A new generation of ‘smart’ and dynamic sensors with high clinical application potential

Integration of nanotechnology with biomaterial science is fostering a new generation of ‘smart’ and dynamic sensors with high clinical application potential. In particular, the combination of plasmonic nanomaterials and biomaterials has recently emerged as a promising alternative to conventional sensing technologies. Nevertheless, achieving an effective translation of physiological information into a readable signal of clinical relevance still represents a significant challenge.

Benefiting from the use of plasmonic nanoparticles, surface-enhanced Raman scattering (SERS) spectroscopy can be used as a sensitive and selective sensing technique. By relying on a SERS fingerprint for every molecule, SERS has shown high potential for detecting multiple analytes in different contexts, including a cancer diagnosis. SERS-based sensors for diagnosis are mostly used in ex vivo settings. Most commonly, the analyses are performed in a laboratory after complex processing of the patient’s sample to reduce eventual interferences. When the approach is label-free, detection is limited by the high complexity of biological samples. Furthermore, the large, laboratory form factor of Raman spectrometers has posed challenges in the clinical acquisition of SERS signals.

The SENTINEL project goes beyond the state of the art by proposing a holistic and ubiquitous approach for cancer surveillance, taking advantage of a portable Raman probe for SERS signalling acquisition. The creation of a SERS-based database of biological patterns at different disease statuses aims to classify the unique SERS profile of a patient as a new cancer diagnosis tool. For that, SENTINEL explores the potential application of plasmonic nanoparticles and label-free SERS, enabled by a minimally invasive implantable biosensor aimed to detect and monitor cancer biomarkers in real-time. Once the cancer biomarkers are in close contact with the nanoparticles within the implanted biosensor, the molecular Raman fingerprint can be acquired by SERS spectroscopy. Data analysis using machine learning aims to interpret acquired spectra and detect suspicious cases for medical follow-up, overcoming the limitation of complex output signal interpretation of complex biological samples

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