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Face mask usage is a critical means of limiting SARS-CoV-2 airborne transmission. To the best of our knowledge, a single study reviewing all major life stages of a mask has yet to be conducted. Here, we first describe the production and material sourcing of respirators, surgical/procedural masks, and cloth masks. We then evaluate filtration efficiency, fit, and breathability in estimating emitted viral load and personal compliance. In decontamination, vaporous hydrogen peroxide and ultraviolet germicidal irradiation are feasible and effective methods for large healthcare systems, while washing is recommended for masks with no electrostatic charge (e.g., cotton masks). Finally, we discuss how disposal of masks only contributes marginally to current environmental issues. Insights into the life cycle stages of masks may inform mask use and support mitigation strategies in preventing the spread of respiratory diseases.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission can be mitigated through a combination of preventive and proactive measures. In this review, we first highlight modes of SARS-CoV-2 transmission, quantitatively assess individual mitigation measures, and conclude with a qualitative comparison. We detail how the efficacy of specific face masks must be balanced with their availability, while for comparison, social distancing and good hygiene practices may not be as directly effective as respirators but are widely accessible methods not subject to limited supplies. Controlling environmental setting, testing, and contact tracing are highly effective mitigation practices, but typically require collective action versus the individual activity of the former strategies. Our qualitative comparative assessment of preventative mitigation factors (i.e., face mask usage, social distancing, hygiene, and choice of environment setting) and proactive mitigation measures (i.e., testing, and contact tracing) serves to inform communities on the effectiveness and feasibility of these strategies.

Face mask usage is one of the most effective ways to limit SARS-CoV-2 transmission, but a mask is only useful if user compliance is high. Through anonymous surveys, we show that mask discomfort is the primary source of noncompliance in mask wearing. Further, through these surveys, we identify three critical parameters that dictate mask comfort: air resistance, water vapor permeability, and face temperature change. To validate these parameters in a physiological context, we performed experiments to measure the respiratory rate and change in face temperature while wearing different types of commonly used masks. Finally, using values of these parameters from experiments and the literature, and surveys asking users to rate the comfort of various masks, three machine learning algorithms were trained and tested to generate overall comfort scores for those masks. Although all three models tested performed with an accuracy of approximately 70%, the multiple linear regression model also provides a simple analytical expression to predict the comfort scores for any face mask provided the input parameters. As face mask usage is crucial during the COVID-19 pandemic, the ability of this quantitative framework to predict mask comfort is likely to improve user experience and prevent discomfort-induced noncompliance.

The COVID-19 pandemic has significantly impacted learning as many institutions switched to remote or hybrid instruction. An in-depth assessment of the risk of infection that takes into account environmental setting and mitigation strategies is needed to make safe and informed decisions regarding reopening university spaces. A quantitative model of infection probability that accounts for space-specific parameters is presented to enable assessment of the risk in reopening university spaces at given densities.The model uses local positivity rate, room capacity, mask filtration efficiency, air exchange rate, room volume, and time spent in the space as parameters to calculate infection probabilities in teaching spaces, dining halls, dorms, and shared bathrooms. The model readily calculates infection probabilities in various university spaces, with mask filtration efficiency and air exchange rate being among the dominant variables. When applied to university spaces, this model demonstrated that, under specific conditions that are feasible to implement, in-person classes could be held in large lecture halls with an infection risk over the semester < 1%. Meal pick-ups from dining halls and the use of shared bathrooms in residential dormitories among small groups of students could also be accomplished with low risk. The results of applying this model to spaces at Harvard University (Cambridge and Allston campuses) and Stanford University are reported. Finally, a user-friendly web application was developed using this model to calculate infection probability following input of space-specific variables. The successful development of a quantitative model and its implementation through a web application may facilitate accurate assessments of infection risk in university spaces. In light of the impact of the COVID-19 pandemic on universities, this tool could provide crucial insight to students, faculty, and university officials in making informed decisions.

ObjectiveTranscript and protein expression were interrogated to examine gene locus and pathway regulation in the peripheral blood of active adult dermatomyositis (DM) and juvenile DM patients receiving immunosuppressive therapies.MethodsExpression data from 14 DM and 12 juvenile DM patients were compared to matched healthy controls. Regulatory effects at the transcript and protein level were analyzed by multi‐enrichment analysis for assessment of affected pathways within DM and juvenile DM.ResultsExpression of 1,124 gene loci were significantly altered at the transcript or protein levels across DM or juvenile DM, with 70 genes shared. A subset of interferon‐stimulated genes was elevated, including CXCL10, ISG15, OAS1, CLEC4A, and STAT1. Innate immune markers specific to neutrophil granules and neutrophil extracellular traps were up‐regulated in both DM and juvenile DM, including BPI, CTSG, ELANE, LTF, MPO, and MMP8. Pathway analysis revealed up‐regulation of PI3K/AKT, ERK, and p38 MAPK signaling, whose central components were broadly up‐regulated in DM, while peripheral upstream and downstream components were differentially regulated in both DM and juvenile DM. Up‐regulated components shared by DM and juvenile DM included cytokine:receptor pairs LGALS9:HAVCR2, LTF/NAMPT/S100A8/HSPA1A:TLR4, CSF2:CSF2RA, EPO:EPOR, FGF2/FGF8:FGFR, several Bcl‐2 components, and numerous glycolytic enzymes. Pathways unique to DM included sirtuin signaling, aryl hydrocarbon receptor signaling, protein ubiquitination, and granzyme B signaling.ConclusionThe combination of proteomics and transcript expression by multi‐enrichment analysis broadened the identification of up‐ and down‐regulated pathways among active DM and juvenile DM patients. These pathways, particularly those which feed into PI3K/AKT and MAPK signaling and neutrophil degranulation, may be potential therapeutic targets. image