3D computational cannula fluorescence microscopy enabled by artificial neural networks.

Computational cannula microscopy (CCM) is a high-resolution widefield fluorescence imaging approach deep inside tissue, which is minimally invasive. Rather than using conventional lenses, a surgical cannula acts as a lightpipe for both excitation and fluorescence emission, where computational methods are used for image visualization. Here, we enhance CCM with artificial neural networks to enable 3D imaging of cultured neurons and fluorescent beads, the latter inside a volumetric phantom.

Computational cannula microscopy of neurons using neural networks.

Computational cannula microscopy is a minimally invasive imaging technique that can enable high-resolution imaging deep inside tissue. Here, we apply artificial neural networks to enable real-time, power-efficient image reconstructions that are more efficiently scalable to larger fields of view. Specifically, we demonstrate widefield fluorescence microscopy of cultured neurons and fluorescent beads with a field of view of 200 µm (diameter) and a resolution of less than 10 µm using a cannula of diameter of only 220 µm.

Theoretical Insights into the Interactions between Star-Shaped Antimicrobial Polypeptides and Bacterial Membranes.

A structurally nanoengineered antimicrobial polypeptide consisting of lysine and valine residues is a new class of antimicrobial agent with superior antibacterial activity against multidrug-resistant bacteria and low toxicity toward mammalian cells. Utilizing coarse-grained models, we studied the interactions of microbial cytoplasmic membranes with polypeptides of either (KV) (star-KV) or CM15 (star-CM15). Our computational results verify the low toxicity of polypeptides of (KV) toward the dipalmitoyl phosphatidylcholine bilayer.