Background: Diagnostics for neurodegenerative diseases lack non-invasive approaches suitable for early-stage biochemical screening and routine examination of neuropathology. Biomarkers of neurodegenerative diseases pass through the brain-nose interface (BNI) and accumulate in nasal secretion. Sample collection from the brain-nose interface presents a compelling prospect as basis for a non-invasive molecular diagnosis of neuropathologies. Here, we evaluated a novel medical device (nosecollect) that is tailored for the standardized collection of nasal secretion samples from BNI, focusing on its sample collection safety and efficiency.
Method: A class I medical device (nosecollect) was developed, to enable the standardized collection of nasal secretion exclusively from BNI in a user-friendly, safe, and comfortable manner. We performed a clinical study to test the collection device on a heterogenous cohort (n = 923) at 8 study centers and evaluated its performance to collect sufficient sample volume from the targeted BNI area, its safety and tolerability. Samples were collected by trained medical personnel (medical doctors and nurses).
Results: Nosecollect gathered a mean volume of 452 ± 317 μl from the BNI. Successful positioning of the absorption material (AM) in the BNI was observed in 95 % of the cases. Pain level/level of discomfort and occurrences of adverse events remained minimal (visual analogue scale (VAS) = 1.97 ± 1.99 (range 0-10), adverse events: 1 %, no serious adverse events). Analysis of the nasal secretion sample identified detectable levels of CNS biomarkers in it.
Conclusions: The precision and ergonomic design of nosecollect ensures a standardized, targeted and safe collection of non-diluted nasal secretion samples from BNI, thus outperforming traditional methods such as swabs, lavage etc which are not customized for accessing undiluted samples from BNI. In addition, the device offers a non-invasive and accessible approach for the acquisition of nasal secretion samples from BNI, signifying a crucial step in the future development of a BNI-based non-invasive diagnostic platform for neurodegenerative diseases.
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