The Salivary Raman COVID-19 Fingerprint

NCT04583306 | Unknown

• 1 other identifier: FDG_SalivaCOVID01
• Study Type: observational
• Target: 120
• Locations: 1 country
• Sites: 5

Brief Summary

The outbreak of coronavirus disease 2019 (COVID-19), caused by infection of SARS-CoV-2, has rapidly spread to become a worldwide pandemic. Global research focused on the understanding of the biochemical infective mechanism and on the discovery of a fast, sensitive and cheap diagnostic tool, able to discriminate the current and past SARS-CoV-2 infections from a minimal invasive biofluid. The fast diagnosis of COVID-19 is fundamental in order to limit and isolate the positive cases, decreasing with a prompt intervention the infection spreading. The aim of the project is to characterize and validate the salivary Raman fingerprint of COVID-19, understanding the principal biomolecules involved in the differences between the three experimental groups: 1) healthy subjects, 2) COVID-19 patients and 3) subjects with a past infection by COVID-19. The large amount of Raman data will be used to create a salivary Raman database, associating each data with the relative clinical data collected. Starting from the preliminary results and protocols of the Laboratory of Nanomedicine and Clinical Biophotonics (LABION) - IRCCS Fondazione Don Gnocchi Milano, the saliva collected from each experimental group will be analysed using Raman spectroscopy. All the data will be processed for the baseline, shift and normalization in order to homogenize the signals collected and creating in this way the Raman database. The average spectrum calculated from each group will be characterized, identifying the principal families of biological molecules responsible for the spectral differences. EXPECTED RESULTS: Verify the possibility to use Raman spectroscopy on saliva samples for the identification of subjects affected by COVID-19. The principal aim of the project is to create a classification model able to: discriminate COVID-19 current and past infection, identify the principal biological molecules altered in saliva during the infection, predict the clinical course of newly diagnosed COVID-19 patients, translation and application of the classification model to a portable Raman for the test of a point of care.

Conditions

Covid19

Keywords

Covid-19; Raman Spectroscopy; Machine Learning; Classification Model; Diagnosis

Outcome Measures

Primary Outcomes (6)

• Identification and characterization of a new COVID-19 salivary signature through Raman spectroscopy

  The Raman analysis of saliva samples collected from patients affected by COVID-19 and with a past infection, will be used to characterize a COVID-19 signature able to discriminate subjects with a current or past infection

  One day

• Evaluation of the spectral differences between the experimental groups

  The Raman data collected from the experimental groups will be compared and interpolated with the huge number of Raman databases on biofluids present in literature. This procedure will provide a determination of the principal biochemical species involved in the differences between the experimental groups (e.g. viral structural protein and lipids, cytokines, inflammatory molecules, damaged biomolecules)

  One months

• Determination of the classification model through multivariate analysis

  The Raman database will be processed through principal component analysis and linear discriminant analysis. The consecutive leave-one out cross-validation will provide a primary discrimination model able to assign each spectra to one of the experimental group

  6 months

• Correlation with the clinical data

  Raman data related to subjects with a current or past infection by SARS-CoV-2 will be correlated with the clinical data, validating in this way our methodology. The principal correlation will be carried out between the severity of the respiratory infection and the time between the first SARS-CoV-2 positive test and the last negative SARS-CoV-2 test.

  One Day

• Test of the methodology

  The classification model will be continuosly questioned and trained using new potential patients and adding new clinical parameters as "sub-groups" for the complete discrimination and prediction of the pathological state.

  One Year

• Portable Raman as point of care

  The characterized and implemented classification model will be translated to a portable Raman equipped with a laser emitting at 785 nm and with a spectral resolution comparable with the one of the bench Raman. This station will be firstly tested with patients coming to the hospital and then applied continuosly implementing the classification model with new Raman spectra and clinical data. In this way we will highly implement the accuracy, sensitivity, precision and specificity of the model.

  One Year

Study Arms (3)

Healthy Subjects

40 Healthy Subjects in a good state of health comparable by age and sex with the other selected groups and with a negative test for SARS-CoV-2 or collected before the pandemic event

Other: Raman analysis of saliva, characterization of the Raman database and building of the classification model

COVID-19 Positive

40 subjects affected by COVID-19, determined by positive nasopharyngeal test for SARS-CoV-2 and with comparable age and sex for the other selected groups

Other: Raman analysis of saliva, characterization of the Raman database and building of the classification model

COVID-19 Negative

40 subjects with a past infection by SARS-CoV-2 confirmed and with at least two consecutive negative tests determined by nasopharyngeal SARS-CoV-2 assay, comparable by age and sex with the other selected groups

Other: Raman analysis of saliva, characterization of the Raman database and building of the classification model

Interventions

Raman analysis of saliva, characterization of the Raman database and building of the classification model OTHER

Saliva will be collected, processed and analysed through Raman spectroscopy. Data acquired will be normalized and treated for the creation of the classification model.

COVID-19 Negative; COVID-19 Positive; Healthy Subjects

Eligibility Criteria

• Age: 18 Years - 90 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

The study population will be recruited from the clinics at IRCCS Fondazione Don Carlo Gnocchi: Santa Maria Nascente (Milano) and Centro Spalenza (Rovato)

You may qualify if:

• Diagnosis of COVID-19 through nasopharyngeal swab positive for SARS-CoV-2
• Provided written consent for the salivary analysis
• Age between 18 and 90 years

You may not qualify if:

• Oral bacterial or fungal infection in progress (e.g. oral candidiasis)
• Age lower than 18 and higher than 90 years
• No written consent provided

Sponsors & Collaborators

• Fondazione Don Carlo Gnocchi Onlus: lead

Study Sites (5)

Azienda Ospedaliera Universitaria Policlinico di Bari

Bari, Apulia, Italy

RECRUITING

Fondazione Don Carlo Gnocchi, Centro Spalenza

Rovato, BS, 25038, Italy

RECRUITING

IRCCS Fondazione Don Carlo Gnocchi, Santa Maria Nascente Hospital (Milano)

Milan, MI, 20148, Italy

RECRUITING

Farmaacquisition srl

Milan, 20100, Italy

RECRUITING

Università degli Studi di Milano-Bicocca

Milan, 20100, Italy

ACTIVE NOT RECRUITING

Related Publications (4)

• Feng Z, Yu Q, Yao S, Luo L, Zhou W, Mao X, Li J, Duan J, Yan Z, Yang M, Tan H, Ma M, Li T, Yi D, Mi Z, Zhao H, Jiang Y, He Z, Li H, Nie W, Liu Y, Zhao J, Luo M, Liu X, Rong P, Wang W. Early prediction of disease progression in COVID-19 pneumonia patients with chest CT and clinical characteristics. Nat Commun. 2020 Oct 2;11(1):4968. doi: 10.1038/s41467-020-18786-x.

  PMID: 33009413 BACKGROUND

• Carlomagno C, Cabinio M, Picciolini S, Gualerzi A, Baglio F, Bedoni M. SERS-based biosensor for Alzheimer disease evaluation through the fast analysis of human serum. J Biophotonics. 2020 Mar;13(3):e201960033. doi: 10.1002/jbio.201960033. Epub 2020 Jan 1.

  PMID: 31868266 BACKGROUND

• Carlomagno C, Banfi PI, Gualerzi A, Picciolini S, Volpato E, Meloni M, Lax A, Colombo E, Ticozzi N, Verde F, Silani V, Bedoni M. Human salivary Raman fingerprint as biomarker for the diagnosis of Amyotrophic Lateral Sclerosis. Sci Rep. 2020 Jun 23;10(1):10175. doi: 10.1038/s41598-020-67138-8.

  PMID: 32576912 BACKGROUND

• Gualerzi A, Niada S, Giannasi C, Picciolini S, Morasso C, Vanna R, Rossella V, Masserini M, Bedoni M, Ciceri F, Bernardo ME, Brini AT, Gramatica F. Raman spectroscopy uncovers biochemical tissue-related features of extracellular vesicles from mesenchymal stromal cells. Sci Rep. 2017 Aug 29;7(1):9820. doi: 10.1038/s41598-017-10448-1.

  PMID: 28852131 BACKGROUND

Related Links

• Laboratory of Nanomedicine and Clinical Biophotonics at IRCCS Fondazione Don Carlo Gnocchi ONLUS, Milano (Italy)
• IRCCS Fondazione Don Carlo Gnocchi ONLUS

MeSH Terms

Conditions

COVID-19; Disease

Condition Hierarchy (Ancestors)

Pneumonia, Viral; Pneumonia; Respiratory Tract Infections; Infections; Virus Diseases; Coronavirus Infections; Coronaviridae Infections; Nidovirales Infections; RNA Virus Infections; Lung Diseases; Respiratory Tract Diseases; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Study Officials

• Marzia Bedoni, PhD

  IRCCS Fondazione Don Carlo Gnocchi, Laboratory of Nanomedicine and Clinical Biophotonics

  STUDY CHAIR

Central Study Contacts

Marzia Bedoni, PhD

CONTACT

0240308874; mbedoni@dongnocchi.it

LABION laboratory

CONTACT

0240308533; labion@dongnocchi.it

Study Design

• Study Type: observational
• Observational Model: CASE CONTROL
• Time Perspective: PROSPECTIVE
• Target Duration: 1 Day
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: October 9, 2020
• First Posted: October 12, 2020
• Study Start: June 1, 2020
• Primary Completion: October 30, 2022
• Study Completion: December 31, 2022
• Last Updated: May 11, 2022

Record last verified: 2022-02

Locations

IT (5)