Development of a non invassive breath test for early diagnosis of tropical disea...
Development of a non invassive breath test for early diagnosis of tropical diseases
This project aims at demonstrating the feasibility of a non-invasive, safe and patient-friendly methodology for on-site rapid diagnosis of tropical diseases. The proposed approach is based on breath samples analyses, which are eas...
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Información proyecto TROPSENSE
Duración del proyecto: 50 meses
Fecha Inicio: 2014-11-17
Fecha Fin: 2019-01-31
Líder del proyecto
UPPSALA UNIVERSITET
No se ha especificado una descripción o un objeto social para esta compañía.
TRL
4-5
Presupuesto del proyecto
1M€
Fecha límite de participación
Sin fecha límite de participación.
Descripción del proyecto
This project aims at demonstrating the feasibility of a non-invasive, safe and patient-friendly methodology for on-site rapid diagnosis of tropical diseases. The proposed approach is based on breath samples analyses, which are easy to obtain and present no discomfort or risk for patients health. In this study will be enrolled patients with three different types of neglected tropical diseases (Hydatidosis, Leishmaniasis and Dengue) from different geographical locations (Europe, South America and Maghreb). Breath sampling will follow a standardised procedure. Analytical chemistry methods will be employed for the identification of the breath volatile biomarkers of these diseases. A pool of potential nanomaterials with high affinity towards the identified VOCs will be selected (e.g., gold nanoparticles, carbon nanotubes and semiconducting nanowires, either pristine or functionalised with selected hydrophobic organic molecules and/or bio-molecules). For maximising the possibility of success of our methodology, we will investigate the synergic effect of different advanced and complementary chemical sensing techniques: Mid-Infrared Quantum Cascade Laser spectroscopy and different types of Chemical Gas Sensors devices. These techniques are particularly attractive, since they can be miniaturised and are suitable for building on-site portable systems. Advanced pattern recognition algorithms will be employed for building discriminative models for the identification of the fingerprints of the different tropical diseases studied, and multisensors data fusion will be then applied for obtaining enhanced resulyts. A point of care prototype will be proposed on the basis of the results obtained and validated on-site.