Benchtop assessment of sealing efficacy and breathability of additively manufactured (AM) face masks.

The onset of the 2019 novel coronavirus disease (COVID-19) led to a shortage of personal protective equipment (PPE), medical devices, and other medical supplies causing many stakeholders and the general public alike to turn to additive manufacturing (AM) as a stopgap when normally accessible devices were not available. However, without a method to test these AM constructs, there continued to be a disconnect between AM suppliers and the community's needs. The objective of this study was to characterize the pressure drop and leakage of four different publicly available AM face mask models with two filter material combinations, as well as to investigate the impact of frame modification techniques including the use of foam strips and hot-water face forming to improve fit when the masks are donned on manikin head forms. AM face mask frame designs were downloaded from public repositories during the early stages of the COVID-19 pandemic. AM face masks were fabricated and tested on manikin head forms within a custom chamber containing dry aerosolized NaCl. Pressure drops, particle penetration, and leakage were evaluated for various flow rates and NaCl concentrations. Results indicated that filter material combination and frame modification played a major role in the overall performance of the AM face masks studied. Filter material combinations showed improved performance when high filtration fabric was used, and the cross-sectional area of the fabric was increased. AM frame modifications appeared to improve AM face mask leakage performance by as much as 69.6%.

Prolonged Physiological Stress Is Associated With a Lower Rate of Exploratory Learning That Is Compounded by Depression.

BACKGROUND: Stress is a major risk factor for depression, and both are associated with important changes in decision-making patterns. However, decades of research have only weakly connected physiological measurements of stress to the subjective experience of depression. Here, we examined the relationship between prolonged physiological stress, mood, and explore-exploit decision making in a population navigating a dynamic environment under stress: health care workers during the COVID-19 pandemic. METHODS: We measured hair cortisol levels in health care workers who completed symptom surveys and performed an explore-exploit restless-bandit decision-making task; 32 participants were included in the final analysis. Hidden Markov and reinforcement learning models assessed task behavior. RESULTS: Participants with higher hair cortisol exhibited less exploration (r = -0.36, p = .046). Higher cortisol levels predicted less learning during exploration (ß = -0.42, false discovery rate [FDR]-corrected p [pFDR] = .022). Importantly, mood did not independently correlate with cortisol concentration, but rather explained additional variance (ß = 0.46, pFDR = .022) and strengthened the relationship between higher cortisol and lower levels of exploratory learning (ß = -0.47, pFDR = .022) in a joint model. These results were corroborated by a reinforcement learning model, which revealed less learning with higher hair cortisol and low mood (ß = -0.67, pFDR = .002). CONCLUSIONS: These results imply that prolonged physiological stress may limit learning from new information and lead to cognitive rigidity, potentially contributing to burnout. Decision-making measures link subjective mood states to measured physiological stress, suggesting that they should be incorporated into future biomarker studies of mood and stress conditions.

A Modified Method for Measuring Pressure Drop in Non-medical Face Masks with Automated Data Acquisition and Analysis.

Background: Non-medical face masks, such as face coverings donned by the general population play an important role in reducing transmission of respiratory pathogens. Pressure drop or breathability of such masks is an important attribute especially with the advent of new standards such as ASTM F3502-21 that have specified pressure drop limits for general use of face coverings. Although several standards are available that discuss pressure drop measurement techniques, the methodologies reported are typically complex or are part of more sophisticated and expensive instruments. Thus, the applicability of such methods is often limited to medical device manufacturers. Objective and Methods: This manuscript adapts from the pressure drop measurements proposed in British Standard EN 14683:2019 and describes a methodology to create a simple 3D printed model of a pressure rig for measuring the breathing resistance across non-medical face masks. The method also enables real time pressure drop data acquisition and analysis of multiple samples or batches using Python and MATLAB scripts. Results: We performed a validation study by comparing the pressure drop obtained for one brand of respirators with our set up and compared it with data obtained by traditional means by CDC. An unpaired two-tailed student t-test (n=3) between the two means implied no statistically significant difference. Conclusion: The method we have developed can be easily implemented at community levels for characterizing the breathability of non-medical grade face masks.

Comprehensive characterization of protective face coverings made from household fabrics.

BACKGROUND: Face coverings constitute an important strategy for containing pandemics, such as COVID-19. Infection from airborne respiratory viruses including Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) can occur in at least three modes; tiny and/or dried aerosols (typically < 1.0 µm) generated through multiple mechanisms including talking, breathing, singing, large droplets (> 0.5 µm) generated during coughing and sneezing, and macro drops transmitted via fomites. While there is a growing number of studies looking at the performance of household materials against some of these situations, to date, there has not been any systematic characterization of household materials against all three modes. METHODS: A three-step methodology was developed and used to characterize the performance of 21 different household materials with various material compositions (e.g. cotton, polyester, polypropylene, cellulose and blends) using submicron sodium chloride aerosols, water droplets, and mucous mimicking macro droplets over an aerosol-droplet size range of ~ 20 nm to 0.6 cm. RESULTS: Except for one thousand-thread-count cotton, most single-layered materials had filtration efficiencies < 20% for sub-micron solid aerosols. However, several of these materials stopped > 80% of larger droplets, even at sneeze-velocities of up to 1700 cm/s. Three or four layers of the same material, or combination materials, would be required to stop macro droplets from permeating out or into the face covering. Such materials can also be boiled for reuse. CONCLUSION: Four layers of loosely knit or woven fabrics independent of the composition (e.g. cotton, polyester, nylon or blends) are likely to be effective source controls. One layer of tightly woven fabrics combined with multiple layers of loosely knit or woven fabrics in addition to being source controls can have sub-micron filtration efficiencies > 40% and may offer some protection to the wearer. However, the pressure drop across such fabrics can be high (> 100 Pa).