title: Approximate Bayesian computation for estimating number concentrations of monodisperse nanoparticles in suspension by optical microscopy.
abstract: We present an approximate Bayesian computation scheme for estimating number concentrations of monodisperse diffusing nanoparticles in suspension by optical particle tracking microscopy. The method is based on the probability distribution of the time spent by a particle inside a detection region. We validate the method on suspensions of well-controlled reference particles. We illustrate its usefulness with an application in gene therapy, applying the method to estimate number concentrations of plasmid DNA molecules and the average number of DNA molecules complexed with liposomal drug delivery particles.
authors.affiliation: SP Food and Bioscience, Soft Materials Science, Göteborg, Sweden and School of Energy and Resources, UCL Australia, University College London, Adelaide, Australia.
authors.forename: Magnus
authors.initials: M
authors.lastname: Röding
authors.affiliation: Laboratory of General Biochemistry and Physical Pharmacy, Ghent University, Ghent, Belgium and Center for Nano- and Biophotonics, Ghent University, Ghent, Belgium.
authors.forename: Elisa
authors.initials: E
authors.lastname: Zagato
authors.affiliation: Laboratory of General Biochemistry and Physical Pharmacy, Ghent University, Ghent, Belgium and Center for Nano- and Biophotonics, Ghent University, Ghent, Belgium.
authors.forename: Katrien
authors.initials: K
authors.lastname: Remaut
authors.affiliation: Laboratory of General Biochemistry and Physical Pharmacy, Ghent University, Ghent, Belgium and Center for Nano- and Biophotonics, Ghent University, Ghent, Belgium.
authors.forename: Kevin
authors.initials: K
authors.lastname: Braeckmans
mesh_terms: D001499:Bayes Theorem; D008853:Microscopy; D053758:Nanoparticles / Q000032:analysis*; D010316:Particle Size; D013223:Statistics as Topic; D013535:Suspensions
chemical_list: D013535:Suspensions