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1. Preface

Author

Cannataro, M., primary, Guzzi, P.H., additional, Agapito, G., additional, Zucco, C., additional, and Milano, M., additional

Published
2022
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2. Acknowledgments

Author

Cannataro, M., primary, Guzzi, P.H., additional, Agapito, G., additional, Zucco, C., additional, and Milano, M., additional

Published
2022
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5. Effects on the incidence of cardiovascular events of the addition of pioglitazone versus sulfonylureas in patients with type 2 diabetes inadequately controlled with metformin (TOSCA.IT): a randomised, multicentre trial

Author

Vaccaro, O, Masulli, M, Nicolucci, A, Bonora, E, Del Prato, S, Maggioni, A, Rivellese, A, Squatrito, S, Giorda, C, Sesti, G, Mocarelli, P, Lucisano, G, Sacco, M, Signorini, S, Cappellini, F, Perriello, G, Babini, A, Lapolla, A, Gregori, G, Giordano, C, Corsi, L, Buzzetti, R, Clemente, G, Di Cianni, G, Iannarelli, R, Cordera, R, La Macchia, O, Zamboni, C, Scaranna, C, Boemi, M, Iovine, C, Lauro, D, Leotta, S, Dall'Aglio, E, Cannarsa, E, Tonutti, L, Pugliese, G, Bossi, A, Anichini, R, Dotta, F, Di Benedetto, A, Citro, G, Antenucci, D, Ricci, L, Giorgino, F, Santini, C, Gnasso, A, De Cosmo, S, Zavaroni, D, Vedovato, M, Consoli, A, Calabrese, M, di Bartolo, P, Fornengo, P, Riccardi, G, D'Angelo, F, Giansanti, R, Tanase, L, Lanari, L, Testa, I, Pancani, F, Ranchelli, A, Vagheggi, P, Scatona, A, Fontana, L, Laviola, L, Tarantino, L, Ippolito, C, Gigantelli, V, Manicone, M, Conte, E, Trevisan, R, Rota, R, Dodesini, A, Reggiani, G, Montesi, L, Mazzella, N, Forlani, G, Caselli, C, Di Luzio, R, Mazzotti, A, Aiello, A, Barrea, A, Musto, A, D'Amico, F, Sinagra, T, Longhitano, S, Trowpea, V, Sparti, M, Italia, S, Lisi, E, Grasso, G, Pezzino, V, Insalaco, F, Carallo, C, Scicchitano, C, De Franceschi, M, Calbucci, G, Ripani, R, Cuneo, G, Corsi, S, Romeo, F, Lesina, A, Comoglio, M, Bonetto, C, Robusto, A, Nada, E, Asprino, V, Cetraro, R, Impieri, M, Lucchese, G, Donnarumma, G, Tizio, B, Lenza, L, Paraggio, P, Tomasi, F, Dozio, N, Scalambra, E, Mannucci, E, Lamanna, C, Cignarelli, M, Macchia, O, Fariello, S, Sorrentino, M, Franzetti, I, Radin, R, Annunziata, F, Bonabello, L, Durante, A, Dolcino, M, Gallo, F, Mazzucchelli, C, Aleo, A, Melga, P, Briatore, L, Maggi, D, Storace, D, Cecoli, F, D'Ugo, E, Pupillo, M, Baldassarre, M, Salvati, F, Minnucci, A, De Luca, A, Zugaro, A, Santarelli, L, Bosco, A, Petrella, V, La Verghetta, G, D'Andrea, S, Giuliani, A, Polidoro, W, Sperandio, A, Sciarretta, F, Pezzella, A, Carlone, A, Potenziani, S, Venditti, C, Foffi, C, Carbone, S, Cipolloni, L, Moretti, C, Leto, G, Serra, R, Petrachi, F, Romano, I, Lacaria, E, Russo, L, Goretti, C, Sannino, C, Dolci, M, Bruselli, L, Mori, M, Baccetti, F, Del Freo, M, Cucinotta, D, Giunta, L, Ruffo, M, Cannizzaro, D, Pintaudi, B, Perrone, G, Pata, P, Ragonese, F, Lettina, G, Mancuso, T, Coppolino, A, Piatti, P, Monti, L, Stuccillo, M, Lucotti, P, Setola, M, Crippa, G, Loi, C, Oldani, M, Bottalico, M, Pellegata, B, Bonomo, M, Menicatti, L, Resi, V, Bertuzzi, F, Disoteo, E, Pizzi, G, Annuzzi, G, Capaldo, B, Nappo, R, Auciello, S, Turco, A, Costagliola, L, Corte, G, Vallefuoco, P, Nappi, F, Vitale, M, Cocozza, S, Ciano, O, Massimino, E, Garofalo, N, Avogaro, A, Guarneri, G, Fedele, D, Sartore, G, Chilelli, N, Burlina, S, Bonsembiante, B, Galluzzo, A, Torregrossa, V, Mancastroppa, G, Arsenio, L, Cioni, F, Caronna, S, Papi, M, Santeusanio, F, Calagreti, G, Timi, A, Tantucci, A, Marino, C, Ginestra, F, Di Biagio, R, Taraborelli, M, Miccoli, R, Bianchi, C, Garofolo, M, Politi, K, Penno, G, Livraga, S, Calzoni, F, Corsini, E, Tedeschi, A, Gagliano, M, Ippolito, G, Salutini, E, Cervellino, F, Natale, M, Salvatore, V, Zampino, A, Sinisi, R, Arcangeli, A, Zogheri, A, Guizzotti, S, Longo, R, Pellicano, F, Scolozzi, P, Termine, S, Luberto, A, Ballardini, G, Trojani, C, Mazzuca, P, Bruglia, M, Ciamei, M, Genghini, S, Zannoni, C, Rangel, G, Salvi, L, Zappaterreno, A, Cordone, S, Simonelli, P, Meggiorini, M, Frasheri, A, Di Pippo, C, Maglio, C, Mazzitelli, G, Rinaldi, M, Galli, A, Romano, M, D'Angelo, P, Suraci, C, Bacci, S, Palena, A, Genovese, S, Mancino, M, Rondinelli, M, Capone, F, Calabretto, E, Bulgheroni, M, Bucciarelli, L, Ceccarelli, E, Fondelli, C, Santacroce, C, Guarino, E, Nigi, L, Lalli, C, Di Vizia, G, Scarponi, M, Montani, V, Di Bernardino, P, Romagni, P, Dolcetti, K, Forte, E, Tamburo, L, Perin, P, Prinzis, T, Gruden, G, Bruno, G, Zucco, C, Perotta, M, Marena, S, Monsignore, S, Panero, F, Ponzi, F, Carpinteri, R, Casagrande, M, Coletti, M, Balini, A, Filopanti, M, Madaschi, S, Pulcina, A, Grimaldi, F, Venturini, G, Agus, S, Pagnutti, S, Guidotti, F, Cavarape, A, Cigolini, M, Pichiri, I, Brangani, C, Fainelli, G, Tomasetto, E, Zoppini, G, Galletti, A, Perrone, D, Capra, C, Bianchini, F, Ceseri, M, Di Nardo, B, Sasso, E, Bartolomei, B, Suliman, I, Fabbri, G, Romano, G, Maturo, N, Nunziata, G, Capobianco, G, De Simone, G, Villa, V, Rota, G, Pentangelo, C, Carbonara, O, Caiazzo, G, Cutolo, M, Sorrentino, T, Mastrilli, V, Amelia, U, Masi, S, Corigliano, G, Gaeta, I, Armentano, V, Calatola, P, Capuano, G, Angiulli, B, Auletta, P, Petraroli, E, Iodice, C, Agrusta, M, Vaccaro O., Masulli M., Nicolucci A., Bonora E., Del Prato S., Maggioni A. P., Rivellese A. A., Squatrito S., Giorda C. B., Sesti G., Mocarelli P., Lucisano G., Sacco M., Signorini S., Cappellini F., Perriello G., Babini A. C., Lapolla A., Gregori G., Giordano C., Corsi L., Buzzetti R., Clemente G., Di Cianni G., Iannarelli R., Cordera R., La Macchia O., Zamboni C., Scaranna C., Boemi M., Iovine C., Lauro D., Leotta S., Dall'Aglio E., Cannarsa E., Tonutti L., Pugliese G., Bossi A. C., Anichini R., Dotta F., Di Benedetto A., Citro G., Antenucci D., Ricci L., Giorgino F., Santini C., Gnasso A., De Cosmo S., Zavaroni D., Vedovato M., Consoli A., Calabrese M., di Bartolo P., Fornengo P., Riccardi G., D'Angelo F., Giansanti R., Tanase L., Lanari L., Testa I., Pancani F., Ranchelli A., Vagheggi P., Scatona A., Fontana L., Laviola L., Tarantino L., Ippolito C., Gigantelli V., Manicone M., Conte E., Trevisan R., Rota R., Dodesini A. R., Reggiani G. 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L., Sperandio A., Sciarretta F., Pezzella A., Carlone A., Potenziani S., Venditti C., Foffi C., Carbone S., Cipolloni L., Moretti C., Leto G., Serra R., Petrachi F., Romano I., Lacaria E., Russo L., Goretti C., Sannino C., Dolci M., Bruselli L., Mori M. L., Baccetti F., Del Freo M., Cucinotta D., Giunta L., Ruffo M. C., Cannizzaro D., Pintaudi B., Perrone G., Pata P., Ragonese F., Lettina G., Mancuso T., Coppolino A., Piatti P. M., Monti L., Stuccillo M., Lucotti P., Setola M., Crippa G. V., Loi C., Oldani M., Bottalico M. L., Pellegata B., Bonomo M., Menicatti L. S. M., Resi V., Bertuzzi F., Disoteo E. O., Pizzi G., Annuzzi G., Capaldo B., Nappo R., Auciello S. M., Turco A. A., Costagliola L., Corte G. D., Vallefuoco P., Nappi F., Vitale M., Cocozza S., Ciano O., Massimino E., Garofalo N., Avogaro A., Guarneri G., Fedele D., Sartore G., Chilelli N. 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P., Genovese S., Mancino M., Rondinelli M., Capone F., Calabretto E., Bulgheroni M., Bucciarelli L., Ceccarelli E., Fondelli C., Santacroce C., Guarino E., Nigi L., Lalli C., Di Vizia G., Scarponi M., Montani V., Di Bernardino P., Romagni P., Dolcetti K., Forte E., Tamburo L., Perin P. C., Prinzis T., Gruden G., Bruno G., Zucco C., Perotta M., Marena S., Monsignore S., Panero F., Ponzi F., Carpinteri R., Casagrande M. L., Coletti M. F., Balini A., Filopanti M., Madaschi S., Pulcina A., Grimaldi F., Venturini G., Agus S., Pagnutti S., Guidotti F., Cavarape A., Cigolini M., Pichiri I., Brangani C., Fainelli G., Tomasetto E., Zoppini G., Galletti A., Perrone D., Capra C., Bianchini F., Ceseri M., Di Nardo B., Sasso E., Bartolomei B., Suliman I., Fabbri G., Romano G., Maturo N., Nunziata G., Capobianco G., De Simone G., Villa V., Rota G., Pentangelo C., Carbonara O., Caiazzo G., Cutolo M., Sorrentino T., Mastrilli V., Amelia U., Masi S., Corigliano G., Gaeta I., Armentano V., Calatola P., Capuano G., Angiulli B., Auletta P., Petraroli E., Iodice C. 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P., Rivellese A. A., Squatrito S., Giorda C. B., Sesti G., Mocarelli P., Lucisano G., Sacco M., Signorini S., Cappellini F., Perriello G., Babini A. C., Lapolla A., Gregori G., Giordano C., Corsi L., Buzzetti R., Clemente G., Di Cianni G., Iannarelli R., Cordera R., La Macchia O., Zamboni C., Scaranna C., Boemi M., Iovine C., Lauro D., Leotta S., Dall'Aglio E., Cannarsa E., Tonutti L., Pugliese G., Bossi A. C., Anichini R., Dotta F., Di Benedetto A., Citro G., Antenucci D., Ricci L., Giorgino F., Santini C., Gnasso A., De Cosmo S., Zavaroni D., Vedovato M., Consoli A., Calabrese M., di Bartolo P., Fornengo P., Riccardi G., D'Angelo F., Giansanti R., Tanase L., Lanari L., Testa I., Pancani F., Ranchelli A., Vagheggi P., Scatona A., Fontana L., Laviola L., Tarantino L., Ippolito C., Gigantelli V., Manicone M., Conte E., Trevisan R., Rota R., Dodesini A. R., Reggiani G. 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C., Burlina S., Bonsembiante B., Galluzzo A., Torregrossa V., Mancastroppa G., Arsenio L., Cioni F., Caronna S., Papi M., Santeusanio F., Calagreti G., Timi A., Tantucci A., Marino C., Ginestra F., Di Biagio R., Taraborelli M., Miccoli R., Bianchi C., Garofolo M., Politi K. S., Penno G., Livraga S., Calzoni F., Mancastroppa G. L. F., Corsini E., Tedeschi A., Gagliano M. S., Ippolito G., Salutini E., Cervellino F., Natale M., Salvatore V., Zampino A., Sinisi R., Arcangeli A., Zogheri A., Guizzotti S., Longo R., Pellicano F., Scolozzi P., Termine S., Luberto A., Ballardini G., Trojani C., Mazzuca P., Bruglia M., Ciamei M., Genghini S., Zannoni C., Rangel G., Salvi L., Zappaterreno A., Cordone S., Simonelli P., Meggiorini M., Frasheri A., Di Pippo C., Maglio C., Mazzitelli G., Rinaldi M. E., Galli A., Romano M., D'Angelo P., Suraci C., Bacci S., Palena A. P., Genovese S., Mancino M., Rondinelli M., Capone F., Calabretto E., Bulgheroni M., Bucciarelli L., Ceccarelli E., Fondelli C., Santacroce C., Guarino E., Nigi L., Lalli C., Di Vizia G., Scarponi M., Montani V., Di Bernardino P., Romagni P., Dolcetti K., Forte E., Tamburo L., Perin P. C., Prinzis T., Gruden G., Bruno G., Zucco C., Perotta M., Marena S., Monsignore S., Panero F., Ponzi F., Carpinteri R., Casagrande M. L., Coletti M. F., Balini A., Filopanti M., Madaschi S., Pulcina A., Grimaldi F., Venturini G., Agus S., Pagnutti S., Guidotti F., Cavarape A., Cigolini M., Pichiri I., Brangani C., Fainelli G., Tomasetto E., Zoppini G., Galletti A., Perrone D., Capra C., Bianchini F., Ceseri M., Di Nardo B., Sasso E., Bartolomei B., Suliman I., Fabbri G., Romano G., Maturo N., Nunziata G., Capobianco G., De Simone G., Villa V., Rota G., Pentangelo C., Carbonara O., Caiazzo G., Cutolo M., Sorrentino T., Mastrilli V., Amelia U., Masi S., Corigliano G., Gaeta I., Armentano V., Calatola P., Capuano G., Angiulli B., Auletta P., Petraroli E., Iodice C. E., and Agrusta M.

Abstract

Background The best treatment option for patients with type 2 diabetes in whom treatment with metformin alone fails to achieve adequate glycaemic control is debated. We aimed to compare the long-term effects of pioglitazone versus sulfonylureas, given in addition to metformin, on cardiovascular events in patients with type 2 diabetes. Methods TOSCA.IT was a multicentre, randomised, pragmatic clinical trial, in which patients aged 50–75 years with type 2 diabetes inadequately controlled with metformin monotherapy (2–3 g per day) were recruited from 57 diabetes clinics in Italy. Patients were randomly assigned (1:1), by permuted blocks randomisation (block size 10), stratified by site and previous cardiovascular events, to add-on pioglitazone (15–45 mg) or a sulfonylurea (5–15 mg glibenclamide, 2–6 mg glimepiride, or 30–120 mg gliclazide, in accordance with local practice). The trial was unblinded, but event adjudicators were unaware of treatment assignment. The primary outcome, assessed with a Cox proportional-hazards model, was a composite of first occurrence of all-cause death, non-fatal myocardial infarction, non-fatal stroke, or urgent coronary revascularisation, assessed in the modified intention-to-treat population (all randomly assigned participants with baseline data available and without any protocol violations in relation to inclusion or exclusion criteria). This study is registered with ClinicalTrials.gov, number NCT00700856. Findings Between Sept 18, 2008, and Jan 15, 2014, 3028 patients were randomly assigned and included in the analyses. 1535 were assigned to pioglitazone and 1493 to sulfonylureas (glibenclamide 24 [2%], glimepiride 723 [48%], gliclazide 745 [50%]). At baseline, 335 (11%) participants had a previous cardiovascular event. The study was stopped early on the basis of a futility analysis after a median follow-up of 57·3 months. The primary outcome occurred in 105 patients (1·5 per 100 person-years) who were given pioglitazone and 108 (1·5 p

Published
2017

6. Estimating Ideology of Brazilian Legislative Parties

Author

Power, T and Zucco, C

Abstract

We present a new data set on the left-right placement of major Brazilian political parties serving in the first five legislatures under democracy. On the basis of survey responses of more than 850 federal legislators from 1990 to 2005, we generate party placements on an ideological scale where 1 = "left" and 10 = "right." The data are rescaled to account for idiosyncrasies in responses as well as variation in use of the survey scale across time. We discuss both the validity and the reliability of our new measures by comparing them to other data sets. We further discuss three substantive issues that the data reveal. First, ideological polarization has moderated over time. Second, the median legislator has shifted noticeably to the left and now stands equidistant from the influential PT and PSDB, the parties that have anchored recent presidential elections. Third, Brazilian political elites continue to shun self-identifications associated with political conservatism or neoliberalism.

Published
2016

7. Addition of either pioglitazone or a sulfonylurea in type 2 diabetic patients inadequately controlled with metformin alone: impact on cardiovascular events. A randomized controlled trial

Author

Vaccaro, O, Masulli, M, Bonora, E, Del Prato, S, Giorda, Cb, Maggioni, Ap, Mocarelli, P, Nicolucci, A, Rivellese, Aa, Squatrito, S, Riccardi, G, IT study group, T. O. S. C. A., Sud, Cm, Imbaro, S, Garofalo, N, Ferrannini, E, Howard, B, Gerdts, E, Imperatore, G, Tavazzi, L, Pellegrini, F, Fabbri, G, Levantesi, G, Turazza, F, Gentile, S, Panico, S, Brambilla, P, Signorini, S, Cappellini, F, Parma, C, D'Alonzo, D, Di Nardo, B, Ferrari, S, Franciosi, M, Pecce, R, Valentini, M, Ceseri, M, Bianchini, F, Baldini, E, Atzori, A, Boemi, M, D'Angelo, F, Giansanti, R, Ricci, L, Ranchelli, A, Di Berardino, P, Cannarsa, E, Giorgino, F, Manicone, M, Tarantino, L, Trevisan, R, Scaranna, C, Forlani, G, Montesi, L, Aiello, A, Barrea, A, Sinagra, T, Longhitano, S, Sesti, G, Gnasso, A, Carallo, C, Scicchitano, C, Santini, C, Calbucci, G, Ripani, R, Corsi, L, Corsi, S, Romeo, F, Asprino, V, Donnarumma, G, Tizio, B, Clemente, G, Tomasi, F, Dozio, N, Mannucci, E, Lamanna, C, Cignarelli, M, Macchia, Ol, Fariello, S, Cordera, R, Mazzucchelli, C, Pupillo, M, Zugaro, A, Bosco, A, De Luca, A, Iannarelli, R, Giuliani, A, Polidoro, L, Sperandio, A, Sciarretta, F, Raffaella, B, Venditti, C, Di Cianni, G, Goretti, C, Dolci, Ma, Bruselli, L, Mori, M, Baccetti, F, Gregori, G, Venezia, A, Cucinotta, D, Pintaudi, B, Ragonese, F, Pata, P, Piatti, Pm, Luccotti, P, Orsi, E, Bonomo, M, Menicatti, L, Turco, Aa, Ciano, O, Vallefuoco, P, Corigliano, G, Pentangelo, C, Petraroli, E, Auletta, P, Carbonara, O, Capobianco, G, Caiazzo, G, Angiulli, B, De Simone, G, Michele, C, Mastrilli, V, Nunziata, G, Romano, G, Gaeta, I, Sorrentino, T, Iovine, C, Nappi, F, Paolisso, G, Rizzo, Mr, Avogaro, A, Vedovato, M, Lapolla, A, Sartore, G, Burlina, S, Chilelli, Nc, Galluzzoy, A, Giordano, C, Torregrossa, V, Arsenio, L, Dall'Aglio, E, Cioni, F, Babini, M, Moncastroppa, G, Perriello, G, Timi, A, Consoli, A, Ginestra, F, Zavaroni, D, Calzoni, F, Miccoli, R, Bianchi, C, Politi, S, Anichini, R, Tedeschi, A, Citro, G, Zampino, A, Rosa, S, Natale, M, Giocoli, Cl, Caruso, E, Tramontano, L, Imbroinise, A, Perna, Cd, Calabrese, M, Zogheri, A, Luberto, A, Ballardini, G, Babini, Ac, Zannoni, C, Pugliese, G, Salvi, L, Mazzitelli, G, Zappaterreno, A, Frontoni, S, Ventricini, A, Lauro, D, Galli, A, Rinaldi, Me, Leotta, S, Fontana, L, Goretti, S, Pozzilli, P, Leonetti, F, Morano, S, Filetti, S, Cosmo, Sd, Bacci, S, Palena, Ap, Calatola, P, Capuano, G, Amelia, U, Dotta, Francesco, Guarino, E, Ceccarelli, E, Lalli, C, Scarponi, M, Forte, E, Potenziani, S, Perin, Pc, Marena, S, Zucco, C, Perotto, M, Bossi, A, Filopanti, M, Grimaldi, F, Tonutti, L, Cavarape, A, Cigolini, M, Pichiri, I, Brangani, C, Tomasetto, E, Capra, C, Cigolini, M., Vaccaro, O1, Masulli, M, Bonora, E, Del Prato, S, Giorda, Cb, Maggioni, Ap, Mocarelli, P, Nicolucci, A, Rivellese, Aa, Squatrito, S, Riccardi, G, Collaborators Riccardi G, T. O. S. C. A. IT study g. r. o. u. p., Sud, Cm, Imbaro, S, Vaccaro, O, Garofalo, N, Ferrannini, E, Howard, B, Gerdts, E, Imperatore, G, Tavazzi, L, Pellegrini, F, Fabbri, G, Levantesi, G, Turazza, F, Gentile, Sandro, Panico, S, Brambilla, P, Signorini, S, Cappellini, F, Parma, C, D'Alonzo, D, Di Nardo, B, Ferrari, S, Franciosi, M, Pecce, R, Valentini, M, Ceseri, M, Bianchini, F, Baldini, E, Atzori, A, Boemi, M, D'Angelo, F, Giansanti, R, Ricci, L, Ranchelli, A, Di Berardino, P, Cannarsa, E, Giorgino, F, Manicone, M, Tarantino, L, Trevisan, R, Scaranna, C, Forlani, G, Montesi, L, Aiello, A, Barrea, A, Sinagra, T, Longhitano, S, Sesti, G, Gnasso, A, Carallo, C, Scicchitano, C, Santini, C, Calbucci, G, Ripani, R, Corsi, L, Corsi, S, Romeo, F, Asprino, V, Donnarumma, G, Tizio, B, Clemente, G, Tomasi, F, Dozio, N, Mannucci, E, Lamanna, C, Cignarelli, M, Macchia, Ol, Fariello, S, Cordera, R, Mazzucchelli, C, Pupillo, M, Zugaro, A, Bosco, A, De Luca, A, Iannarelli, R, Giuliani, A, Polidoro, L, Sperandio, A, Sciarretta, F, Raffaella, B, Venditti, C, Di Cianni, G, Goretti, C, Dolci, Ma, Bruselli, L, Mori, M, Baccetti, F, Gregori, G, Venezia, A, Cucinotta, D, Pintaudi, B, Ragonese, F, Pata, P, Piatti, Pm, Luccotti, P, Orsi, E, Bonomo, M, Menicatti, L, Turco, Aa, Ciano, O, Vallefuoco, P, Corigliano, G, Pentangelo, C, Petraroli, E, Auletta, P, Carbonara, O, Capobianco, G, Caiazzo, G, Angiulli, B, De Simone, G, Michele, C, Mastrilli, V, Nunziata, G, Romano, G, Gaeta, I, Sorrentino, T, Iovine, C, Nappi, F, Paolisso, Giuseppe, Rizzo, Maria Rosaria, Avogaro, A, Vedovato, M, Lapolla, A, Sartore, G, Burlina, S, Chilelli, Nc, Galluzzoy, A, Giordano, C, Torregrossa, V, Arsenio, L, Dall'Aglio, E, Cioni, F, Babini, M, Moncastroppa, G, Perriello, G, Timi, A, Consoli, A, Ginestra, F, Zavaroni, D, Calzoni, F, Miccoli, R, Bianchi, C, Politi, S, Anichini, R, Tedeschi, A, Citro, G, Zampino, A, Rosa, S, Natale, M, Giocoli, Cl, Caruso, E, Tramontano, L, Imbroinise, A, Perna, Cd, Calabrese, M, Zogheri, A, Luberto, A, Ballardini, G, Babini, Ac, Zannoni, C, Pugliese, G, Salvi, L, Mazzitelli, G, Zappaterreno, A, Frontoni, S, Ventricini, A, Lauro, D, Galli, A, Rinaldi, Me, Leotta, S, Fontana, L, Goretti, S, Pozzilli, P, Leonetti, F, Morano, S, Filetti, S, Cosmo, Sd, Bacci, S, Palena, Ap, Calatola, P, Capuano, G, Amelia, U, Dotta, F, Guarino, E, Ceccarelli, E, Lalli, C, Scarponi, M, Forte, E, Potenziani, S, Perin, Pc, Marena, S, Zucco, C, Perotto, M, Bossi, A, Filopanti, M, Grimaldi, F, Tonutti, L, Cavarape, A, Cigolini, M, Pichiri, I, Brangani, C, Tomasetto, E, Capra, C, Cigolini M., Author information, Vaccaro, Olga, Masulli, Maria, Rivellese, ANGELA ALBAROSA, Riccardi, Gabriele, Giorda, C, Maggioni, A, Rivellese, A, Giorda, CB, Maggioni, AP, and Rivellese, AA

Subjects
Blood Glucose, Male, BIO/12 - BIOCHIMICA CLINICA E BIOLOGIA MOLECOLARE CLINICA, Endocrinology, Diabetes and Metabolism, pioglitazone, sulfonylurea, type 2 diabetes, metformin, cardiovascular events, Medicine (miscellaneous), Type 2 diabetes, Settore MED/13 - Endocrinologia, Body Mass Index, law.invention, Randomized controlled trial, Risk Factors, law, Surveys and Questionnaires, Cardiovascular Disease, pioglitazone, piogllitazone, Stroke, Diabetes, Therapy, Pioglitazone, Nutrition and Dietetics, Diabetes, Thiazolidinedione, cardiovascular events, Type 2 Diabetes Mellitus, sulphonylureas, Middle Aged, Metformin, Sulfonylurea Compound, Treatment Outcome, Tolerability, Cardiovascular Diseases, Drug Therapy, Combination, Female, type 2 diabetes, Cardiology and Cardiovascular Medicine, Human, medicine.drug, medicine.medical_specialty, Endpoint Determination, sulfonylurea, cardiovascualr event, Sudden death, Follow-Up Studie, Internal medicine, Diabetes mellitus, medicine, Humans, Hypoglycemic Agents, sulfonylureas, interventio trial, randomized controlled trial, Aged, Hypoglycemic Agent, Questionnaire, business.industry, Risk Factor, medicine.disease, Surgery, Sulfonylurea Compounds, Diabetes Mellitus, Type 2, Quality of Life, Thiazolidinediones, Therapy, business, metformin, Pioglitazone, Follow-Up Studies
Abstract

Background and aims Metformin is the first-line therapy in type 2 diabetes. In patients inadequately controlled with metformin, the addition of a sulfonylurea or pioglitazone are equally plausible options to improve glycemic control. However, these drugs have profound differences in their mechanism of action, side effects, and impact on cardiovascular risk factors. A formal comparison of these two therapies in terms of cardiovascular morbidity and mortality is lacking. The TOSCA.IT study was designed to explore the effects of adding pioglitazone or a sulfonylurea on cardiovascular events in type 2 diabetic patients inadequately controlled with metformin. Methods Multicentre, randomized, open label, parallel group trial of 48 month duration. Type 2 diabetic subjects, 50–75 years, BMI 20–45 Kg/m 2 , on secondary failure to metformin monotherapy will be randomized to add-on a sulfonylurea or pioglitazone. The primary efficacy outcome is a composite endpoint of all-cause mortality, nonfatal myocardial infarction, nonfatal stroke, and unplanned coronary revascularization. Principal secondary outcome is a composite ischemic endpoint of sudden death, fatal and non-fatal myocardial infarction and stroke, endovascular or surgical intervention on the coronary, leg or carotid arteries, major amputations. Side effects, quality of life and economic costs will also be evaluated. Efficacy, safety, tolerability, and study conduct will be monitored by an independent Data Safety Monitoring Board. End points will be adjudicated by an independent external committee. Conclusions TOSCA.IT is the first on-going study investigating the head-to-head comparison of adding a sulfonylurea or pioglitazone to existing metformin treatment in terms of hard cardiovascular outcomes. Registration: Clinicaltrials.gov ID NCT00700856.

Published
2012

23. C-reactive protein and 5-year survival in type 2 diabetes: the Casale Monferrato Study.

Author

Bruno G, Fornengo P, Novelli G, Panero F, Perotto M, Segre O, Zucco C, Deambrogio P, Bargero G, Perin PC, Bruno, Graziella, Fornengo, Paolo, Novelli, Giulia, Panero, Francesco, Perotto, Massimo, Segre, Olivia, Zucco, Chiara, Deambrogio, PierCarlo, Bargero, Giuseppe, and Perin, Paolo Cavallo

Abstract

Objective: To determine to what extent plasma C-reactive protein (CRP) values influence 5-year all-cause and cardiovascular mortality in type 2 diabetic individuals, independently of albumin excretion rate (AER) and other cardiovascular risk factors, and its incremental usefulness for predicting individual risk of mortality.Research Design and Methods: Measurements of CRP were performed in 2,381 of 3,249 (73.3%) subjects as part of the population-based Casale Monferrato Study. Its association with 5-year all-cause and cardiovascular mortality was assessed with multivariate Cox proportional hazards modeling. The C statistic and measures of calibration and global fit were also assessed.Results: Results are based on 496 deaths in 11.717 person-years of observations (median follow-up 5.4 years). With respect to subjects with CRP < or =3 mg/l, those with higher values had an adjusted hazard ratio (HR) of 1.51 (95% CI 1.18-1.92) for all-cause mortality and 1.44 (0.99-2.08) for cardiovascular mortality. In normoalbuminuric subjects, respective HRs of CRP were 1.56 (1.13-2.15) and 1.65 (1.00-2.74), AER being neither a modifier nor a confounder of CRP association. In analysis limited to diabetic subjects without cardiovascular disease (CVD), adjusted HRs were 1.67 (1.24-2.24) for all-cause mortality and 1.36 (0.83-2.24) for cardiovascular mortality. The improvement in individual risk assessment was marginal when measured with various statistical measures of model discrimination, calibration, and global fit.Conclusions: CRP measurement is independently associated with short-term mortality risk in type 2 diabetic individuals, even in normoalbuminuric subjects and in those without a previous diagnosis of CVD. Its clinical usefulness in individual assessment of 5-year risk of mortality, however, is limited. [ABSTRACT FROM AUTHOR]

Published
2009
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38. The Influence of Substituents on the Tautomerism of Symmetrically Substituted 2,2'-Bis-benzimidazoles

Author

Dall'Oglio Evandro, Caro Miguel B., Gesser José C., Zucco César, and Rezende Marcos C.

Subjects
bis-benzimidazoles, tautomerism, dynamic NMR, Chemistry, QD1-999
Abstract

The tautomerism of five symmetrically substituted 2,2'-bis-benzimidazoles [5(6),5'(6')-tetramethyl- (1); 5(6),5'(6')-dimethyl-(2); 5(6),5'(6')-dichloro- (3); 5(6),5'(6')-dimethoxy- (4) and 4(7),4'(7')-dimethyl-2,2'-bis-benzimidazole (5)], was studied by means of ¹H NMR spectroscopy at variable temperatures, and the influence of the substituents on the energy barriers for tautomeric interconversion was interpreted with the aid of theoretical calculations.

Published
2002

39. A new dinucleating N,O donor ligand (H2BPClNOL) and the structural and magnetic properties of two diiron complexes with the di-m-alkoxo motif

Author

Horn Jr. Adolfo, Neves Ademir, Vencato Ivo, Drago Valderes, Zucco César, Werner Rüdiger, and Haase Wolfgang

Subjects
binuclear FeIII complexes, X-ray analysis, magnetochemistry, Chemistry, QD1-999
Abstract

A new polidentade ligand, N-(2-hydroxybenzyl)-N-(2-pyridylmethyl) [(3-chloro)(2-hydroxy)]propylamine (H2BPClNOL), was synthesized and two Fe2III complexes containing the FeIII(m-alcoxo)2FeIII core were structurally and magnetically characterized. Complex [Fe2(BPClNOL)2(OAc)]ClO4 (1) differs from complex [Fe2(BPClNOL)2(H2O)2](ClO4)2.4H2O (2) by the presence of an acetate-bridge between the two iron centers. The complex with the acetate bridge is about 20 % less antiferromagnetically coupled than the complex without the acetate bridge.

Published
2000

40. [Untitled]

Author

Andrade Jailson B. de, Pinto Angelo C., Vieira Paulo Cezar, Cadore Solange, and Zucco Cezar

Subjects
Chemistry, QD1-999
Published
2003

44. Investigating Topic Modeling Techniques to Extract Meaningful Insights in Italian Long COVID Narration.

Author

Scarpino I, Zucco C, Vallelunga R, Luzza F, and Cannataro M

Abstract

Through an adequate survey of the history of the disease, Narrative Medicine (NM) aims to allow the definition and implementation of an effective, appropriate, and shared treatment path. In the present study different topic modeling techniques are compared, as Latent Dirichlet Allocation (LDA) and topic modeling based on BERT transformer, to extract meaningful insights in the Italian narration of COVID-19 pandemic. In particular, the main focus was the characterization of Post-acute Sequelae of COVID-19, (i.e., PASC) writings as opposed to writings by health professionals and general reflections on COVID-19, (i.e., non-PASC) writings, modeled as a semi-supervised task. The results show that the BERTopic-based approach outperforms the LDA-base approach by grouping in the same cluster the 97.26% of analyzed documents, and reaching an overall accuracy of 91.97%.

Published
2022
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45. An Extensive Assessment of Network Embedding in PPI Network Alignment.

Author

Milano M, Zucco C, Settino M, and Cannataro M

Abstract

Network alignment is a fundamental task in network analysis. In the biological field, where the protein-protein interaction (PPI) is represented as a graph, network alignment allowed the discovery of underlying biological knowledge such as conserved evolutionary pathways and functionally conserved proteins throughout different species. A recent trend in network science concerns network embedding, i.e., the modelling of nodes in a network as a low-dimensional feature vector. In this survey, we present an overview of current PPI network embedding alignment methods, a comparison among them, and a comparison to classical PPI network alignment algorithms. The results of this comparison highlight that: (i) only five network embeddings for network alignment algorithms have been applied in the biological context, whereas the literature presents several classical network alignment algorithms; (ii) there is a need for developing an evaluation framework that may enable a unified comparison between different algorithms; (iii) the majority of the proposed algorithms perform network embedding through matrix factorization-based techniques; (iv) three out of five algorithms leverage external biological resources, while the remaining two are designed for domain agnostic network alignment and tested on PPI networks; (v) two algorithms out of three are stated to perform multi-network alignment, while the remaining perform pairwise network alignment.

Published
2022
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46. International Comparisons of Harmonized Laboratory Value Trajectories to Predict Severe COVID-19: Leveraging the 4CE Collaborative Across 342 Hospitals and 6 Countries: A Retrospective Cohort Study.

Author

Weber GM, Hong C, Palmer NP, Avillach P, Murphy SN, Gutiérrez-Sacristán A, Xia Z, Serret-Larmande A, Neuraz A, Omenn GS, Visweswaran S, Klann JG, South AM, Loh NHW, Cannataro M, Beaulieu-Jones BK, Bellazzi R, Agapito G, Alessiani M, Aronow BJ, Bell DS, Bellasi A, Benoit V, Beraghi M, Boeker M, Booth J, Bosari S, Bourgeois FT, Brown NW, Bucalo M, Chiovato L, Chiudinelli L, Dagliati A, Devkota B, DuVall SL, Follett RW, Ganslandt T, García Barrio N, Gradinger T, Griffier R, Hanauer DA, Holmes JH, Horki P, Huling KM, Issitt RW, Jouhet V, Keller MS, Kraska D, Liu M, Luo Y, Lynch KE, Malovini A, Mandl KD, Mao C, Maram A, Matheny ME, Maulhardt T, Mazzitelli M, Milano M, Moore JH, Morris JS, Morris M, Mowery DL, Naughton TP, Ngiam KY, Norman JB, Patel LP, Pedrera Jimenez M, Ramoni RB, Schriver ER, Scudeller L, Sebire NJ, Serrano Balazote P, Spiridou A, Tan AL, Tan BW, Tibollo V, Torti C, Trecarichi EM, Vitacca M, Zambelli A, Zucco C, Kohane IS, Cai T, and Brat GA

Abstract

Objectives: To perform an international comparison of the trajectory of laboratory values among hospitalized patients with COVID-19 who develop severe disease and identify optimal timing of laboratory value collection to predict severity across hospitals and regions., Design: Retrospective cohort study., Setting: The Consortium for Clinical Characterization of COVID-19 by EHR (4CE), an international multi-site data-sharing collaborative of 342 hospitals in the US and in Europe., Participants: Patients hospitalized with COVID-19, admitted before or after PCR-confirmed result for SARS-CoV-2., Primary and Secondary Outcome Measures: Patients were categorized as "ever-severe" or "never-severe" using the validated 4CE severity criteria. Eighteen laboratory tests associated with poor COVID-19-related outcomes were evaluated for predictive accuracy by area under the curve (AUC), compared between the severity categories. Subgroup analysis was performed to validate a subset of laboratory values as predictive of severity against a published algorithm. A subset of laboratory values (CRP, albumin, LDH, neutrophil count, D-dimer, and procalcitonin) was compared between North American and European sites for severity prediction., Results: Of 36,447 patients with COVID-19, 19,953 (43.7%) were categorized as ever-severe. Most patients (78.7%) were 50 years of age or older and male (60.5%). Longitudinal trajectories of CRP, albumin, LDH, neutrophil count, D-dimer, and procalcitonin showed association with disease severity. Significant differences of laboratory values at admission were found between the two groups. With the exception of D-dimer, predictive discrimination of laboratory values did not improve after admission. Sub-group analysis using age, D-dimer, CRP, and lymphocyte count as predictive of severity at admission showed similar discrimination to a published algorithm (AUC=0.88 and 0.91, respectively). Both models deteriorated in predictive accuracy as the disease progressed. On average, no difference in severity prediction was found between North American and European sites., Conclusions: Laboratory test values at admission can be used to predict severity in patients with COVID-19. Prediction models show consistency across international sites highlighting the potential generalizability of these models., Competing Interests: COMPETING INTEREST STATEMENT There are no competing interests to report.

Published
2021
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47. COVID-19 Community Temporal Visualizer: a new methodology for the network-based analysis and visualization of COVID-19 data.

Author

Milano M, Zucco C, and Cannataro M

Abstract

Understanding the evolution of the spread of the COVID-19 pandemic requires the analysis of several data at the spatial and temporal levels. Here, we present a new network-based methodology to analyze COVID-19 data measures containing spatial and temporal features and its application on a real dataset. The goal of the methodology is to analyze sets of homogeneous datasets (i.e. COVID-19 data taken in different periods and in several regions) using a statistical test to find similar/dissimilar datasets, mapping such similarity information on a graph and then using a community detection algorithm to visualize and analyze the spatio-temporal evolution of data. We evaluated diverse Italian COVID-19 data made publicly available by the Italian Protezione Civile Department at https://github.com/pcm-dpc/COVID-19/. Furthermore, we considered the climate data related to two periods and we integrated them with COVID-19 data measures to detect new communities related to climate changes. In conclusion, the application of the proposed methodology provides a network-based representation of the COVID-19 measures by highlighting the different behaviour of regions with respect to pandemics data released by Protezione Civile and climate data. The methodology and its implementation as R function are publicly available at https://github.com/mmilano87/analyzeC19D., Competing Interests: Conflict of interestThe authors declare that they have not conflict of interests., (© The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2021.)

Published
2021
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48. COVID-WAREHOUSE: A Data Warehouse of Italian COVID-19, Pollution, and Climate Data.

Author

Agapito G, Zucco C, and Cannataro M

Subjects
COVID-19, Coronavirus Infections virology, Environmental Pollution, Humans, Italy, Pneumonia, Viral virology, Public Health, SARS-CoV-2, Wind, Betacoronavirus isolation & purification, Climate, Coronavirus Infections epidemiology, Data Warehousing, Pandemics, Pneumonia, Viral epidemiology
Abstract

The management of the COVID-19 pandemic presents several unprecedented challenges in different fields, from medicine to biology, from public health to social science, that may benefit from computing methods able to integrate the increasing available COVID-19 and related data (e.g., pollution, demographics, climate, etc.). With the aim to face the COVID-19 data collection, harmonization and integration problems, we present the design and development of COVID-WAREHOUSE, a data warehouse that models, integrates and stores the COVID-19 data made available daily by the Italian Protezione Civile Department and several pollution and climate data made available by the Italian Regions. After an automatic ETL (Extraction, Transformation and Loading) step, COVID-19 cases, pollution measures and climate data, are integrated and organized using the Dimensional Fact Model, using two main dimensions: time and geographical location. COVID-WAREHOUSE supports OLAP (On-Line Analytical Processing) analysis, provides a heatmap visualizer, and allows easy extraction of selected data for further analysis. The proposed tool can be used in the context of Public Health to underline how the pandemic is spreading, with respect to time and geographical location, and to correlate the pandemic to pollution and climate data in a specific region. Moreover, public decision-makers could use the tool to discover combinations of pollution and climate conditions correlated to an increase of the pandemic, and thus, they could act in a consequent manner. Case studies based on data cubes built on data from Lombardia and Puglia regions are discussed. Our preliminary findings indicate that COVID-19 pandemic is significantly spread in regions characterized by high concentration of particulate in the air and the absence of rain and wind, as even stated in other works available in literature.

Published
2020
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49. A Comprehensive Machine-Learning-Based Software Pipeline to Classify EEG Signals: A Case Study on PNES vs. Control Subjects.

Author

Varone G, Gasparini S, Ferlazzo E, Ascoli M, Tripodi GG, Zucco C, Calabrese B, Cannataro M, and Aguglia U

Subjects
Algorithms, Humans, Seizures physiopathology, Support Vector Machine, Electroencephalography methods, Machine Learning, Seizures diagnosis, Software
Abstract

The diagnosis of psychogenic nonepileptic seizures (PNES) by means of electroencephalography (EEG) is not a trivial task during clinical practice for neurologists. No clear PNES electrophysiological biomarker has yet been found, and the only tool available for diagnosis is video EEG monitoring with recording of a typical episode and clinical history of the subject. In this paper, a data-driven machine learning (ML) pipeline for classifying EEG segments (i.e., epochs) of PNES and healthy controls (CNT) is introduced. This software pipeline consists of a semiautomatic signal processing technique and a supervised ML classifier to aid clinical discriminative diagnosis of PNES by means of an EEG time series. In our ML pipeline, statistical features like the mean, standard deviation, kurtosis, and skewness are extracted in a power spectral density (PSD) map split up in five conventional EEG rhythms (delta, theta, alpha, beta, and the whole band, i.e., 1-32 Hz). Then, the feature vector is fed into three different supervised ML algorithms, namely, the support vector machine (SVM), linear discriminant analysis (LDA), and Bayesian network (BN), to perform EEG segment classification tasks for CNT vs. PNES. The performance of the pipeline algorithm was evaluated on a dataset of 20 EEG signals (10 PNES and 10 CNT) that was recorded in eyes-closed resting condition at the Regional Epilepsy Centre, Great Metropolitan Hospital of Reggio Calabria, University of Catanzaro, Italy. The experimental results showed that PNES vs. CNT discrimination tasks performed via the ML algorithm and validated with random split (RS) achieved an average accuracy of 0.97 ± 0.013 (RS-SVM), 0.99 ± 0.02 (RS-LDA), and 0.82 ± 0.109 (RS-BN). Meanwhile, with leave-one-out (LOO) validation, an average accuracy of 0.98 ± 0.0233 (LOO-SVM), 0.98 ± 0.124 (LOO-LDA), and 0.81 ± 0.109 (LOO-BN) was achieved. Our findings showed that BN was outperformed by SVM and LDA. The promising results of the proposed software pipeline suggest that it may be a valuable tool to support existing clinical diagnosis., Competing Interests: All the authors declare no conflict of interest.

Published
2020
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50. Oral Hypoglycemic Drugs: Pathophysiological Basis of Their Mechanism of ActionOral Hypoglycemic Drugs: Pathophysiological Basis of Their Mechanism of Action.

Author

Lorenzati B, Zucco C, Miglietta S, Lamberti F, and Bruno G

Abstract

Type 2 diabetes is a syndrome characterized by relative insulin deficiency, insulin resistance and increased hepatic glucose output. Medications used to treat the disease are designed to correct one or more of these metabolic abnormalities. Current recommendations of the American Diabetes Association (ADA) and European Association for the Study of Diabetes (EASD) include diet and exercise as first-line therapy plus hypoglycemic drugs. Actually there are seven distinct classes of anti-hyperglicemic agents, each of them displaying unique pharmacologic properties. The aim of this review is to describe the pathophysiological basis of their mechanism of action, a necessary step to individualize treatment of diabetic people, taking into proper consideration potential benefits and secondary effects of drugs.

Published
2010
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