Search

Your search keyword '"Pardini, Matteo"' showing total 922 results
Start Over Author "Pardini, Matteo"
922 results on '"Pardini, Matteo"'

4. Clinical validity of the Italian adaptation of the Uniform Data Set Neuropsychological Test Battery (I-UDSNB) in Mild Cognitive Impairment and Alzheimer’s Disease

Author

Conca, Francesca, Esposito, Valentina, Catricalà, Eleonora, Manenti, Rosa, L’Abbate, Federica, Quaranta, Davide, Giuffrè, Guido Maria, Rossetto, Federica, Solca, Federica, Orso, Beatrice, Inguscio, Emanuela, Crepaldi, Valeria, De Matteis, Maddalena, Rotondo, Emanuela, Manera, Marina, Caruso, Giulia, Catania, Valentina, Canu, Elisa, Rundo, Francesco, Cotta Ramusino, Matteo, Filippi, Massimo, Fundarò, Cira, Piras, Federica, Arighi, Andrea, Tiraboschi, Pietro, Stanzani Maserati, Michelangelo, Pardini, Matteo, Poletti, Barbara, Silani, Vincenzo, Marra, Camillo, Di Tella, Sonia, Cotelli, Maria, Lodi, Raffaele, Tagliavini, Fabrizio, and Cappa, Stefano Francesco

Published
2024
Full Text
View/download PDF

5. Cerebrospinal fluid NPTX2 changes and relationship with regional brain metabolism metrics across mild cognitive impairment due to Alzheimer's disease

Author

Massa, Federico, Martinuzzo, Caterina, Gómez de San José, Nerea, Pelagotti, Virginia, Kreshpa, Wendy, Abu-Rumeileh, Samir, Barba, Lorenzo, Mattioli, Pietro, Orso, Beatrice, Brugnolo, Andrea, Girtler, Nicola, Vigo, Tiziana, Arnaldi, Dario, Serrati, Carlo, Uccelli, Antonio, Morbelli, Silvia, Chincarini, Andrea, Otto, Markus, and Pardini, Matteo

Published
2024
Full Text
View/download PDF

6. Predicting disease severity in multiple sclerosis using multimodal data and machine learning

Author

Andorra, Magi, Freire, Ana, Zubizarreta, Irati, de Rosbo, Nicole Kerlero, Bos, Steffan D., Rinas, Melanie, Høgestøl, Einar A., de Rodez Benavent, Sigrid A., Berge, Tone, Brune-Ingebretse, Synne, Ivaldi, Federico, Cellerino, Maria, Pardini, Matteo, Vila, Gemma, Pulido-Valdeolivas, Irene, Martinez-Lapiscina, Elena H., Llufriu, Sara, Saiz, Albert, Blanco, Yolanda, Martinez-Heras, Eloy, Solana, Elisabeth, Bäcker-Koduah, Priscilla, Behrens, Janina, Kuchling, Joseph, Asseyer, Susanna, Scheel, Michael, Chien, Claudia, Zimmermann, Hanna, Motamedi, Seyedamirhosein, Kauer-Bonin, Josef, Brandt, Alex, Saez-Rodriguez, Julio, Alexopoulos, Leonidas G., Paul, Friedemann, Harbo, Hanne F., Shams, Hengameh, Oksenberg, Jorge, Uccelli, Antonio, Baeza-Yates, Ricardo, and Villoslada, Pablo

Published
2024
Full Text
View/download PDF

7. Metabolic network alterations as a supportive biomarker in dementia with Lewy bodies with preserved dopamine transmission

Author

Stockbauer, Anna, Beyer, Leonie, Huber, Maria, Kreuzer, Annika, Palleis, Carla, Katzdobler, Sabrina, Rauchmann, Boris-Stephan, Morbelli, Silvia, Chincarini, Andrea, Bruffaerts, Rose, Vandenberghe, Rik, Kramberger, Milica G., Trost, Maja, Garibotto, Valentina, Nicastro, Nicolas, Lathuilière, Aurélien, Lemstra, Afina W., van Berckel, Bart N. M., Pilotto, Andrea, Padovani, Alessandro, Ochoa-Figueroa, Miguel A., Davidsson, Anette, Camacho, Valle, Peira, Enrico, Bauckneht, Matteo, Pardini, Matteo, Sambuceti, Gianmario, Aarsland, Dag, Nobili, Flavio, Gross, Mattes, Vöglein, Jonathan, Perneczky, Robert, Pogarell, Oliver, Buerger, Katharina, Franzmeier, Nicolai, Danek, Adrian, Levin, Johannes, Höglinger, Günter U., Bartenstein, Peter, Cumming, Paul, Rominger, Axel, and Brendel, Matthias

Published
2024
Full Text
View/download PDF

8. Validation of the REM behaviour disorder phenoconversion-related pattern in an independent cohort

Author

Orso, Beatrice, Mattioli, Pietro, Yoon, Eun-Jin, Kim, Yu Kyeong, Kim, Heejung, Shin, Jung Hwan, Kim, Ryul, Liguori, Claudio, Famà, Francesco, Donniaquio, Andrea, Massa, Federico, García, David Vállez, Meles, Sanne K., Leenders, Klaus L., Chiaravalloti, Agostino, Pardini, Matteo, Bauckneht, Matteo, Morbelli, Silvia, Nobili, Flavio, Lee, Jee-Young, and Arnaldi, Dario

Published
2023
Full Text
View/download PDF

9. Correction: Italian adaptation of the Uniform Data Set Neuropsychological Test Battery (I‑UDSNB 1.0): development and normative data

Author

Conca, Francesca, Esposito, Valentina, Rundo, Francesco, Quaranta, Davide, Muscio, Cristina, Manenti, Rosa, Caruso, Giulia, Lucca, Ugo, Galbussera, Alessia Antonella, Di Tella, Sonia, Baglio, Francesca, L’Abbate, Federica, Canu, Elisa, Catania, Valentina, Filippi, Massimo, Mattavelli, Giulia, Poletti, Barbara, Silani, Vincenzo, Lodi, Raffaele, De Matteis, Maddalena, Maserati, Michelangelo Stanzani, Arighi, Andrea, Rotondo, Emanuela, Tanzilli, Antonio, Pace, Andrea, Garramone, Federica, Cavaliere, Carlo, Pardini, Matteo, Rizzetto, Cristiano, Sorbi, Sandro, Perri, Roberta, Tiraboschi, Pietro, Canessa, Nicola, Cotelli, Maria, Ferri, Raffaele, Weintraub, Sandra, Marra, Camillo, Tagliavini, Fabrizio, Catricalà, Eleonora, and Cappa, Stefano Francesco

Published
2023
Full Text
View/download PDF

11. Different z-score cut-offs for striatal binding ratio (SBR) of DaT SPECT are needed to support the diagnosis of Parkinson’s Disease (PD) and dementia with Lewy bodies (DLB)

Author

Lanfranchi, Francesco, Arnaldi, Dario, Miceli, Alberto, Mattioli, Pietro, D’Amico, Francesca, Raffa, Stefano, Donegani, Maria Isabella, Chiola, Silvia, Massa, Federico, Pardini, Matteo, Di Raimondo, Tania, Sambuceti, Gianmario, Bauckneht, Matteo, Nobili, Flavio, and Morbelli, Silvia

Published
2023
Full Text
View/download PDF

12. The reliability of a deep learning model in clinical out-of-distribution MRI data: a multicohort study

Author

Mårtensson, Gustav, Ferreira, Daniel, Granberg, Tobias, Cavallin, Lena, Oppedal, Ketil, Padovani, Alessandro, Rektorova, Irena, Bonanni, Laura, Pardini, Matteo, Kramberger, Milica, Taylor, John-Paul, Hort, Jakub, Snædal, Jón, Kulisevsky, Jaime, Blanc, Frederic, Antonini, Angelo, Mecocci, Patrizia, Vellas, Bruno, Tsolaki, Magda, Kłoszewska, Iwona, Soininen, Hilkka, Lovestone, Simon, Simmons, Andrew, Aarsland, Dag, and Westman, Eric

Subjects
Physics - Medical Physics, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing, Quantitative Biology - Quantitative Methods, Statistics - Machine Learning
Abstract

Deep learning (DL) methods have in recent years yielded impressive results in medical imaging, with the potential to function as clinical aid to radiologists. However, DL models in medical imaging are often trained on public research cohorts with images acquired with a single scanner or with strict protocol harmonization, which is not representative of a clinical setting. The aim of this study was to investigate how well a DL model performs in unseen clinical data sets---collected with different scanners, protocols and disease populations---and whether more heterogeneous training data improves generalization. In total, 3117 MRI scans of brains from multiple dementia research cohorts and memory clinics, that had been visually rated by a neuroradiologist according to Scheltens' scale of medial temporal atrophy (MTA), were included in this study. By training multiple versions of a convolutional neural network on different subsets of this data to predict MTA ratings, we assessed the impact of including images from a wider distribution during training had on performance in external memory clinic data. Our results showed that our model generalized well to data sets acquired with similar protocols as the training data, but substantially worse in clinical cohorts with visibly different tissue contrasts in the images. This implies that future DL studies investigating performance in out-of-distribution (OOD) MRI data need to assess multiple external cohorts for reliable results. Further, by including data from a wider range of scanners and protocols the performance improved in OOD data, which suggests that more heterogeneous training data makes the model generalize better. To conclude, this is the most comprehensive study to date investigating the domain shift in deep learning on MRI data, and we advocate rigorous evaluation of DL models on clinical data prior to being certified for deployment., Comment: 11 pages, 3 figures

Published
2019
Full Text
View/download PDF

13. Cognitive rehabilitation and aerobic exercise for cognitive impairment in people with progressive multiple sclerosis (CogEx): a randomised, blinded, sham-controlled trial

Author

Pietrusz, Alex, Tacchino, Andrea, Smith, Angela, Michelsen, Anne Sophie, Kristin, Ashlie, Bichler, Blake, Truax, Brendon, Vizzino, Carmen, Jones, Catherine Danielle, Holme, Catherine, Smith, Catherine, Keytsman, Charly, Pollio, Chiara, Cole, Chris, Niccolai, Claudia, Cordani, Claudio, Colombo, Eleonora, Pelosin, Elisa, Vanzeir, Ellen, Vannetti, Fedrica, Gerli, Filippo, Maranta, Francesco, Riccitelli, Gianna, Pasquini, Guido, Wilkinson, Holly, Mosca, Irene, Braisher, James, Baird, Jessica, Podda, Jessica, Morecraft, Jimmy, Lenaerts, Joke, Puopolo, Juliana, Algie, Kimberley, Kenton, Laura, Toll, Laura, Madsen, Laurits T., Knevels, Leen, Lee, Louie, Pedullà, Ludovico, Cellerino, Maria, Braisher, Marie, Jørgensen, Marie-Louise Kjeldgaard, Pardini, Matteo, Sibilia, Mauro, Nabarro, Max, Diedmann, Mette Dahl, DiBenedetto, Michael, Curran, Michele, Koch, Michelle, D'Hooge, Mieke, Moore, Nancy, Weerdt, Natasja De, Preziosa, Paolo, Pajak, Patrizia, Silic, Petra, Walters, Rebecca Bex, Finegan, Rebecca, Veldkamp, Renee, Hernandez, Roberto, Donnee, Rudi, Casagrande, Sabrina, Lancia, Samantha, Bella, Sara Della, Vandecasteele, Séline, Vandael, Veerle, Feinstein, Anthony, Amato, Maria Pia, Brichetto, Giampaolo, Chataway, Jeremy, Chiaravalloti, Nancy D, Cutter, Gary, Dalgas, Ulrik, DeLuca, John, Farrell, Rachel, Feys, Peter, Filippi, Massimo, Freeman, Jennifer, Inglese, Matilde, Meza, Cecilia, Motl, Robert W, Rocca, Maria A, Sandroff, Brian M, and Salter, Amber

Published
2023
Full Text
View/download PDF

17. Dog-assisted physiotherapy in amyotrophic lateral sclerosis: a randomized controlled pilot study

Author

VIGNOLO, Manuela, primary, ZUCCARINO, Riccardo, additional, TRUFFELLI, Romina, additional, GEMELLI, Chiara, additional, GIOVE, Elisa, additional, FERRARO, Pilar M., additional, MANUNZA, Davide, additional, TRINCHERO, Clotilde, additional, CIPOLLINA, Iacopo, additional, LUNGU, Mirela, additional, LIZIO, Andrea, additional, GRAGNANO, Gaia, additional, CABONA, Corrado, additional, PARDINI, Matteo, additional, CAPONNETTO, Claudia, additional, and RAO, Fabrizio, additional

Published
2024
Full Text
View/download PDF

22. Neurological long-COVID in the outpatient clinic: Two subtypes, two courses

Author

Grisanti, Stefano Giuseppe, Garbarino, Sara, Barisione, Emanuela, Aloè, Teresita, Grosso, Marco, Schenone, Cristina, Pardini, Matteo, Biassoni, Erica, Zaottini, Federico, Picasso, Riccardo, Morbelli, Silvia, Campi, Cristina, Pesce, Giampaola, Massa, Federico, Girtler, Nicola, Battaglini, Denise, Cabona, Corrado, Bassetti, Matteo, Uccelli, Antonio, Schenone, Angelo, Piana, Michele, and Benedetti, Luana

Published
2022
Full Text
View/download PDF

24. Added value of semiquantitative analysis of brain FDG-PET for the differentiation between MCI-Lewy bodies and MCI due to Alzheimer’s disease

Author

Massa, Federico, Chincarini, Andrea, Bauckneht, Matteo, Raffa, Stefano, Peira, Enrico, Arnaldi, Dario, Pardini, Matteo, Pagani, Marco, Orso, Beatrice, Donegani, Maria Isabella, Brugnolo, Andrea, Biassoni, Erica, Mattioli, Pietro, Girtler, Nicola, Guerra, Ugo Paolo, Morbelli, Silvia, and Nobili, Flavio

Published
2022
Full Text
View/download PDF

27. Italian adaptation of the Uniform Data Set Neuropsychological Test Battery (I-UDSNB 1.0): development and normative data

Author

Conca, Francesca, Esposito, Valentina, Rundo, Francesco, Quaranta, Davide, Muscio, Cristina, Manenti, Rosa, Caruso, Giulia, Lucca, Ugo, Galbussera, Alessia Antonella, Di Tella, Sonia, Baglio, Francesca, L’Abbate, Federica, Canu, Elisa, Catania, Valentina, Filippi, Massimo, Mattavelli, Giulia, Poletti, Barbara, Silani, Vincenzo, Lodi, Raffaele, De Matteis, Maddalena, Stanzani Maserati, Michelangelo, Arighi, Andrea, Rotondo, Emanuela, Tanzilli, Antonio, Pace, Andrea, Garramone, Federica, Cavaliere, Carlo, Pardini, Matteo, Rizzetto, Cristiano, Sorbi, Sandro, Perri, Roberta, Tiraboschi, Pietro, Canessa, Nicola, Cotelli, Maria, Ferri, Raffaele, Weintraub, Sandra, Marra, Camillo, Tagliavini, Fabrizio, Catricalà, Eleonora, and Cappa, Stefano Francesco

Published
2022
Full Text
View/download PDF

32. Artificial intelligence of imaging and clinical neurological data for predictive, preventive and personalized (P3) medicine for Parkinson Disease: The NeuroArtP3 protocol for a multi-center research study

Author

Malaguti, Maria Chiara, primary, Gios, Lorenzo, additional, Giometto, Bruno, additional, Longo, Chiara, additional, Riello, Marianna, additional, Ottaviani, Donatella, additional, Pellegrini, Maria, additional, Di Giacopo, Raffaella, additional, Donner, Davide, additional, Rozzanigo, Umberto, additional, Chierici, Marco, additional, Moroni, Monica, additional, Jurman, Giuseppe, additional, Bincoletto, Giorgia, additional, Pardini, Matteo, additional, Bacchin, Ruggero, additional, Nobili, Flavio, additional, Di Biasio, Francesca, additional, Avanzino, Laura, additional, Marchese, Roberta, additional, Mandich, Paola, additional, Garbarino, Sara, additional, Pagano, Mattia, additional, Campi, Cristina, additional, Piana, Michele, additional, Marenco, Manuela, additional, Uccelli, Antonio, additional, and Osmani, Venet, additional

Published
2024
Full Text
View/download PDF

33. Safety, tolerability, and activity of mesenchymal stem cells versus placebo in multiple sclerosis (MESEMS): a phase 2, randomised, double-blind crossover trial

Author

Aghdami, Naser, Agüera-Morales, Eduardo, Ali, Rehiana, Allan, David, Arab, Leila, Battaglia, Mario, Berry, Isabelle, Blinkenberg, Morten, Bonetti, Bruno, Brundin, Lou, Capelli, Chiara, Castellan, Lucio, Cellerino, Maria, Cencioni, Maria Teresa, Clanet, Michel, Comi, Giancarlo, Courtman, David, Dazzi, Francesco, Fischer-Nielsen, Anne, Fernandez, Oscar, Fernandez, Victoria, Freedman, Mark S., Furlan, Roberto, Gimona, Mario, Gualandi, Francesca, Guan, Qingdong, Iacobaeus, Ellen, Inglese, Matilde, Introna, Martino, Izquierdo, Guillermo, Karimi, Shahedeh, Laroni, Alice, Le Blanc, Katarina, Loaiza, Sandra, Mallik, Shahrukh, Marley, Stephen, Marrie, Ruth Ann, Marriot, James, Martino, Gianvito, Nabavi, Seyed Massood, Miller, David, Muraro, Paolo A., Nicholas, Richard, Oliveri, Roberto S, Orengo, Giovanni, Palanicawande, Renuka, Pardini, Matteo, Radue, Ernst W, Ramo Tello, Cristina, Rush, Carolina, Schiavetti, Irene, Sellner, Johann, Sensebe, Luc, Soelberg Sorensen, Per, Sormani, Maria Pia, Strunk, Dirk, Szwajcer, David, Thalamas, Claire, Uccelli, Antonio, Wuerfel, Jens Thomas, Battaglia, Mario Alberto, Marriott, James, Muraro, Paolo, Radue, Ernst, Sorensen, Per Soelberg, and Freedman, Mark S

Published
2021
Full Text
View/download PDF

42. Forest 3D Radar Reflectivity Reconstruction at X-Band Using a Lidar Derived Polarimetric Coherence Tomography Basis.

Author

Guliaev, Roman, Pardini, Matteo, and Papathanassiou, Konstantinos P.

Subjects
*SYNTHETIC aperture radar, *LIDAR, *TOMOGRAPHY, *RAIN forests, *RADAR, *OPTICAL radar
Abstract

Tomographic Synthetic Aperture Radar (SAR) allows the reconstruction of the 3D radar reflectivity of forests from a large(r) number of multi-angular acquisitions. However, in most practical implementations it suffers from limited vertical resolution and/or reconstruction artefacts as the result of non-ideal acquisition setups. Polarisation Coherence Tomography (PCT) offers an alternative to traditional tomographic techniques that allow the reconstruction of the low-frequency 3D radar reflectivity components from a small(er) number of multi-angular SAR acquisitions. PCT formulates the tomographic reconstruction problem as a series expansion on a given function basis. The expansion coefficients are estimated from interferometric coherence measurements between acquisitions. In its original form, PCT uses the Legendre polynomial basis for the reconstruction of the 3D radar reflectivity. This paper investigates the use of new basis functions for the reconstruction of X-band 3D radar reflectivity of forests derived from available lidar waveforms. This approach enables an improved 3D radar reflectivity reconstruction with enhanced vertical resolution, tailored to individual forest conditions. It also allows the translation from sparse lidar waveform vertical reflectivity information into continuous vertical reflectivity estimates when combined with interferometric SAR measurements. This is especially relevant for exploring the synergy of actual missions such as GEDI and TanDEM-X. The quality of the reconstructed 3D radar reflectivity is assessed by comparing simulated InSAR coherences derived from the reconstructed 3D radar reflectivity against measured coherences at different spatial baselines. The assessment is performed and discussed for interferometric TanDEM-X acquisitions performed over two tropical Gabonese rainforest sites: Mondah and Lopé. The results demonstrate that the lidar-derived basis provides more physically realistic vertical reflectivity profiles, which also produce a smaller bias in the simulated coherence validation, compared to the conventional Legendre polynomial basis. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

43. Presynaptic Dopaminergic Imaging Characterizes Patients with REM Sleep Behavior Disorder Due to Synucleinopathy.

Author

Arnaldi, Dario, Mattioli, Pietro, Raffa, Stefano, Pardini, Matteo, Massa, Federico, Iranzo, Alex, Perissinotti, Andres, Niñerola‐Baizán, Aida, Gaig, Carles, Serradell, Monica, Muñoz‐Lopetegi, Amaia, Mayà, Gerard, Liguori, Claudio, Fernandes, Mariana, Placidi, Fabio, Chiaravalloti, Agostino, Šonka, Karel, Dušek, Petr, Zogala, David, and Trnka, Jiri

Subjects
DOPAMINERGIC imaging, SLEEP, RAPID eye movement sleep, SLEEP disorders, LEWY body dementia, SMELL disorders, POLYSOMNOGRAPHY
Abstract

Objective: To apply a machine learning analysis to clinical and presynaptic dopaminergic imaging data of patients with rapid eye movement (REM) sleep behavior disorder (RBD) to predict the development of Parkinson disease (PD) and dementia with Lewy bodies (DLB). Method s : In this multicenter study of the International RBD study group, 173 patients (mean age 70.5 ± 6.3 years, 70.5% males) with polysomnography‐confirmed RBD who eventually phenoconverted to overt alpha‐synucleinopathy (RBD due to synucleinopathy) were enrolled, and underwent baseline presynaptic dopaminergic imaging and clinical assessment, including motor, cognitive, olfaction, and constipation evaluation. For comparison, 232 RBD non‐phenoconvertor patients (67.6 ± 7.1 years, 78.4% males) and 160 controls (68.2 ± 7.2 years, 53.1% males) were enrolled. Imaging and clinical features were analyzed by machine learning to determine predictors of phenoconversion. Results: Machine learning analysis showed that clinical data alone poorly predicted phenoconversion. Presynaptic dopaminergic imaging significantly improved the prediction, especially in combination with clinical data, with 77% sensitivity and 85% specificity in differentiating RBD due to synucleinopathy from non phenoconverted RBD patients, and 85% sensitivity and 86% specificity in discriminating PD‐converters from DLB‐converters. Quantification of presynaptic dopaminergic imaging showed that an empirical z‐score cutoff of −1.0 at the most affected hemisphere putamen characterized RBD due to synucleinopathy patients, while a cutoff of −1.0 at the most affected hemisphere putamen/caudate ratio characterized PD‐converters. Interpretation: Clinical data alone poorly predicted phenoconversion in RBD due to synucleinopathy patients. Conversely, presynaptic dopaminergic imaging allows a good prediction of forthcoming phenoconversion diagnosis. This finding may be used in designing future disease‐modifying trials. ANN NEUROL 2024;95:1178–1192 [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

47. Predicting disease severity in multiple sclerosis using multimodal data and machine learning

Author

Andorra, Magi, primary, Freire, Ana, additional, Zubizarreta, Irati, additional, de Rosbo, Nicole Kerlero, additional, Bos, Steffan D., additional, Rinas, Melanie, additional, Høgestøl, Einar A., additional, de Rodez Benavent, Sigrid A., additional, Berge, Tone, additional, Brune-Ingebretse, Synne, additional, Ivaldi, Federico, additional, Cellerino, Maria, additional, Pardini, Matteo, additional, Vila, Gemma, additional, Pulido-Valdeolivas, Irene, additional, Martinez-Lapiscina, Elena H., additional, Llufriu, Sara, additional, Saiz, Albert, additional, Blanco, Yolanda, additional, Martinez-Heras, Eloy, additional, Solana, Elisabeth, additional, Bäcker-Koduah, Priscilla, additional, Behrens, Janina, additional, Kuchling, Joseph, additional, Asseyer, Susanna, additional, Scheel, Michael, additional, Chien, Claudia, additional, Zimmermann, Hanna, additional, Motamedi, Seyedamirhosein, additional, Kauer-Bonin, Josef, additional, Brandt, Alex, additional, Saez-Rodriguez, Julio, additional, Alexopoulos, Leonidas G., additional, Paul, Friedemann, additional, Harbo, Hanne F., additional, Shams, Hengameh, additional, Oksenberg, Jorge, additional, Uccelli, Antonio, additional, Baeza-Yates, Ricardo, additional, and Villoslada, Pablo, additional

Published
2023
Full Text
View/download PDF

48. Medical Information Extraction with NLP-Powered QABots: a Real-World Scenario

Author

Crema, Claudio, primary, Verde, Federico, additional, Tiraboschi, Pietro, additional, Marra, Camillo, additional, Arighi, Andrea, additional, Fostinelli, Silvia, additional, Giuffré, Guido Maria, additional, Maschio, Vera Pacoova Dal, additional, L'abbate, Federica, additional, Solca, Federica, additional, Poletti, Barbara, additional, Silani, Vincenzo, additional, Rotondo, Emanuela, additional, Borracci, Vittoria, additional, Vimercati, Roberto, additional, Crepaldi, Valeria, additional, Inguscio, Emanuela, additional, Filippi, Massimo, additional, Caso, Francesca, additional, Rosati, Alessandra Maria, additional, Quaranta, Davide, additional, Binetti, Giuliano, additional, Pagnoni, Ilaria, additional, Morreale, Manuela, additional, Burgio, Francesca, additional, Maserati, Michelangelo Stanzani, additional, Capellari, Sabina, additional, Pardini, Matteo, additional, Girtler, Nicola, additional, Piras, Federica, additional, Piras, Fabrizio, additional, Lalli, Stefania, additional, Perdixi, Elena, additional, Lombardi, Gemma, additional, Tella, Sonia Di, additional, Costa, Alfredo, additional, Capelli, Marco, additional, Fundarò, Cira, additional, Manera, Marina, additional, Muscio, Cristina, additional, Pellencin, Elisa, additional, Lodi, Raffaele, additional, Tagliavini, Fabrizio, additional, and Redolfi, Alberto, additional

Published
2023
Full Text
View/download PDF

50. Monoaminergic Disfunctions and Misfolded Proteins levels in the Alzheimer’s Disease Continuum

Author

Orso, Beatrice, primary, Peira, Enrico, additional, Arnaldi, Dario, additional, Girtler, Nicola, additional, Brugnolo, Andrea, additional, Mattioli, Pietro, additional, Biassoni, Erica, additional, Donniaquio, Andrea, additional, Massa, Federico, additional, Uccelli, Antonio, additional, Bauckneht, Matteo, additional, Morbelli, Silvia, additional, and Pardini, Matteo, additional

Published
2023
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results