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Customised next-generation sequencing multigene panel to screen a large cohort of individuals with chromatin-related disorder.
Squeo GM, Augello B, Massa V, Milani D, Colombo EA, Mazza T, Castellana S, Piccione M, Maitz S, Petracca A, Prontera P, Accadia M, Della Monica M, Di Giacomo MC, Melis D, Selicorni A, Giglio S, Fischetto R, Di Fede E, Malerba N, Russo M, Castori M, Gervasini C, Merla G. Squeo GM, et al. J Med Genet. 2020 Nov;57(11):760-768. doi: 10.1136/jmedgenet-2019-106724. Epub 2020 Mar 13. J Med Genet. 2020. PMID: 32170002
Genotype-phenotype correlations in patients with de novo KCNQ2 pathogenic variants.
Malerba F, Alberini G, Balagura G, Marchese F, Amadori E, Riva A, Vari MS, Gennaro E, Madia F, Salpietro V, Angriman M, Giordano L, Accorsi P, Trivisano M, Specchio N, Russo A, Gobbi G, Raviglione F, Pisano T, Marini C, Mancardi MM, Nobili L, Freri E, Castellotti B, Capovilla G, Coppola A, Verrotti A, Martelli P, Miceli F, Maragliano L, Benfenati F, Cilio MR, Johannesen KM, Møller RS, Ceulemans B, Minetti C, Weckhuysen S, Zara F, Taglialatela M, Striano P. Malerba F, et al. Neurol Genet. 2020 Nov 30;6(6):e528. doi: 10.1212/NXG.0000000000000528. eCollection 2020 Dec. Neurol Genet. 2020. PMID: 33659638 Free PMC article.
Clinical factors associated with death in 3044 COVID-19 patients managed in internal medicine wards in Italy: results from the SIMI-COVID-19 study of the Italian Society of Internal Medicine (SIMI).
Corradini E, Ventura P, Ageno W, Cogliati CB, Muiesan ML, Girelli D, Pirisi M, Gasbarrini A, Angeli P, Querini PR, Bosi E, Tresoldi M, Vettor R, Cattaneo M, Piscaglia F, Brucato AL, Perlini S, Martelletti P, Pontremoli R, Porta M, Minuz P, Olivieri O, Sesti G, Biolo G, Rizzoni D, Serviddio G, Cipollone F, Grassi D, Manfredini R, Moreo GL, Pietrangelo A; SIMI-COVID-19 Collaborators. Corradini E, et al. Intern Emerg Med. 2021 Jun;16(4):1005-1015. doi: 10.1007/s11739-021-02742-8. Epub 2021 Apr 24. Intern Emerg Med. 2021. PMID: 33893976 Free PMC article.
Calculation of proper energy barriers for atomistic kinetic Monte Carlo simulations on rigid lattice with chemical and strain field long-range effects using artificial neural networks.
Castin N, Malerba L. Castin N, et al. J Chem Phys. 2010 Feb 21;132(7):074507. doi: 10.1063/1.3298990. J Chem Phys. 2010. PMID: 20170237
In this paper we take a few steps further in the development of an approach based on the use of an artificial neural network (ANN) to introduce long-range chemical effects and zero temperature relaxation (elastic strain) effects in a rigid lattice atomistic kinetic Monte Carlo
In this paper we take a few steps further in the development of an approach based on the use of an artificial neural network (ANN) to introd …
Modeling the first stages of Cu precipitation in alpha-Fe using a hybrid atomistic kinetic Monte Carlo approach.
Castin N, Pascuet MI, Malerba L. Castin N, et al. J Chem Phys. 2011 Aug 14;135(6):064502. doi: 10.1063/1.3622045. J Chem Phys. 2011. PMID: 21842938
We simulate the coherent stage of Cu precipitation in alpha-Fe with an atomistic kinetic Monte Carlo (AKMC) model. The vacancy migration energy as a function of the local chemical environment is provided on-the-fly by a neural network, trained with high precision on values …
We simulate the coherent stage of Cu precipitation in alpha-Fe with an atomistic kinetic Monte Carlo (AKMC) model. The vacancy migrat …