact of the ATRA/AZA/PGZ treatment regimen is currently further investigated in a randomized clinical trial in chemorefractory AML patients (NCT02942758).

Published
2018
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30. Beyond task success: A closer look at jointly learning to see, ask, and GuessWhat

Author

Ravi Shekhar, Venkatesh, A., Baumgärtner, T., Bruni, E., Plank, B., Bernardi, R., and Fernández, R.

Subjects
FOS: Computer and information sciences, Computer Science - Computation and Language, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, Computation and Language (cs.CL)
Abstract

We propose a grounded dialogue state encoder which addresses a foundational issue on how to integrate visual grounding with dialogue system components. As a test-bed, we focus on the GuessWhat?! game, a two-player game where the goal is to identify an object in a complex visual scene by asking a sequence of yes/no questions. Our visually-grounded encoder leverages synergies between guessing and asking questions, as it is trained jointly using multi-task learning. We further enrich our model via a cooperative learning regime. We show that the introduction of both the joint architecture and cooperative learning lead to accuracy improvements over the baseline system. We compare our approach to an alternative system which extends the baseline with reinforcement learning. Our in-depth analysis shows that the linguistic skills of the two models differ dramatically, despite approaching comparable performance levels. This points at the importance of analyzing the linguistic output of competing systems beyond numeric comparison solely based on task success., Comment: Accepted to NAACL 2019

Published
2018
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31. Adversarial evaluation for open-domain dialogue generation

Author

Bruni, E., Fernández, R., Jokinen, K., Stede, M., DeVault, D., Louis, A., ILLC (FNWI), and Language and Computation (ILLC, FNWI/FGw)

Subjects
business.industry, Computer science, 02 engineering and technology, 010501 environmental sciences, Machine learning, computer.software_genre, 01 natural sciences, Task (project management), Adversarial system, Discriminative model, Evaluation methods, 0202 electrical engineering, electronic engineering, information engineering, Open domain, 020201 artificial intelligence & image processing, Artificial intelligence, business, computer, 0105 earth and related environmental sciences
Abstract

We investigate the potential of adversarial evaluation methods for open-domain dialogue generation systems, comparing the performance of a discriminative agent to that of humans on the same task. Our results show that the task is hard, both for automated models and humans, but that a discriminative agent can learn patterns that lead to above-chance performance.

Published
2017

32. Gland segmentation in colon histology images: The GlaS challenge contest

Author

Sirinukunwattana, K, Pluim, JPW, Chen, H, Qi, X, Heng, P-A, Guo, YB, Wang, LY, Matuszewski, B, Bruni, E, Sanchez, U, et al., Sirinukunwattana, K, Pluim, JPW, Chen, H, Qi, X, Heng, P-A, Guo, YB, Wang, LY, Matuszewski, B, Bruni, E, Sanchez, U, and et al.

Abstract

Colorectal adenocarcinoma originating in intestinal glandular structures is the most common form of colon cancer. In clinical practice, the morphology of intestinal glands, including architectural appearance and glandular formation, is used by pathologists to inform prognosis and plan the treatment of individual patients. However, achieving good inter-observer as well as intra-observer reproducibility of cancer grading is still a major challenge in modern pathology. An automated approach which quantifies the morphology of glands is a solution to the problem. This paper provides an overview to the Gland Segmentation in Colon Histology Images Challenge Contest (GlaS) held at MICCAI’2015. Details of the challenge, including organization, dataset and evaluation criteria, are presented, along with the method descriptions and evaluation results from the top performing methods.

Published
2017

33. Indifferenza religiosa contemporanea: per un superamento attraverso l’educazione estetica

Author

Augelli, A Bortolotto, M Bruni, E M Caputo, M Castaldi, MC Cavana, L Cima, R Dal Toso, P Guetta, S Lascioli, A Loro, D Mari, G Moscato, MT Musaio, M Naccari, A Nanni, C Pinelli, G Porcarelli, A Secchi, P Tempesta, M Triani, P, Dal Toso, P Loro, D, Musaio, Marisa, Musaio, M (ORCID:0000-0003-1555-7314), Augelli, A Bortolotto, M Bruni, E M Caputo, M Castaldi, MC Cavana, L Cima, R Dal Toso, P Guetta, S Lascioli, A Loro, D Mari, G Moscato, MT Musaio, M Naccari, A Nanni, C Pinelli, G Porcarelli, A Secchi, P Tempesta, M Triani, P, Dal Toso, P Loro, D, Musaio, Marisa, and Musaio, M (ORCID:0000-0003-1555-7314)

Abstract

Il contributo si propone di rintracciare alcune possibili implicazioni tra esperienza estetica ed esperienza religiosa, a fronte di un contesto contemporaneo incline a forme di indifferenza che riversano le loro conseguenze sul modo di vivere la spiritualità., The contribution aims to trace some possible implications between aesthetic experience and religious experience, in the face of a contemporary context in which the forms of indifference pour their consequences on the way of living spirituality.

Published
2017

35. Are all people with diabetes and cardiovascular risk factors or microvascular complications at very high risk? Findings from the Risk and Prevention Study

Author

Marzona, I., Avanzini, F., Lucisano, G., Tettamanti, M., Baviera, M., Nicolucci, A., Roncaglioni, M. C., Tombesi, M., Tognoni, G., Massa, E., Marrocco, W., Micalella, M., Caimi, V., Longoni, P., Franzosi, M. G., Monesi, L., Pangrazzi, I., Barlera, S., Milani, V., Nicolis, E., Casola, C., Clerici, F., Palumbo, A., Sgaroni, G., Marchioli, R., Silletta, M. G., Pioggiarella, R., Scarano, M., Marfisi, R. M., Flamminio, A., Macino, L., Ferri, B., Pera, C., Polidoro, A., Abbatino, D., Acquati, M., Addorisio, G., Adinolfi, D., Adreani, L., Agistri, M. R., Agneta, A., Agnolio, M. L., Agostini, N., Agostino, G., Airo, A., Alaimo, N., Albano, M., Albano, N., Alecci, G., Alemanno, S., Alexanian, A., Alfarano, M., Alfe, L., Alonzo, N., Alvino, S., Ancora, A., Andiloro, S., Andreatta, E., Angeli, S., Angiari, F., Angilletti, V., Annicchiarico, C., Anzivino, M., Aprea, R., Aprile, A., Aprile, E., Aprile, I., Aprile, L., Armellani, V., Arnetoli, M., Aronica, A., Autiero, V., Bacca, G., Baccalaro, A. M., Bacci, M., Baglio, G., Bagnani, M., Baiano, A., Baldari, A., Ballarini, L., Banchi, G., Bandera, R., Bandini, F., Baratella, M., Barbieri, A., Barbieri Vita, A., Bardi, M., Barlocchi, M., Baron, P., Bartoli, M., Basile, A., Basile, F., Basile, S., Battaggia, A., Battaglia, A., Bau, A., Beconcini, G., Beggio, R., Belfiore, P. A., Belicchi, M., Bellamoli, S., Bellini, C., Bellomo, M., Benetollo, C., Benetti, R., Beretta, E., Bertalero, P., Bertaso, F. G., Bertolani, U., Bettelli, G., Biagiotti, G., Bianchi, S., Bianco, G., Biccari, F., Bigioli, F., Bindi, M., Bisanti, G., Bitetti, E. M., Blasetti, M. P., Blesi, F., Boato, V., Boga, S., Boidi, E., Boldrin, G., Bollati, A., Bolzan, L., Bolzonella, S., Bonardi, P., Bonato, G. B., Bonci, M., Bonfitto, G., Bonincontro, E., Boninsegna, F., Bonissone, D., Bono, L., Bonollo, E., Borghi, M., Borioli, N., Borsatto, M., Bosco, T., Bosisio Pioltelli, M., Botarelli, C., Botassis, S., Bottini, F., Bottos, C., Bova, G., Bova, V., Bozzani, A., Bozzetto, R. M., Braga, V. T., Braglia, M., Bramati, E., Brazzoli, C., Breglia, G., Brescia, A., Briganti, D., Brigato, G., Brocchi, A., Brosio, F. A., Bruni, E., Buscaglia, E., Bussini, M. D., Bussotti, A., Buzzaccarini, F., Buzzatti, A., Caccamo, G., Cacciavillani, C., Caggiano, G., Calciano, F. P., Calderisi, M., Calienno, S., Caltagirone, P., Calzolari, I., Cammisa, M., Campanaro, M., Campanella, G. B., Campese, F., Canali, G., Candiani, D. E. L., Canepa, R., Canini, D., Canino, A., Cantoro, E. A., Capilupi, V., Capotosto, P., Cappelli, B., Capraro, G., Carafa, F. A., Carano, Q., Carcaterra, V., Carriero, D., Carrozzo, G., Cartanese, M., Casalena, M., Casarola, M., Caso, C., Casotto, M., Castaldi, F., Castegnaro, R., Castellani, G., Castri, S., Catalano, E., Catinello, N., Caturano, G., Cavallaro, R., Cavallo, A. M., Cavallo, G., Cavion, M. T., Cavirani, G., Cazzaniga, F., Cazzetta, D., Cecconi, V., Cefalo, A., Celebrano, M., Celora, A., Centonze, P., Cerati, D., Cesaretti, D., Checchia, G., Checchin, A., Cherubini, M., Chianese, L., Chiappa, A., Chiappa, M. V., Chiariello, G., Chiavini, G., Chicco, M., Chiumeo, F., Ciacciarelli, A., Ciaci, D., Ciancaglini, R., Cicale, C., Cicale, S., Cipolla, A., Ciruolo, A., Citeri, A. L., Citterio, G., Clerici, M., Coazzoli, E., Collecchia, G., Colletta, F., Colombo, I., Colorio, P., Coluccia, S., Comerio, M., Comoretto, P., Compagni, M., Conte, O., Contri, S., Contrisciani, A., Coppetti, T., Corasaniti, F., Corradi, M. T., Corsano, A., Corsini, A., Corti, N., Costantini, G., Costantino, A., Cotroneo, S., Cozzi, D., Cravello, M. G., Cristiano, E., Cucchi, R., Cusmai, L., D'Errico, G. B., D'Agostino, P., Dal Bianco, L., Dal Mutto, U., Dal Pozzo, G., Dallapiccola, P., Dallatorre, G., Dalle Molle, G., Dalloni, E., D'Aloiso, A., D'Amicis, G., Danese, R., Danieli, D., Danisi, G., D'Anna, M. A., Danti, G., D'Ascanio, S., Davidde, G., De Angeli, D., De Bastiani, R., De Battisti, A., De Bellis, A., De Berardinis, G., De Carlo, F., De Giorgi, D., De Gobbi, R., De Lorenzis, E., De Luca, P., De Martini, G., De Marzi, M., De Matteis, D., De Padova, S., De Polo, P., De Sabato, N., De Stefano, T., De Vita, M. T., De Vito, U., De Zolt, V., Debernardi, F., Del Carlo, A., Del Re, G., Del Zotti, F., D'Elia, R., Della Giovanna, P., Dell'Acqua, L., Dell'Orco, R. L., Demaria, G., Di Benedetto, M. G., Di Chiara, G., Di Corcia, V., Di Domizio, O., Di Donato, P., Di Donato, S., Di Fermo, G., Di Franco, M., Di Giovannantonio, G., Di Lascio, G., Di Lecce, G., Di Lorenzo, N., Di Maro, T., Di Mattia, Q., Di Michele, E., Di Modica, R. S., Di Murro, D., Di Noi, M. C., Di Paoli, V., Di Santi, M., Di Sanzo, A., Di Turi, C., Diazzi, A., Dileo, I., D'Ingianna, A. P., Dolci, A., Dona, G., Donato, C., Donato, P., Donini, A., Donna, M. E., Donvito, T. V., Esposito, L., Esposito, N., Evangelista, M., Faita, G., Falco, M., Falcone, D. A., Falorni, F., Fanciullacci, A., Fanton, L., Fasolo, L., Fassina, R., Fassone, A., Fatarella, P., Fedele, F., Fera, I., Fera, L., Ferioli, S., Ferlini, M. G., Ferlino, R., Ferrante, G., Ferrara, F. N., Ferrarese, M. F., Ferrari, G., Ferrari, O., Ferreri, A., Ferroni, M., Fezzi, G., Figaroli, C., Fina, M. G., Fioretta, A., Fiorucci, C., Firrincieli, R., Fischetti, M., Fischietti, G., Fiume, D. C., Flecchia, G., Forastiere, G., Fossati, B., Franceschi, P. 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C., Marrali, F., Marseglia, C., Martello, G., Martino, C., Martino, G., Martino, M., Marulli, C. F., Maruzzi, G., Marzotti, A., Mascheroni, G., Mascolo, P., Masoch, G., Masone, R., Massa, L., Massafra, M., Massi, M., Massignani, D. M., Matarese, A. M., Matini, G., Mauro, R., Mazzi, M., Mazzillo, A., Mazzocato, E., Mazzoleni, N. S., Mazzone, A., Melacci, A., Mele, E., Meliota, P., Menaspa, S., Meneghello, F., Merola, G., Merone, L., Metrucci, A., Mezzina, V., Micchi, A., Michielon, A., Migliore, N., Minero, G., Minotta, F., Mirandola, C., Mistrorigo, S., Modafferi, L., Moitre, R., Mola, E., Monachese, C., Mongiardini, C., Montagna, F., Montani, M., Montemurno, I., Montolli, R., Montorsi, S., Montresor, M., Monzani, M. G., Morabito, F., Mori, G., Moro, A., Mosca, M. F., Motti, F., Muddolon, L., Mugnai, M., Muscas, F., Naimoli, F., Nanci, G., Nargi, E., Nasorri, R., Nastrini, G., Negossi, M., Negrini, A., Negroni, A., Neola, V., Niccolini, F., Niro, C. M., Nosengo, C., Novella, G., Nuti, C., Obici, F., Olita, C., Oliverio, S. S., Olivieri, I., Oriente, S., Orlando, G., Paci, C., Pagano, G., Pagliara, C., Paita, G., Paladini, G., Paladino, G., Palano, T., Palatella, A., Palermo, P., Palmisano, M., Pando, P., Panessa, P., Panigo, F., Panozzo, G., Panvini, F., Panzieri, F., Panzino, A., Panzitta, F., Paoli, N., Papagna, R., Papaleo, M. G., Papalia, G., Parisi, R., Parotti, N., Parravicini, D., Passarella, P., Pastore, G. A., Patafio, M., Pavone, P., Pedroli, W., Pedroni, M., Pelligra, G., Pellizzari, M., Penati, A., Perlot, M., Perrone, A., Perrone, G., Peruzzi, P., Peselli, C., Petracchini, L., Petrera, L., Petrone, S., Peverelli, C., Pianorsi, F., Piazza, G. P., Piazzolla, G., Picci, A., Pienabarca, G., Pietronigro, T. P., Pignocchino, P., Pilone, R., Pinto, D., Pirovano, E., Pirrotta, D., Pisante, V., Pitotto, P., Pittari, L., Piva, A., Pizzoglio, A., Plantera, O. R., Plebani, W., Plessi, S., Podrecca, D., Poerio, V., Poggiani, F., Pogliani, W., Poli, L., Poloni, F. G., Porcelli, R., Porto, S., Pranzo, L., Prevedello, C., Profeta, C., Profico, D., Punzi, A., Quaglia, G. M., Racano, M., Raccone, A., Radice, F., Raho, C. A., Raimondi, R., Raino, M., Ramponi, R., Ramunni, A., Ramunni, A. L., Ravasio, F., Ravera, M., Re Sarto, G., Rebustello, G., Regazzoli, S., Restelli, C., Rezzonico, M., Ricchiuto, F., Rigo, S., Rigon, G., Rigon, R., Rinaldi, O. V., Rinaldi, M., Risplendente, P. G., Rispoli, M., Riundi, R., Riva, M. G., Rizzi, A. L., Rizzi, D., Rizzo, L. D., Rocchi, L., Rondinone, B., Rosa, B., Rosati, F., Roselli, F., Rossetti, A., Rossetti, C., Rossi, R., Rossi, P. R., Rossi, A., Rossi, C. L., Rossitto, A., Ruffini, R., Ruffo, A., Ruggio, S., Ruo, M., Russo, B., Russo, L., Russo, R., Russo, S., Russo, U., Russo, V., Ruta, G., Sacchi, F., Sacco Botto, F., Saia, A., Salladini, G., Salmoiraghi, S., Saluzzo, F., Salvatore, C., Salvatori, E., Salvio, G., Sandri, P., Sandrini, T., Sangermano, V., Santoni, N., Saracino, A. D., Saracino, A., Sarasin, P., Sardo Infirri, C., Sarri, B., Sartori, G., Sartori, N., Sauro, C., Scaglioni, M., Scalfi, C., Scamardella, A. M., Scandale, G., Scandone, L., Scannavini, G., Scarati, R., Scardi, A., Scarpa, F. M., Scazzi, P., Schifone, A., Schiroso, G., Scigliano, G., Scilla, A., Sciortino, M., Scolaro, G., Scollo, E., Scorretti, G., Sellitti, R., Selmo, A., Selvaggio, G., Sempio, A., Seren, F., Serio, L., Serra, C., Serra, L., Siciliano, D., Sideri, A., Sighele, M., Signore, R., Siliberto, F., Silvestro, M., Simioni, G., Simmini, G., Simonato, L., Sinchetto, F., Sizzano, E., Smajato, G., Smaldone, M., Sola, G., Sordillo, L., Sovran, C. 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Subjects
Male, medicine.medical_specialty, Endocrinology, Diabetes and Metabolism, Population, 030209 endocrinology & metabolism, 030204 cardiovascular system & hematology, Overweight, 03 medical and health sciences, 0302 clinical medicine, Endocrinology, Prediction model, Internal medicine, Diabetes mellitus, Internal Medicine, Diabetes Mellitus, Medicine, Humans, Multicenter Studies as Topic, Myocardial infarction, Risk factor, education, Stroke, Aged, Randomized Controlled Trials as Topic, education.field_of_study, Lifestyle habits, business.industry, Major cardiovascular events, Atrial fibrillation, General Medicine, Middle Aged, medicine.disease, Cardiovascular Diseases, Heart failure, Physical therapy, Female, medicine.symptom, business, Diabetic Angiopathies
Abstract

To verify whether it is possible, in people with diabetes mellitus (DM) considered at very high cardiovascular (CV) risk, stratify this risk better and identify significant modifiable risk factor (including lifestyle habits) to help patients and clinicians improve CV prevention. People with DM and microvascular diseases or one or more CV risk factors (hypertension, hyperlipidemia, smoking, poor dietary habits, overweight, physical inactivity) included in the Risk and Prevention study were selected. We considered the combined endpoint of non-fatal acute myocardial infarction and stroke and CV death. A multivariate Cox proportional analysis was carried out to identify relevant predictors. We also used the RECPAM method to identify subgroups of patients at higher risk. In our study, the rate of major CV events was lower than expected (5 % in 5 years). Predictors of CV events were age, male, sex, heart failure, previous atherosclerotic disease, atrial fibrillation, insulin treatment, high HbA1c, heart rate and other CV diseases while being physically active was protective. RECPAM analysis indicated that history of atherosclerotic diseases and a low BMI defined worse prognosis (HR 4.51 95 % CI 3.04–6.69). Among subjects with no previous atherosclerotic disease, men with HbA1c more than 8 % were at higher CV risk (HR 2.77; 95 % CI 1.86–4.14) with respect to women. In this population, the rate of major CV events was lower than expected. This prediction model could help clinicians identify people with DM at higher CV risk and support them in achieving goals of physical activity and HbA1c.

Published
2016

36. Polar Localization of PhoN2, a Periplasmic Virulence-Associated Factor of Shigella flexneri, Is Required for Proper IcsA Exposition at the Old Bacterial Pole

Author

Scribano D, Petrucca A, Pompili M, Ambrosi C, Bruni E, Zagaglia C, Prosseda G, Nencioni L, POLTICELLI, Fabio, Nicoletti M., CASALINO, Maria Assunta, Scribano, D, Petrucca, A, Pompili, M, Ambrosi, C, Bruni, E, Zagaglia, C, Prosseda, G, Nencioni, L, Casalino, Maria Assunta, Polticelli, Fabio, and Nicoletti, M.

Subjects
bacteria
Abstract

Proper protein localization is critical for bacterial virulence. PhoN2 is a virulence-associated ATP-diphosphohydrolase (apyrase) involved in IcsA-mediated actin-based motility of S. flexneri. Herein, by analyzing a ΔphoN2 mutant of the S. flexneri strain M90T and by generating phoN2::HA fusions, we show that PhoN2, is a periplasmic protein that strictly localizes at the bacterial poles, with a strong preference for the old pole, the pole where IcsA is exposed, and that it is required for proper IcsA exposition. PhoN2-HA was found to be polarly localized both when phoN2::HA was ectopically expressed in a Escherichia coli K-12 strain and in a S. flexneri virulence plasmid-cured mutant, indicating a conserved mechanism of PhoN2 polar delivery across species and that neither IcsA nor the expression of other virulence-plasmid encoded genes are involved in this process. To assess whether PhoN2 and IcsA may interact, two-hybrid and cross-linking experiments were performed. While no evidence was found of a PhoN2-IcsA interaction, unexpectedly the outer membrane protein A (OmpA) was shown to bind PhoN2-HA through its periplasmic-exposed C-terminal domain. Therefore, to identify PhoN2 domains involved in its periplasmic polar delivery as well as in the interaction with OmpA, a deletion and a set of specific amino acid substitutions were generated. Analysis of these mutants indicated that neither the (183)PAPAP(187) motif of OmpA, nor the N-terminal polyproline (43)PPPP(46) motif and the Y155 residue of PhoN2 are involved in this interaction while P45, P46 and Y155 residues were found to be critical for the correct folding and stability of the protein. The relative rapid degradation of these amino acid-substituted recombinant proteins was found to be due to unknown S. flexneri-specific protease(s). A model depicting how the PhoN2-OmpA interaction may contribute to proper polar IcsA exposition in S. flexneri is presented

Published
2014

37. N-3 fatty acids in patients with multiple cardiovascular risk factors

Author

Roncaglioni, Maria Carla, Avanzini, Fausto, Barlera, Simona, Marzona, Irene, Milani, Valentina, Tombesi, Massimo, Caimi, Vittorio, Longoni, Paolo, Silletta, Maria Giuseppina, Tognoni, Gianni, Marchioli, Avanzini F, Roberto., Caimi, V, Longoni, P, Marchioli, R, Roncaglioni, Mc, Silletta, Mg, Tognoni, G, Tombesi, M, Barlera, S, Milani, V, Nicolis, Eb, Casola, C, Marzona, I, Massa, E, Marrocco, W, Micalella, M, Avanzini, F, Franzosi, Mg, Geraci, E, Giansiracusa, N, Rocchetti, L, Decarli, A, Satolli, R, Alli, C, Beghi, E, Bertele', V, Volpi, A, Baviera, M, Monesi, L, Pangrazzi, I, Nicolis, E, Clerici, F, Palumbo, A, Sgaroni, G, Pioggiarella, R, Scarano, M, Marfisi, Rm, Flamminio, A, Macino, L, Ferri, B, Pera, C, Polidoro, A, Abbatino, D, Acquati, M, Addorisio, G, Adinolfi, D, Adreani, L, Agistri, Mr, Agneta, A, Agnolio, Ml, Agostini, N, Agostino, G, Airò, A, Alaimo, N, Albano, M, Albano, N, Alecci, G, Alemanno, S, Alexanian, A, Alfarano, M, Alfè, L, Alonzo, N, Alvino, S, Ancora, A, Andiloro, S, Andreatta, E, Angeli, S, Angiari, F, Angilletti, V, Annicchiarico, C, Anzivino, M, Aprea, R, Aprile, A, Aprile, E, Aprile, I, Aprile, L, Armellani, V, Arnetoli, M, Aronica, A, Autiero, V, Bacca, G, Baccalaro, Am, Bacci, M, Baglio, G, Bagnani, M, Baiano, A, Baldari, A, Ballarini, L, Banchi, G, Bandera, R, Bandini, F, Baratella, M, Barbieri, A, Barbieri Vita, A, Bardi, M, Barlocchi, M, Baron, P, Bartoli, M, Basile, A, Basile, F, Basile, S, Battaggia, A, Battaglia, A, Baù, A, Beconcini, G, Beggio, R, Belfiore, Pa, Belicchi, M, Bellamoli, S, Bellini, C, Bellomo, M, Benetollo, C, Benetti, R, Beretta, E, Bertalero, P, Bertaso, Fg, Bertolani, U, Bettelli, G, Biagiotti, G, Bianchi, S, Bianco, G, Biccari, F, Bigioli, F, Bindi, M, Bisanti, G, Bitetti, Em, Blasetti, Mp, Blesi, F, Boato, V, Boga, S, Boidi, E, Boldrin, G, Bollati, A, Bolzan, L, Bolzonella, S, Bonardi, P, Bonato, Gb, Bonci, M, Bonfitto, G, Bonincontro, E, Boninsegna, F, Bonissone, D, Bono, L, Bonollo, E, Borghi, M, Borioli, N, Borsatto, M, Bosco, T, Bosisio Pioltelli, M, Botarelli, C, Botassis, S, Bottini, F, Bottos, C, Bova, G, Bova, V, Bozzani, A, Bozzetto, Rm, Braga, Vt, Braglia, M, Bramati, E, Brazzoli, C, Breglia, G, Brescia, A, Briganti, D, Brigato, G, Brocchi, A, Brosio, Fa, Bruni, E, Buscaglia, E, Bussini, Md, Bussotti, A, Buzzaccarini, F, Buzzatti, A, Caccamo, G, Cacciavillani, C, Caggiano, G, Calciano, Fp, Calderisi, M, Calienno, S, Caltagirone, P, Calzolari, I, Cammisa, M, Campanaro, M, Campanella, Gb, Campese, F, Canali, G, Candiani, De, Canepa, R, Canini, D, Canino, A, Cantoro, Ea, Capilupi, V, Capotosto, P, Cappelli, B, Capraro, G, Carafa, Fa, Carano, Q, Carcaterra, V, Carriero, D, Carrozzo, G, Cartanese, M, Casalena, M, Casarola, M, Caso, C, Casotto, M, Castaldi, F, Castegnaro, R, Castellani, G, Castri, S, Catalano, E, Catinello, N, Caturano, G, Cavallaro, R, Cavallo, Am, Cavallo, G, Cavion, Mt, Cavirani, G, Cazzaniga, F, Cazzetta, D, Cecconi, V, Cefalo, A, Celebrano, M, Celora, A, 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Rebustello, G, Regazzoli, S, Restelli, C, Rezzonico, M, Ricchiuto, F, Rigo, S, Rigon, G, Rigon, R, Rinaldi, Ov, Rinaldi, M, Risplendente, Pg, Rispoli, M, Riundi, R, Riva, Mg, Rizzi, Al, Rizzi, D, Rizzo, Ld, Rocchi, L, Rondinone, B, Rosa, B, Rosati, F, Roselli, F, Rossetti, A, Rossetti, C, Rossi, R, Rossi, Pr, Rossi, A, Rossi, Cl, Rossitto, A, Ruffini, R, Ruffo, A, Ruggio, S, Ruo, M, Russo, B, Russo, L, Russo, R, Russo, S, Russo, U, Russo, V, Ruta, G, Sacchi, F, Sacco Botto, F, Saia, A, Salladini, G, Salmoiraghi, S, Saluzzo, F, Salvatore, C, Salvatori, E, Salvio, G, Sandri, P, Sandrini, T, Sangermano, V, Santoni, N, Saracino, Ad, Saracino, A, Sarasin, P, Sardo Infirri, C, Sarrì, B, Sartori, G, Sartori, N, Sauro, C, Scaglioni, M, Scalfi, C, Scamardella, Am, Scandale, G, Scandone, L, Scannavini, G, Scarati, R, Scardi, A, Scarpa, Fm, Scazzi, P, Schifone, A, Schirosa, G, Scigliano, G, Scilla, A, Sciortino, M, Scolaro, G, Scollo, E, Scorretti, G, Sellitti, R, Selmo, A, Selvaggio, G, Sempio, 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Subjects
Male, medicine.medical_specialty, General Practice, Kaplan-Meier Estimate, Placebo, Double-Blind Method, Risk Factors, Internal medicine, Fatty Acids, Omega-3, Clinical endpoint, medicine, Humans, Myocardial infarction, Treatment Failure, Aged, Proportional Hazards Models, chemistry.chemical_classification, Omega-3, business.industry, Proportional hazards model, Medicine (all), Hazard ratio, Fatty Acids, General Medicine, Middle Aged, medicine.disease, Hospitalization, Primary Prevention, chemistry, Cardiovascular Diseases, Heart failure, Cohort, Female, business, Polyunsaturated fatty acid
Abstract

Background Trials have shown a beneficial effect of n-3 polyunsaturated fatty acids in patients with a previous myocardial infarction or heart failure. We evaluated the potential benefit of such therapy in patients with multiple cardiovascular risk factors or atherosclerotic vascular disease who had not had a myocardial infarction. Methods In this double-blind, placebo-controlled clinical trial, we enrolled a cohort of patients who were followed by a network of 860 general practitioners in Italy. Eligible patients were men and women with multiple cardiovascular risk factors or atherosclerotic vascular disease but not myocardial infarction. Patients were randomly assigned to n-3 fatty acids (1 g daily) or placebo (olive oil). The initially specified primary end point was the cumulative rate of death, nonfatal myocardial infarction, and nonfatal stroke. At 1 year, after the event rate was found to be lower than anticipated, the primary end point was revised as time to death from cardiovascular causes or admission to the hospital for cardiovascular causes. Results Of the 12,513 patients enrolled, 6244 were randomly assigned to n-3 fatty acids and 6269 to placebo. With a median of 5 years of follow-up, the primary end point occurred in 1478 of 12,505 patients included in the analysis (11.8%), of whom 733 of 6239 (11.7%) had received n-3 fatty acids and 745 of 6266 (11.9%) had received placebo (adjusted hazard ratio with n-3 fatty acids, 0.97; 95% confidence interval, 0.88 to 1.08; P=0.58). The same null results were observed for all the secondary end points. Conclusions In a large general-practice cohort of patients with multiple cardiovascular risk factors, daily treatment with n-3 fatty acids did not reduce cardiovascular mortality and morbidity. (Funded by Societa Prodotti Antibiotici and others; ClinicalTrials.gov number, NCT00317707.).

Published
2013

44. Mepacrine antagonises tumour cell growth induced by natural polyamines

Author

Rossi, Tiziana, Coppi, A, Bruni, E, Ruberto, Ippazio Antonio, Giudice, Stefania, and Baggio, Giosuè Gabriele

Subjects
Spermidine, Biogenic Polyamines, Blotting, Western, cell proliferation, Antineoplastic Agents, Breast Neoplasms, mepacrine, Cell Growth Processes, Vero, Quinacrine, Cell Line, Tumor, Polyamines, MCF-7, Chlorocebus aethiops, Putrescine, Animals, Humans, Spermine, Drug Screening Assays, Antitumor, Vero Cells
Abstract

Mepacrine is an antiproliferative agent, characterised by an aliphatic chain similar to that of natural polyamines whose activation is closely associated with cell proliferation and may lead to malignant transformation and neurodegenerative diseases. This study aims to investigate a possible antagonism between mepacrine and polyamines in tumour proliferation.MCF-7 and Vero cells were cultured in Eagle's minimum essential medium and then subjected to graded concentrations of putrescine, spermine and spermidine alone and in combination with mepacrine. Methyl thiazole tetrazolium test and Western-blotting were performed.Putrescine and spermidine at 0.5 mg/l significantly stimulated cell growth, whereas mepacrine treatment confirmed the enhanced p21 expression previously reported by a recent study and growth inhibition. When used in combination, mepacrine antagonized MCF-7 growth induced by polyamines.Our results suggest that mepacrine may represent a choice in the treatment of tumours induced by the modified concentration of polyamines.

Published
2008

45. Role of the pharmaceutical excipients in the tamoxifen activity on MCF-7 and Vero cell cultures

Author

Rossi, T., Valentina Iannuccelli, Coppi, G., Bruni, E., and Baggio, G.

Subjects
microparticles, Chitosan, Tamoxifen, MCF-7, bioavailability, toxicity, alginate/chitosan, Toxicity, tamoxifen, Alginates, Hexuronic Acids, Breast Neoplasms, Cell Growth Processes, Excipients, Glucuronic Acid, Cell Line, Tumor, Chlorocebus aethiops, Animals, Humans, Particle Size, Vero Cells
Abstract

Microparticles are used for controlled drug delivery. With the aim of improving both bioavailability and tamoxifen selective toxicity, the activity of tamoxifen embedded in calcium alginate/chitosan microparticles was studied.Tamoxifen alone and embedded in microparticles prepared with sodium alginate from Kelco (62% mannuronic acid and 38% guluronic acid) and from Fluka (30% mannuronic acid and 70% guluronic acid) was added to MCF-7 and Vero cultures and evaluated for antiproliferative activity by the MTT test.The use of Kelco or Fluka alginate resulted in different LD(50) values on Vero and MCF-7 cultures, showing a higher cytotoxicity toward Vero cells treated with tamoxifen embedded in Kelco microparticles (25 microM vs. 48 microM on MCF-7 cells) but a selective toxicity with Fluka microparticles (25 microM and 10 microM on Vero and MCF-7 cells respectively).Microparticle formulation may improve selective toxicity according to the alginate employed: differences in the chemical alginate composition can dramatically change both drug activity and toxicity.

Published
2008

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