Search

Your search keyword '"Ubezio M"' showing total 3 results
Start Over Author "Ubezio M" Publisher country:us
3 results on '"Ubezio M"'

1. Synthetic Data Generation by Artificial Intelligence to Accelerate Research and Precision Medicine in Hematology

Author

D'Amico, S, Dall'Olio, D, Sala, C, Dall'Olio, L, Sauta, E, Zampini, M, Asti, G, Lanino, L, Maggioni, G, Campagna, A, Ubezio, M, Russo, A, Bicchieri, M, Riva, E, Tentori, C, Travaglino, E, Morandini, P, Savevski, V, Santoro, A, Prada-Luengo, I, Krogh, A, Santini, V, Kordasti, S, Platzbecker, U, Diez-Campelo, M, Fenaux, P, Haferlach, T, Castellani, G, Della Porta, M, D'Amico S., Dall'Olio D., Sala C., Dall'Olio L., Sauta E., Zampini M., Asti G., Lanino L., Maggioni G., Campagna A., Ubezio M., Russo A., Bicchieri M. E., Riva E., Tentori C. A., Travaglino E., Morandini P., Savevski V., Santoro A., Prada-Luengo I., Krogh A., Santini V., Kordasti S., Platzbecker U., Diez-Campelo M., Fenaux P., Haferlach T., Castellani G., Della Porta M. G., D'Amico, S, Dall'Olio, D, Sala, C, Dall'Olio, L, Sauta, E, Zampini, M, Asti, G, Lanino, L, Maggioni, G, Campagna, A, Ubezio, M, Russo, A, Bicchieri, M, Riva, E, Tentori, C, Travaglino, E, Morandini, P, Savevski, V, Santoro, A, Prada-Luengo, I, Krogh, A, Santini, V, Kordasti, S, Platzbecker, U, Diez-Campelo, M, Fenaux, P, Haferlach, T, Castellani, G, Della Porta, M, D'Amico S., Dall'Olio D., Sala C., Dall'Olio L., Sauta E., Zampini M., Asti G., Lanino L., Maggioni G., Campagna A., Ubezio M., Russo A., Bicchieri M. E., Riva E., Tentori C. A., Travaglino E., Morandini P., Savevski V., Santoro A., Prada-Luengo I., Krogh A., Santini V., Kordasti S., Platzbecker U., Diez-Campelo M., Fenaux P., Haferlach T., Castellani G., and Della Porta M. G.

Abstract

PURPOSE: Synthetic data are artificial data generated without including any real patient information by an algorithm trained to learn the characteristics of a real source data set and became widely used to accelerate research in life sciences. We aimed to (1) apply generative artificial intelligence to build synthetic data in different hematologic neoplasms; (2) develop a synthetic validation framework to assess data fidelity and privacy preservability; and (3) test the capability of synthetic data to accelerate clinical/translational research in hematology. METHODS: A conditional generative adversarial network architecture was implemented to generate synthetic data. Use cases were myelodysplastic syndromes (MDS) and AML: 7,133 patients were included. A fully explainable validation framework was created to assess fidelity and privacy preservability of synthetic data. RESULTS: We generated MDS/AML synthetic cohorts (including information on clinical features, genomics, treatment, and outcomes) with high fidelity and privacy performances. This technology allowed resolution of lack/incomplete information and data augmentation. We then assessed the potential value of synthetic data on accelerating research in hematology. Starting from 944 patients with MDS available since 2014, we generated a 300% augmented synthetic cohort and anticipated the development of molecular classification and molecular scoring system obtained many years later from 2,043 to 2,957 real patients, respectively. Moreover, starting from 187 MDS treated with luspatercept into a clinical trial, we generated a synthetic cohort that recapitulated all the clinical end points of the study. Finally, we developed a website to enable clinicians generating high-quality synthetic data from an existing biobank of real patients. CONCLUSION: Synthetic data mimic real clinical-genomic features and outcomes, and anonymize patient information. The implementation of this technology allows to increase the scientific

Published
2023

2. Clinical relevance of clonal hematopoiesis in persons aged ≥80 years

Author

Rossi, M, Meggendorfer, M, Zampini, M, Tettamanti, M, Riva, E, Travaglino, E, Bersanelli, M, Mandelli, S, Galbussera, A, Mosca, E, Saba, E, Chiereghin, C, Manes, N, Milanesi, C, Ubezio, M, Morabito, L, Peano, C, Soldà, G, Asselta, R, Duga, S, Selmi, C, De Santis, M, Malik, K, Maggioni, G, Bicchieri, M, Campagna, A, Tentori, C, Russo, A, Civilini, E, Allavena, P, Piazza, R, Corrao, G, Sala, C, Termanini, A, Giordano, L, Detoma, P, Malabaila, A, Sala, L, Rosso, S, Zanetti, R, Saitta, C, Condorelli, G, Passamonti, F, Santoro, A, Sole, F, Platzbecker, U, Fenaux, P, Bolli, N, Castellani, G, Kern, W, Vassiliou, G, Haferlach, T, Lucca, U, Della Porta, M, Rossi, Marianna, Meggendorfer, Manja, Zampini, Matteo, Tettamanti, Mauro, Riva, Emma, Travaglino, Erica, Bersanelli, Matteo, Mandelli, Sara, Galbussera, Alessia Antonella, Mosca, Ettore, Saba, Elena, Chiereghin, Chiara, Manes, Nicla, Milanesi, Chiara, Ubezio, Marta, Morabito, Lucio, Peano, Clelia, Soldà, Giulia, Asselta, Rosanna, Duga, Stefano, Selmi, Carlo, De Santis, Maria, Malik, Karolina, Maggioni, Giulia, Bicchieri, Maria Elena, Campagna, Alessia, Tentori, Cristina Astrid, Russo, Antonio, Civilini, Efrem, Allavena, Paola, Piazza, Rocco, Corrao, Giovanni, Sala, Claudia, Termanini, Alberto, Giordano, Laura, Detoma, Paolo, Malabaila, Aurelio, Sala, Luca, Rosso, Stefano, Zanetti, Roberto, Saitta, Claudia, Riva, Elena, Condorelli, Gianluigi, Passamonti, Francesco, Santoro, Armando, Sole, Francesc, Platzbecker, Uwe, Fenaux, Pierre, Bolli, Niccolo, Castellani, Gastone, Kern, Wolfgang, Vassiliou, George, Haferlach, Torsten, Lucca, Ugo, Della Porta, Matteo G, Rossi, M, Meggendorfer, M, Zampini, M, Tettamanti, M, Riva, E, Travaglino, E, Bersanelli, M, Mandelli, S, Galbussera, A, Mosca, E, Saba, E, Chiereghin, C, Manes, N, Milanesi, C, Ubezio, M, Morabito, L, Peano, C, Soldà, G, Asselta, R, Duga, S, Selmi, C, De Santis, M, Malik, K, Maggioni, G, Bicchieri, M, Campagna, A, Tentori, C, Russo, A, Civilini, E, Allavena, P, Piazza, R, Corrao, G, Sala, C, Termanini, A, Giordano, L, Detoma, P, Malabaila, A, Sala, L, Rosso, S, Zanetti, R, Saitta, C, Condorelli, G, Passamonti, F, Santoro, A, Sole, F, Platzbecker, U, Fenaux, P, Bolli, N, Castellani, G, Kern, W, Vassiliou, G, Haferlach, T, Lucca, U, Della Porta, M, Rossi, Marianna, Meggendorfer, Manja, Zampini, Matteo, Tettamanti, Mauro, Riva, Emma, Travaglino, Erica, Bersanelli, Matteo, Mandelli, Sara, Galbussera, Alessia Antonella, Mosca, Ettore, Saba, Elena, Chiereghin, Chiara, Manes, Nicla, Milanesi, Chiara, Ubezio, Marta, Morabito, Lucio, Peano, Clelia, Soldà, Giulia, Asselta, Rosanna, Duga, Stefano, Selmi, Carlo, De Santis, Maria, Malik, Karolina, Maggioni, Giulia, Bicchieri, Maria Elena, Campagna, Alessia, Tentori, Cristina Astrid, Russo, Antonio, Civilini, Efrem, Allavena, Paola, Piazza, Rocco, Corrao, Giovanni, Sala, Claudia, Termanini, Alberto, Giordano, Laura, Detoma, Paolo, Malabaila, Aurelio, Sala, Luca, Rosso, Stefano, Zanetti, Roberto, Saitta, Claudia, Riva, Elena, Condorelli, Gianluigi, Passamonti, Francesco, Santoro, Armando, Sole, Francesc, Platzbecker, Uwe, Fenaux, Pierre, Bolli, Niccolo, Castellani, Gastone, Kern, Wolfgang, Vassiliou, George, Haferlach, Torsten, Lucca, Ugo, and Della Porta, Matteo G

Abstract

Clonal hematopoiesis of indeterminate potential (CHIP) is associated with increased risk of cancers and inflammation-related diseases. This phenomenon becomes common in persons aged ≥80 years, in whom the implications of CHIP are not well defined. We performed a mutational screening in 1794 persons aged ≥80 years and investigated the relationships between CHIP and associated pathologies. Mutations were observed in one-third of persons aged ≥80 years and were associated with reduced survival. Mutations in JAK2 and splicing genes, multiple mutations (DNMT3A, TET2, and ASXL1 with additional genetic lesions), and variant allele frequency ≥0.096 had positive predictive value for myeloid neoplasms. Combining mutation profiles with abnormalities in red blood cell indices improved the ability of myeloid neoplasm prediction. On this basis, we defined a predictive model that identifies 3 risk groups with different probabilities of developing myeloid neoplasms. Mutations in DNMT3A, TET2, ASXL1, or JAK2 were associated with coronary heart disease and rheumatoid arthritis. Cytopenia was common in persons aged ≥80 years, with the underlying cause remaining unexplained in 30% of cases. Among individuals with unexplained cytopenia, the presence of highly specific mutation patterns was associated with myelodysplastic-like phenotype and a probability of survival comparable to that of myeloid neoplasms. Accordingly, 7.5% of subjects aged ≥80 years with cytopenia had presumptive evidence of myeloid neoplasm. In summary, specific mutational patterns define different risk of developing myeloid neoplasms vs inflammatory-associated diseases in persons aged ≥80 years. In individuals with unexplained cytopenia, mutational status may identify those subjects with presumptive evidence of myeloid neoplasms.

Published
2021

3. Clinical Features Associated with Delirium Motor Subtypes in Older Inpatients: Results of a Multicenter Study

Author

Morandi, A, Di Santo, S, Cherubini, A, Mossello, E, Meagher, D, Mazzone, A, Bianchetti, A, Ferrara, N, Ferrari, A, Musicco, M, Trabucchi, M, Bellelli, G, Boffelli, S, Di Stefano, F, De Filippi, F, Guerini, F, Bertoletti, E, March, A, Margiotta, A, Mecocci, P, Addesi, D, Fanto, F, Isaia, G, Dijik, B, Porrino, P, Cotroneo, A, Galli, G, Bruni, A, Bernardini, B, Corsini, C, Cagnin, A, Zurlo, A, Barbagallo, G, Lunardelli, M, Martini, E, Battaglia, G, Latella, R, Petritola, D, Sinforiani, E, Cester, A, Formilan, M, Carbone, P, Appollonio, I, Cereda, D, Tremolizzo, L, Bottacchi, E, Lucchetti, L, Mariani, C, Rapazzini, P, Romanelli, G, Marengoni, A, Zuliani, G, Bianchi, L, Suardi, T, Muti, E, Bottura, R, Sgro, G, Mandas, A, Serchisu, L, Crippa, P, Ivaldi, C, Ungar, A, Villani, D, Raimondi, C, Mussi, C, Provenzano, G, Mari, D, Odetti, P, Monacelli, F, Incalzi, R, Pluderi, A, Bellamoli, C, Terranova, L, Scarpini, E, D'Amico, F, Cavallini, M, Guerrini, G, Scotuzzi, A, Chiarello, A, Pilotto, A, Tognini, S, Dell'Aquila, G, Toigo, G, Ceschia, G, Piccinini, M, Fabbo, A, Zoli, M, Forti, P, Wenter, C, Basile, G, Lasagni, A, Padovani, A, Rozzini, L, Cottino, M, Vitali, S, Tripi, G, Avanzi, S, Umidi, S, Moretti, D, Ruotolo, G, Boschi, F, Bonino, P, Marchionni, N, Fascendini, S, Noro, G, Turco, R, Ubezio, M, Serrati, C, Infante, M, Gentile, S, Pernigotti, L, Biagini, C, Canonico, E, Bonati, P, Gareri, P, Caffarra, P, Ceretti, A, Castiglia, R, Gabelli, C, Storto, M, Putzu, P, Rozzini, R, Zanetti, E, Bo, M, Morandi, A, Di Santo, S, Cherubini, A, Mossello, E, Meagher, D, Mazzone, A, Bianchetti, A, Ferrara, N, Ferrari, A, Musicco, M, Trabucchi, M, Bellelli, G, Boffelli, S, Di Stefano, F, De Filippi, F, Guerini, F, Bertoletti, E, March, A, Margiotta, A, Mecocci, P, Addesi, D, Fanto, F, Isaia, G, Dijik, B, Porrino, P, Cotroneo, A, Galli, G, Bruni, A, Bernardini, B, Corsini, C, Cagnin, A, Zurlo, A, Barbagallo, G, Lunardelli, M, Martini, E, Battaglia, G, Latella, R, Petritola, D, Sinforiani, E, Cester, A, Formilan, M, Carbone, P, Appollonio, I, Cereda, D, Tremolizzo, L, Bottacchi, E, Lucchetti, L, Mariani, C, Rapazzini, P, Romanelli, G, Marengoni, A, Zuliani, G, Bianchi, L, Suardi, T, Muti, E, Bottura, R, Sgro, G, Mandas, A, Serchisu, L, Crippa, P, Ivaldi, C, Ungar, A, Villani, D, Raimondi, C, Mussi, C, Provenzano, G, Mari, D, Odetti, P, Monacelli, F, Incalzi, R, Pluderi, A, Bellamoli, C, Terranova, L, Scarpini, E, D'Amico, F, Cavallini, M, Guerrini, G, Scotuzzi, A, Chiarello, A, Pilotto, A, Tognini, S, Dell'Aquila, G, Toigo, G, Ceschia, G, Piccinini, M, Fabbo, A, Zoli, M, Forti, P, Wenter, C, Basile, G, Lasagni, A, Padovani, A, Rozzini, L, Cottino, M, Vitali, S, Tripi, G, Avanzi, S, Umidi, S, Moretti, D, Ruotolo, G, Boschi, F, Bonino, P, Marchionni, N, Fascendini, S, Noro, G, Turco, R, Ubezio, M, Serrati, C, Infante, M, Gentile, S, Pernigotti, L, Biagini, C, Canonico, E, Bonati, P, Gareri, P, Caffarra, P, Ceretti, A, Castiglia, R, Gabelli, C, Storto, M, Putzu, P, Rozzini, R, Zanetti, E, and Bo, M

Abstract

Objective To date motor subtypes of delirium have been evaluated in single-center studies with a limited examination of the relationship between predisposing factors and motor profile of delirium. We sought to report the prevalence and clinical profile of subtypes of delirium in a multicenter study. Methods This is a point prevalence study nested in the “Delirium Day 2015”, which included 108 acute and 12 rehabilitation wards in Italy. Delirium was detected using the 4-AT and motor subtypes were measured with the Delirium Motor Subtype Scale (DMSS). A multinomial logistic regression was used to determine the factors associated with delirium subtypes. Results Of 429 patients with delirium, the DMSS was completed in 275 (64%), classifying 21.5% of the patients with hyperactive delirium, 38.5% with hypoactive, 27.3% with mixed and 12.7% with the non-motor subtype. The 4-AT score was higher in the hyperactive subtype, similar in the hypoactive, mixed subtypes, while it was lowest in the non-motor subtype. Dementia was associated with all three delirium motor subtypes (hyperactive, OR 3.3, 95% CI: 1.2-8.7; hypoactive, OR 2.8, 95% CI: 1.2-6.5; mixed OR 2.6, 95% CI: 1.1-6.2). Atypical antipsychotics were associated with hypoactive delirium (OR 0.23, 95% CI: 0.1-0.7), while intravenous lines were associated with mixed delirium (OR 2.9, 95% CI: 1.2-6.9). Conclusions The study shows that hypoactive delirium is the most common subtype among hospitalized older patients. Specific clinical features were associated with different delirium subtypes. The use of standardized instruments can help to characterize the phenomenology of different motor subtypes of delirium.

Published
2017

Catalog

Books, media, physical & digital resources
See catalog results