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Airborne transmission by droplets and aerosols is known to play a critical role in the spread of many viruses amongst which are the common flu and the more recent SARS-CoV-2 viruses. In the case of SARS-CoV-2, the nasal cavity not only constitutes an important viral entry point, but also a primary site of infection (Sungnak W. et al. Nat. Med. 26:681–687. https://doi.org/10.1038/s41591-020-0868-6, 2020).. Although face masks are a well-established preventive measure, development of novel and easy-to-use prophylactic measures would be highly beneficial in fighting viral spread and the subsequent emergence of variants of concern (Tao K. et al. Nat Rev Genet 22:757–773. https://doi.org/10.1038/s41576-021-00408-x, 2021). Our group has been working on optimizing a nasal spray delivery system that deposits particles inside the susceptible regions of the nasal cavity to act as a mechanical barrier to impede viral entry. Here, we identify computationally the delivery parameters that maximize the protection offered by this barrier. We introduce the computational approach and quantify the protection rate obtained as a function of a broad range of delivery parameters. We also introduce a modified design and demonstrate that it significantly improves deposition, thus constituting a viable approach to protect against nasal infection of airborne viruses. We then discuss our findings and the implications of this novel system on the prevention of respiratory diseases and targeted drug delivery.

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Electrical stimulation of the peripheral nervous system is a promising therapeutic option for several conditions; however, its effects on tissue and the safety of the stimulation remain poorly understood. In order to devise stimulation protocols that enhance therapeutic efficacy without the risk of causing tissue damage, we constructed computational models of peripheral nerve and stimulation cuffs based on extremely high-resolution cross-sectional images of the nerves using the most recent advances in computing power and machine learning techniques. We developed nerve models using nonstimulated (healthy) and over-stimulated (damaged) rat sciatic nerves to explore how nerve damage affects the induced current density distribution. Using our in-house computational, quasi-static, platform, and the Admittance Method (AM), we estimated the induced current distribution within the nerves and compared it for healthy and damaged nerves. We also estimated the extent of localized cell damage in both healthy and damaged nerve samples. When the nerve is damaged, as demonstrated principally by the decreased nerve fiber packing, the current penetrates deeper into the over-stimulated nerve than in the healthy sample. As safety limits for electrical stimulation of peripheral nerves still refer to the Shannon criterion to distinguish between safe and unsafe stimulation, the capability this work demonstrated is an important step toward the development of safety criteria that are specific to peripheral nerve and make use of the latest advances in computational bioelectromagnetics and machine learning, such as Python-based AM and CNN-based nerve image segmentation.