Despite the wide range of proposed biomarkers for Parkinson’s disease (PD), there are no specific molecules or signals able to early and uniquely identify the pathology onset, progression and stratification. Saliva is a complex biofluid, containing a wide range of biological molecules shared with blood and cerebrospinal fluid. By means of an optimized Raman spectroscopy procedure, the salivary Raman signature of PD can be characterized and used to create a classification model. Raman analysis was applied to collect the global signal from the saliva of 23 PD patients and related pathological and healthy controls. The acquired spectra were computed using machine and deep learning approaches. The Raman database was used to create a classification model able to discriminate each spectrum to the correct belonging group, with accuracy, specificity, and sensitivity of more than 97% for the single spectra attribution. Similarly, each patient was correctly assigned with discriminatory power of more than 90%. Moreover, the extracted data were significantly correlated with clinical data used nowadays for the PD diagnosis and monitoring. The preliminary data reported highlight the potentialities of the proposed methodology that, once validated in larger cohorts and with multi-centered studies, could represent an innovative minimally invasive and accurate procedure to determine the PD onset, progression and to monitor therapies and rehabilitation efficacy.

Carlomagno, C., Bertazioli, D., Gualerzi, A., Picciolini, S., Andrico, M., Roda, F., et al. (2021). Identification of the Raman Salivary Fingerprint of Parkinson’s Disease Through the Spectroscopic– Computational Combinatory Approach. FRONTIERS IN NEUROSCIENCE, 15(26 October 2021) [10.3389/fnins.2021.704963].

Identification of the Raman Salivary Fingerprint of Parkinson’s Disease Through the Spectroscopic– Computational Combinatory Approach

Bertazioli D.;Picciolini S.;Andrico M.;Messina E.;
2021

Abstract

Despite the wide range of proposed biomarkers for Parkinson’s disease (PD), there are no specific molecules or signals able to early and uniquely identify the pathology onset, progression and stratification. Saliva is a complex biofluid, containing a wide range of biological molecules shared with blood and cerebrospinal fluid. By means of an optimized Raman spectroscopy procedure, the salivary Raman signature of PD can be characterized and used to create a classification model. Raman analysis was applied to collect the global signal from the saliva of 23 PD patients and related pathological and healthy controls. The acquired spectra were computed using machine and deep learning approaches. The Raman database was used to create a classification model able to discriminate each spectrum to the correct belonging group, with accuracy, specificity, and sensitivity of more than 97% for the single spectra attribution. Similarly, each patient was correctly assigned with discriminatory power of more than 90%. Moreover, the extracted data were significantly correlated with clinical data used nowadays for the PD diagnosis and monitoring. The preliminary data reported highlight the potentialities of the proposed methodology that, once validated in larger cohorts and with multi-centered studies, could represent an innovative minimally invasive and accurate procedure to determine the PD onset, progression and to monitor therapies and rehabilitation efficacy.
Articolo in rivista - Articolo scientifico
classification model; deep learning; Parkinson’s disease; Raman spectroscopy; saliva;
English
Carlomagno, C., Bertazioli, D., Gualerzi, A., Picciolini, S., Andrico, M., Roda, F., et al. (2021). Identification of the Raman Salivary Fingerprint of Parkinson’s Disease Through the Spectroscopic– Computational Combinatory Approach. FRONTIERS IN NEUROSCIENCE, 15(26 October 2021) [10.3389/fnins.2021.704963].
Carlomagno, C; Bertazioli, D; Gualerzi, A; Picciolini, S; Andrico, M; Roda, F; Meloni, M; Banfi, P; Verde, F; Ticozzi, N; Silani, V; Messina, E; Bedoni, M
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/370650
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