Non-invasive screening tests for preventive healthcare

Time Resolved

Time-Resolved Fluorescence (TRF) uses fluorescence lifetime to gain information about a molecule or its molecular environment, as fluorescence lifetime is characteristic for each fluorescent molecule and is also influenced by the chemical composition of its environment; it can thus be used to characterize a sample. Due the elimination of the natural background fluorescence it can delivery highly accurate information of the molecular environment. With Aqsens proprietary invention (E-TRF) this unique capability is further enhanced.

Basic E-TRF

For the detection and classification of a certain disease AQH’s chemistries are optimised so that the interactions between the europium chemistry, AQH chemistries and the binding function on the disease indicator are measured. This allows direct measurement of the active disease indicator over a wide range of sample concentration eg. like in urine samples.


The Aqsens E-TRF method combines Time Resolved Fluorescence with proprietary chemical modulators (A, B, C, etc..) which interacts with the sample molecules to create a unique fingerprint of the sample. By selecting a proper modulator it is possible to direct method’s sensitivity to the samples’ parameters of interest (specificity) and hence a simple test can be formulated which is sensitive to a specific needs. By combining several parallel measurements a unique and repeatable fingerprint can be created through counting the photons resulting from each interaction by using TRF. Additionally the method uses TRF to eliminate natural background fluorescence and hence it is immune to sample variations in fluorescence which are unrelated to the fingerprint in question.

The individual measurement channels modulated by different chemistries can be 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, that is, they reliably tag some subgroup of positive samples, but others remain undetected. This is where machine learning comes to play. Information content from different channels can be combined intelligently. A combination of noisy, insensitive and less than accurate channels can thus significantly outperform a screening method based on concentration of a single marker molecule and a corresponding reference value.