PcP-Sens: Development of a biosensing diagnostic tool for the fast identification of infection by Pneumocystis

The overall aim of PcP-Sens project is to develop a portable nanobiosensor device that will facilitate the easy and fast detection of Pneumocystis jirovecii pneumonia (PcP), one of the most serious and potentially fatal infections encountered in immonosuppressed patients. The single most important diagnostic tool for Pneumocystis infection is a high clinical suspicion. However, specific diagnosis of PcP requires documentation of the microorganism in respiratory specimens. Since Pneumocystis cannot be cultured, the diagnosis of PcP relies on microscopic visualizations on stained respiratory specimens. This makes the detection costly, slow and relying in specialized technicians in clinical settings. In addition, most of the patients are located in third-world or developing areas, where the clinical resources are scarce.

For the diagnosis of Pneumocystis jirovecii pneumonia, there is a clear need of a portable diagnostic tool for quick, accurate, reliable and cost-effective diagnosis that will enable more timely and efficacious medical intervention. The application of a portable, easy-to-use and highly sensitive point-of-care (POC) platform for PcP real-time diagnosis could offer significant advantages over current methods and could afford the diagnosis in underdeveloped and developing countries, helping to open the door to a global access to the diagnostics. The final outcome of this project will be the proof of principle of a hand-held biosensor device based on optical evanescent wave interferometric sensors for the detection and analysis of several DNA strands present in the exhaled breath of people with Pneumocystis jirovecii pneumonia. In addition, the device could offer as well the analysis of the antibiotic-resistance of these fungus, helping in the selection of the right therapy for the patient.

 

Finantial support: Proyecto Intramural CIBER-BBNLogo Ciber BBN
Participating Institutions: CIN2-CSIC, GQNA-CSIC, IBIS
From 2014 to 2017