Significance: The increasing availability of powerful light microscopes capable of collecting terabytes of high-resolution 2D and 3D videos in a single day has created a great demand for automated image analysis tools. Tracking the movement of nanometer-scale particles (e.g., virus, proteins, and synthetic drug particles) is critical for understanding how pathogens breach mucosal barriers and for the design of new drug therapies. Our advancement is to use an artificial neural network that provides, first and foremost, substantially improved automation. Additionally, our method improves accuracy compared with current methods and reproducibility across users and laboratories.

Cystic fibrosis (CF) is a recessive genetic disease that is characterised by airway mucus plugging and reduced mucus clearance. There are currently alternative hypotheses that attempt to describe the abnormally viscous and elastic mucus that is a hallmark of CF airways disease, including: 1) loss of CF transmembrane regulator (CFTR)-dependent airway surface volume (water) secretion, producing mucus hyperconcentration-dependent increased viscosity, and 2) impaired bicarbonate secretion by CFTR, producing acidification of airway surfaces and increased mucus viscosity.A series of experiments was conducted to determine the contributions of mucus concentrationversuspH to the rheological properties of airway mucus across length scales from the nanoscopic to macroscopic.For length scales greater than the nanoscopic,i.e.those relevant to mucociliary clearance, the effect of mucus concentration dominated over the effect of airway acidification.Mucus hydration and chemical reduction of disulfide bonds that connect mucin monomers are more promising therapeutic approaches than alkalisation.