Solid state nanopores aided by machine learning for identification and quantification of heparins and glycosaminoglycans
Abstract:
The present disclosure provides a method for identifying and quantifying sulfated glycosaminoglycans, including for example heparin, by passing a sample through nanopores. The glycosaminoglycans sample is measured in microliter quantities, at nanomolar concentrations with detection of impurities below 0.5%, and a dynamic range over five decades of magnitude with a trained machine learning algorithm.
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