Human metabolism is digitized to distinguish between healthy and diseased

We combine biology, deep data, artificial intelligence and cloud technologies to enable digital metabolomic insights for:

How could lifespin help me?

Our proprietary Algorithms allow for the quantitative identification of metabolomic Networks in biological Samples

Standard Hardware & Procedures

lifespin™ Proprietary Data Science (Cloud)

lifespin is developing a breakthrough diagnostic testing platform capable of quantitatively detecting individual metabolomes, i.e., up to hundreds of metabolite concentrations with a single nuclear magnetic resonance (NMR) measurement

This allows quantitative identification of deviations of an individual’s metabolism from a healthy baseline based on lifespin’s proprietary database.

We are building a proprietary Healthcare Diagnostics Software-as-a-Service solution that combines quantitative, digital scanning of metabolomic information with AI-enhanced Data Science.

Key Facts:

Tech Solution

Utilizing its technology, lifespin is performing quantitative in-house measurements of metabolomes for systematic mapping across various health conditions, digitizing metabolomic profiles that include billions of metabolic relationships. These digital metabolic profiles will enable differential diagnosis for the early detection of health conditions, staging of diseases, monitoring of treatment success and personalized medicine.

For quantitative measurement of metabolomes, lifespin uses state-of-the-art, proprietary, nuclear spin-based physical measurement techniques (Nuclear magnetic resonance, NMR) to determine hundreds of metabolites from just a few drops of blood, for example. This is done quickly, cost-effectively and scalable, potentially even for entire populations.

The data obtained by the measurement (spectrum = the sum of all signals from all metabolites) are resolved using proprietary AI algorithms. All metabolites present are identified and quantified by the software. Thus, our proprietary algorithms enable the quantitative identification of metabolomic networks in biological samples.

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