|drug1726||Investigational Product - ViraCide Wiki||1.00|
|drug2506||Placebo - Starch Powder Soft gels Wiki||1.00|
|D014777||Virus Diseases NIH||0.10|
|D018352||Coronavirus Infections NIH||0.03|
There is one clinical trial.
BACKGROUND/RATIONALE: The outbreak of coronavirus disease 2019 (COVID-19), caused by infection of SARS-CoV-2, has rapidly spread to become a worldwide pandemic between 2019 and 2020. Nowadays, the spreading of the infections is still increasing with the 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. Moreover, the prediction of the respiratory infection severity could be of crucial importance for the fast identification and discrimination between mild clinical course, severe illness, and Acute Respiratory Distress Syndrome (ARDS). One of the first infection sites of SARS-CoV-2 is the oral cavity where the virus is able to bind and penetrate through the ACE2 receptors present on the epithelial cells of the salivary glands. Thus, a high concentration of virus particles could be found in saliva in the preliminary phases of the infection. Saliva is a complex biofluid composed of bioactive molecules that can be collected with a really minimal-invasive procedure. Raman spectroscopy is a non-invasive, fast and label-free vibrational technique, able to provide information regarding presence, concentration, environment, modifications and interactions of all the biochemical species present in a specific biofluid. Using the Raman spectroscopy, we will analyze saliva collected from healthy subjects, patients affected by COVID-19 and subjects with a past infection by COVID-19. The data collected will be analyzed and used to create a Raman database able to provide a classification model based on machine learning. The possibility to monitor and characterize a potential salivary COVID-19 fingerprint could be of crucial importance for the monitoring and discrimination of COVID-19 subjects with a current and past infection from the healthy subjects. OBJECTIVES: 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. The Raman database will be used for the creation of a classification model through the application of multivariate analysis in terms of principal component analysis and linear discriminant analysis. This classification model will provide a fast tool for the discrimination of the COVID-19 condition, potentially providing also information on the respiratory clinical course of the patient. The model will be translated for the application to a portable Raman spectrometer, leading to the creation of a Raman Point of Care METHODS: 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. Consecutively, all the spectra will be processed through multivariate analysis (principal component analysis and linear discriminant analysis) obtaining in this way the classification model. LOOCV will be used for the training of the classification model, which will be questioned using the subset validation analysis. The partial correlation coefficient (Pearson's and Spearman's correlation) will be used for the Raman correlation with the clinical parameter (e.g. COVID-19 clinical course) using as control covariates the age and sex of the subjects. The classification model will be then translated and used as point of care using a portable Raman equipped with a laser emitting at 785 nm, with a comparable spectral resolution. EXPECTED RESULTS: We will 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.
Description: 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 infectionMeasure: Identification and characterization of a new COVID-19 salivary signature through Raman spectroscopy Time: One day
Description: 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)Measure: Evaluation of the spectral differences between the experimental groups Time: One months
Description: 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 groupMeasure: Determination of the classification model through multivariate analysis Time: 6 months
Description: 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.Measure: Correlation with the clinical data Time: One Day
Description: 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.Measure: Test of the methodology Time: One Year
Description: 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.Measure: Portable Raman as point of care Time: One Year
Data processed on January 01, 2021.
An HTML report was created for each of the unique drugs, MeSH, and HPO terms associated with COVID-19 clinical trials. Each report contains a list of either the drug, the MeSH terms, or the HPO terms. All of the terms in a category are displayed on the left-hand side of the report to enable easy navigation, and the reports contain a list of correlated drugs, MeSH, and HPO terms. Further, all reports contain the details of the clinical trials in which the term is referenced. Every clinical trial report shows the mapped HPO and MeSH terms, which are also hyperlinked. Related HPO terms, with their associated genes, protein mutations, and SNPs are also referenced in the report.Drug Reports MeSH Reports HPO Reports