SILVIA FIGINI

Prof. SILVIA FIGINI

University of Pavia
orcid

Research Focus & Metrics

Academic Career Length

21Years (2006-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 May 2008 in Methodology and Computing in Applied Probability
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Dean FantazziniDean Fantazzini
Dean Fantazzini
SILVIA FIGINISILVIA FIGINI
SILVIA FIGINI

17 April 2009
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Book
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Paolo GiudiciPaolo Giudici
Paolo Giudici
SILVIA FIGINISILVIA FIGINI
SILVIA FIGINI
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1 June 2011 in Journal of the Operational Research Society
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SILVIA FIGINISILVIA FIGINI
SILVIA FIGINI
PGP Giudici
P Giudici

1 October 2017 in Decision Support Systems
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Journal Article
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SILVIA FIGINISILVIA FIGINI
SILVIA FIGINI
FBFederico Bonelli
Federico Bonelli
EGEmanuele Giovannini
Emanuele Giovannini

1 September 2014 in Computational Statistics & Data Analysis
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CGChiara Gigliarano
Chiara Gigliarano
SILVIA FIGINISILVIA FIGINI
SILVIA FIGINI
PMPietro Muliere
Pietro Muliere

28 May 2019 in Movement Disorders
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CMChiara Milanese
Chiara Milanese
CPCésar Payán‐Gómez
César Payán‐Gómez
MGMarta Galvani
Marta Galvani
NGNicolás Molano González
Nicolás Molano González
MTMaria Tresini
Maria Tresini
PMPier G. Mastroberardino
Pier G. Mastroberardino
Background: Parkinson's disease is an intractable disorder with heterogeneous clinical presentation that may reflect different underlying pathogenic mechanisms. Surrogate indicators of pathogenic processes correlating with clinical measures may assist in better patient stratification. Mitochondrial function, which is impaired in and central to PD pathogenesis, may represent one such surrogate indicator.Methods: Mitochondrial function was assessed by respirometry experiment in fibroblasts derived from idiopathic patients (n = 47) in normal conditions and in experimental settings that do not permit glycolysis and therefore force energy production through mitochondrial function. Respiratory parameters and clinical measures were correlated with bivariate analysis. Machine‐learning‐based classification and regression trees were used to classify patients on the basis of biochemical and clinical measures. The effects of mitochondrial respiration on α‐synuclein stress were assessed monitoring the protein phosphorylation in permitting versus restrictive glycolysis conditions.Results: Bioenergetic properties in peripheral fibroblasts correlate with clinical measures in idiopathic patients, and the correlation is stronger with predominantly nondopaminergic signs. Bioenergetic analysis under metabolic stress, in which energy is produced solely by mitochondria, shows that patients’ fibroblasts can augment respiration, therefore indicating that mitochondrial defects are reversible. Forcing energy production through mitochondria, however, favors α‐synuclein stress in different cellular experimental systems. Machine‐learning‐based classification identified different groups of patients in which increasing disease severity parallels higher mitochondrial respiration.Conclusion: The suppression of mitochondrial activity in PD may be an adaptive strategy to cope with concomitant pathogenic factors. Moreover, mitochondrial measures in fibroblasts are potential peripheral biomarkers to follow disease progression. © 2019 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.