Non-invasive screening tests for preventive healthcare

Time Resolved
Fluorescence


Time-Resolved Fluorescence (TRF) uses fluorescence lifetime to gain information about a molecule or its molecular environment.

As fluorescence’s lifetime is characteristic for each fluorescent molecule and also influenced by the chemical composition of its environment, it can be used to characterize a sample.

When the natural background fluorescence is eliminated, the sample’s fluorescence can be used to deliver highly accurate information about its molecular environment. Aqsens Health’s proprietary invention (E-TRF) enhances this unique capability even further.

Basic E-TRF
interactions


Aqsens Health’s modulators are optimized for the detection and classification of diseases by measuring the interactions between the label, AQH modulators, and the binding function on the disease indicator. This allows the direct measurement of active disease indicators over a wide range of sample concentrations, such as saliva or urine samples.

E-TRF measurement
process


The Aqsens E-TRF method combines Time Resolved Fluorescence with proprietary modulators, which interact with the sample molecules to create a unique fingerprint of the sample. By selecting a proper modulator it is possible to direct the method's sensitivity to the samples’ parameters of interest (specificity) and a simple test can be formulated. By combining several parallel measurements E-TRF creates a unique and repeatable fingerprint by counting the photons from each interaction. Additionally, the method uses TRF to eliminate natural background fluorescence and is therefore immune to sample variations in fluorescence which are unrelated to the fingerprint in question.

The individual measurements channels using different modulators are sensitive to several chemical compounds or even families of molecules. Only some of them might be valid markers for the disease in question. In addition, some markers can be highly specific but only moderately sensitive, meaning that they reliably tag some subgroups of positive samples, while others remain undetected. This is where artificial intelligence comes to play. A combination of channels with high sensitivity to complementary specificities can outperform a screening method based on a single biomarker and its corresponding reference value.