More than 125 million people wear contact lenses worldwide, and contact lens use is the single greatest risk factor for developing microbial keratitis. We tested the antibacterial activity of multipurpose contact lens solutions and their individual component preservatives against the two most common pathogens causing bacterial keratitis, Pseudomonas aeruginosa and Staphylococcus aureus The in vitro antibacterial activity of five multipurpose contact lens solutions (Opti-Free GP, Boston Simplus, Boston Advance, Menicare GP, and Lobob) was assayed by the standard broth dilution method. Synergy between the preservative components found in the top performing solutions was assayed using checkerboard and time-kill assays. The ISO 14729 criteria and the standard broth dilution method were used to define an optimized contact lens solution formulation against a clinical panel of drug-susceptible and drug-resistant P. aeruginosa and S. aureus strains. Preservatives with the biguanide function group, chlorhexidine and polyaminopropylbiguanide (PAPB), had the best antistaphylococcal activity, while EDTA was the best antipseudomonal preservative. The combination of chlorhexidine and EDTA had excellent synergy against P. aeruginosa A solution formulation containing chlorhexidine (30 ppm), PAPB (5 ppm), and EDTA (5,000 ppm) had three to seven times more antipseudomonal activity than anything available to consumers today. A multipurpose contact lens solution containing a combination of chlorhexidine, PAPB, and EDTA could help to reduce the incidence of microbial keratitis for contact lens users worldwide.

A quantitative understanding of the coupled dynamics of flow and particles in aerosol and droplet transmission associated with speech remains elusive. Here, we summarize an effort that integrates insights into flow-particle dynamics induced by the production plosive sounds during speech with skin-integrated electronic systems for monitoring the production of these sounds. In particular, we uncover diffusive and ballistic regimes separated by a threshold particle size and characterize the Lagrangian acceleration and pair dispersion. Lagrangian dynamics of the particles in the diffusive regime exhibit features of isotropic turbulence. These fundamental findings highlight the value in skin-interfaced wireless sensors for continuously measuring critical speech patterns in clinical settings, work environments, and the home, based on unique neck biomechanics associated with the generation of plosive sounds. We introduce a wireless, soft device that captures these motions to enable detection of plosive sounds in multiple languages through a convolutional neural network approach. This work spans fundamental flow-particle physics to soft electronic technology, with implications in monitoring and studying critical speech patterns associated with aerosol and droplet transmissions relevant to the spread of infectious diseases.

Capabilities in continuous monitoring of key physiological parameters of disease have never been more important than in the context of the global COVID-19 pandemic. Soft, skin-mounted electronics that incorporate high-bandwidth, miniaturized motion sensors enable digital, wireless measurements of mechanoacoustic (MA) signatures of both core vital signs (heart rate, respiratory rate, and temperature) and underexplored biomarkers (coughing count) with high fidelity and immunity to ambient noises. This paper summarizes an effort that integrates such MA sensors with a cloud data infrastructure and a set of analytics approaches based on digital filtering and convolutional neural networks for monitoring of COVID-19 infections in sick and healthy individuals in the hospital and the home. Unique features are in quantitative measurements of coughing and other vocal events, as indicators of both disease and infectiousness. Systematic imaging studies demonstrate correlations between the time and intensity of coughing, speaking, and laughing and the total droplet production, as an approximate indicator of the probability for disease spread. The sensors, deployed on COVID-19 patients along with healthy controls in both inpatient and home settings, record coughing frequency and intensity continuously, along with a collection of other biometrics. The results indicate a decaying trend of coughing frequency and intensity through the course of disease recovery, but with wide variations across patient populations. The methodology creates opportunities to study patterns in biometrics across individuals and among different demographic groups.

Itch is a common clinical symptom and major driver of disease-related morbidity across a wide range of medical conditions. A substantial unmet need is for objective, accurate measurements of itch. In this article, we present a noninvasive technology to objectively quantify scratching behavior via a soft, flexible, and wireless sensor that captures the acousto-mechanic signatures of scratching from the dorsum of the hand. A machine learning algorithm validated on data collected from healthy subjects (n = 10) indicates excellent performance relative to smartwatch-based approaches. Clinical validation in a cohort of predominately pediatric patients (n = 11) with moderate to severe atopic dermatitis included 46 sleep-nights totaling 378.4 hours. The data indicate an accuracy of 99.0% (84.3% sensitivity, 99.3% specificity) against visual observation. This work suggests broad capabilities relevant to applications ranging from assessing the efficacy of drugs for conditions that cause itch to monitoring disease severity and treatment response.