Zobrazeno 1 - 10
of 815
pro vyhledávání: '"Brusini A"'
Autor:
Dolci, Giorgio, Ellis, Charles A., Cruciani, Federica, Brusini, Lorenza, Abrol, Anees, Galazzo, Ilaria Boscolo, Menegaz, Gloria, Calhoun, Vince D.
Amyloid-$\beta$ (A$\beta$) plaques in conjunction with hyperphosphorylated tau proteins in the form of neurofibrillary tangles are the two neuropathological hallmarks of Alzheimer's disease. It is well-known that the identification of individuals wit
Externí odkaz:
http://arxiv.org/abs/2406.13305
Autor:
Lorenza Brusini, Federica Cruciani, Gabriele Dall'glio, Tommaso Zajac, Ilaria Boscolo Galazzo, Mauro Zucchelli, Gloria Menegaz
Publikováno v:
IEEE Journal of Translational Engineering in Health and Medicine, Vol 12, Pp 569-579 (2024)
Brain microstructural changes already occur in the earliest phases of Alzheimer’s disease (AD) as evidenced in diffusion magnetic resonance imaging (dMRI) literature. This study investigates the potential of the novel dMRI Apparent Measures Using R
Externí odkaz:
https://doaj.org/article/48c85069bd1d488cba613615d1016cac
Autor:
Giovanni M. Di Liberto, Adam Attaheri, Giorgia Cantisani, Richard B. Reilly, Áine Ní Choisdealbha, Sinead Rocha, Perrine Brusini, Usha Goswami
Publikováno v:
Nature Communications, Vol 14, Iss 1, Pp 1-11 (2023)
Abstract Even prior to producing their first words, infants are developing a sophisticated speech processing system, with robust word recognition present by 4–6 months of age. These emergent linguistic skills, observed with behavioural investigatio
Externí odkaz:
https://doaj.org/article/3f28cf01af8f451ab59464630c0e5d99
Autor:
Siamak Yousefi, PhD, Xiaoqin Huang, PhD, Asma Poursoroush, MSc, Julek Majoor, PhD, Hans Lemij, MD, Koen Vermeer, PhD, Tobias Elze, PhD, Mengyu Wang, PhD, Kouros Nouri-Mahdavi, MD, MPH, Vahid Mohammadzadeh, MD, Paolo Brusini, MD, Chris Johnson, PhD
Publikováno v:
Ophthalmology Science, Vol 4, Iss 2, Pp 100389- (2024)
Purpose: To develop an objective glaucoma damage severity classification system based on OCT-derived retinal nerve fiber layer (RNFL) thickness measurements. Design: Algorithm development for RNFL damage severity classification based on multicenter O
Externí odkaz:
https://doaj.org/article/49e40736a10f45428c8e079d36c0d471
Autor:
Keshavarzi, Mahmoud, Choisdealbha, Áine Ní, Attaheri, Adam, Rocha, Sinead, Brusini, Perrine, Gibbon, Samuel, Boutris, Panagiotis, Mead, Natasha, Olawole-Scott, Helen, Ahmed, Henna, Flanagan, Sheila, Mandke, Kanad, Goswami, Usha
Publikováno v:
In Journal of Neuroscience Methods March 2024 403
Autor:
Yousefi, Siamak, Huang, Xiaoqin, Poursoroush, Asma, Majoor, Julek, Lemij, Hans, Vermeer, Koen, Elze, Tobias, Wang, Mengyu, Nouri-Mahdavi, Kouros, Mohammadzadeh, Vahid, Brusini, Paolo, Johnson, Chris
Publikováno v:
In Ophthalmology Science March-April 2024 4(2)
Autor:
Bendazzoli, Simone, Brusini, Irene, Astaraki, Mehdi, Persson, Mats, Yu, Jimmy, Connolly, Bryan, Nyrén, Sven, Strand, Fredrik, Smedby, Örjan, Wang, Chunliang
Segmentation of COVID-19 lesions from chest CT scans is of great importance for better diagnosing the disease and investigating its extent. However, manual segmentation can be very time consuming and subjective, given the lesions' large variation in
Externí odkaz:
http://arxiv.org/abs/2012.14752
Autor:
Cruciani, Federica, Aparo, Antonino, Brusini, Lorenza, Combi, Carlo, Storti, Silvia F., Giugno, Rosalba, Menegaz, Gloria, Boscolo Galazzo, Ilaria
Publikováno v:
In Journal of Biomedical Informatics January 2024 149
Publikováno v:
mBio, Vol 15, Iss 2 (2024)
ABSTRACT Apicomplexa encompasses a large number of intracellular parasites infecting a wide range of animals. Cyclic nucleotide signaling is crucial for a variety of apicomplexan life stages and cellular processes. The cyclases and kinases that synth
Externí odkaz:
https://doaj.org/article/db88c2eeeb0c472fb95d9276197b39d8
Autor:
Brusini, Irene, Padilla, Daniel Ferreira, Barroso, José, Skoog, Ingmar, Smedby, Örjan, Westman, Eric, Wang, Chunliang
Brain MRI segmentation results should always undergo a quality control (QC) process, since automatic segmentation tools can be prone to errors. In this work, we propose two deep learning-based architectures for performing QC automatically. First, we
Externí odkaz:
http://arxiv.org/abs/2005.13987