Diagnosis of RHeumatic and Musculoskeletal Diseases From Synovial Fluid Using Sers and Artificial Intelligence
il y a 3 semaines
**Diagnosis of Rheumatic and Musculoskeletal Diseases from Synovial Fluid Using SERS and Artificial Intelligence**:
- Réf **ABG-127705**
- Stage master 2 / Ingénieur
- Durée 6 mois
- Salaire net mensuel environ 570€/mois
- 23/12/2024
- Université de Reims Champagne-Ardenne
- Lieu de travail- Reims Grand Est France
- Champs scientifiques- Science de la donnée (stockage, sécurité, mesure, analyse)
- Informatique
- Mathématiques
- Mots clés- Artificial Intelligence, Machine Learning, SERS, Synovial fluid- 31/01/2025
**Établissement recruteur**:
**Site web**:
BioSpecT (Translational BioSpectroscopy), EA 7506
Our team is specialized in the development of vibrational spectroscopy techniques (infrared absorption and Raman scattering) for the characterization of biological samples (cell, tissue, biofluid). These techniques allow the identification of new diagnostic biomarkers for the detection of cancer lesions or to highlight molecular alterations associated to tissue aging (skin). Spectral data exploitation is based on the implementation of multivariate statistical algorithms developed by the team. Our team has a real and recognized expertise in biological analysis by vibrational spectrocopy.
**Description**:
Rheumatic and musculoskeletal diseases affect a quarter of the population in the European Union—over 120 million people. They are the leading cause of sick leave and premature retirement worldwide. As a result, these diseases impose a significant economic burden on global healthcare systems, with public spending in Europe exceeding €200 billion per year.
Current diagnostic methods typically fail to detect these diseases until they reach advanced stages, at which point joint damage may already have occurred. Recently, we developed a novel method for the early diagnosis of joint disorders based on the analysis of synovial fluid (SF) using Surface-Enhanced Raman Spectroscopy (SERS). SERS spectra were acquired from the synovial fluid of patients with chondrocalcinosis (n=13), gonarthrosis (n=18), rheumatoid arthritis (n=14), and gout (n=5).
i) to implement supervised AI classification tools to accurately diagnose diseases based on SERS spectra obtained from synovial fluid;
ii) to expand the dataset by acquiring additional SERS spectra from synovial fluid samples.
This internship is part of a collaboration between the BioSpecT unit (Université de Reims Champagne-Ardenne, Reims, France) and the LCBPT (Université Paris Cité, Paris, France).
**Profil**:
**Prise de fonction**:
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