ArticleScientific reports2022
The measuring aerosol spreading during countermeasures (MASC) study presents an automated system to investigate face mask efficacy and other aerosol countermeasures in varying environments.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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0 citing papers in PubMed, 2 citations in OpenAlex.
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Authors and funding
4 authors at 1 institution in 1 country.
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Abstract
The COVID-19 pandemic exemplified the importance of research on personal protective equipment. In specific, understanding how effective surgical masks or particulate filter respirators are at reducing the transmission of infectious diseases has suddenly become one of the most pressing issues for legislators, regulators, and everyday life. However, there was a lack of available scientific platforms to assess this issue. Therefore, we designed and built a system entitled MASC: measuring aerosol spreading during countermeasures. This platform allows the simulation of various everyday situations and evaluation of the efficacy of masks and respirators in reducing the amount of inhaled particulate matter from the air. Furthermore, MASC can be used to investigate how aerosols propagate in closed spaces, such as offices or classrooms. It can be used to generate aerosols on command and control the room temperature, humidity, and wind speed. Up to four laser aerosol spectrometers can be read simultaneously, and a camera can automatically take pictures to evaluate the efficacy of countermeasures to prevent the spread of aerosols. The aerosol generation, measurement periods, and the number of repetitions for an experiment can be configured digitally and are executed by a computer automatically. A website displays the data in real time and allows monitoring of the experiment. Upon completion, statistical values are calculated automatically to accelerate the evaluation of the gathered data. Codes and technical drawings in this present methodology publication are open source and can be used by the scientific community to establish similar systems.
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