Development of a therapeutic delivery platform with reduced colonization potential.

Bacterial biotherapeutic delivery vehicles have the potential to treat a variety of diseases. This approach obviates the need to purify the recombinant effector molecule, allows delivery of therapeutics via oral or intranasal administration, and protects the effector molecule during gastrointestinal transit. Lactic acid bacteria have been broadly developed as therapeutic delivery vehicles though risks associated with the colonization of a genetically modified microorganism have so-far not been addressed.

Doxorubicin-Loaded Ultrasmall Gold Nanoparticles (1.5 nm) for Brain Tumor Therapy and Assessment of Their Biodistribution.

Ultrasmall gold nanoparticles (1.5 nm) were covalently conjugated with doxorubicin (AuDox) and AlexaFluor647 (AuAF647) to assess their biodistribution and their efficiency toward brain tumors (glioblastoma). A thorough characterization by transmission electron microscopy, small-angle X-ray scattering, and differential centrifugal sedimentation confirmed their uniform ultrasmall nature which makes them very mobile in the body.

Increased Cytotoxicity of Bimetallic Ultrasmall Silver-Platinum Nanoparticles (2 nm) on Cells and Bacteria in Comparison to Silver Nanoparticles of the Same Size.

Ultrasmall nanoparticles (diameter 2 nm) of silver, platinum, and bimetallic nanoparticles (molar ratio of Ag:Pt 0:100; 20:80; 50:50; 70:30; 100:0), stabilized by the thiolated ligand glutathione, were prepared and characterized by transmission electron microscopy, differential centrifugal sedimentation, X-ray photoelectron spectroscopy, small-angle X-ray scattering, X-ray powder diffraction, and NMR spectroscopy in aqueous dispersion. Gold nanoparticles of the same size were prepared as control. The particles were fluorescently labeled by conjugation of the dye AlexaFluor-647 via copper-catalyzed azide-alkyne cycloaddition after converting amine groups of glutathione into azide groups.

Simulated HRTEM images of nanoparticles to train a neural network to classify nanoparticles for crystallinity.

Machine learning approaches for image analysis require extensive training datasets for an accurate analysis. This also applies to the automated analysis of electron microscopy data where training data are usually created by manual annotation. Besides nanoparticle shape and size distribution, their internal crystal structure is a major parameter to assess their nature and their physical properties.