SARS-CoV-2 infection of human pluripotent stem cell-derived vascular cells reveals smooth muscle cells as key mediators of vascular pathology during infection (preprint)

Although respiratory symptoms are the most prevalent disease manifestation of infection by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), nearly 20% of hospitalized patients are at risk for thromboembolic events. This prothrombotic state is considered a key factor in the increased risk of stroke, which has been observed clinically during both acute infection and long after symptoms have cleared. Here we developed a model of SARS-CoV-2 infection using human-induced pluripotent stem cell-derived endothelial cells, pericytes, and smooth muscle cells to recapitulate the vascular pathology associated with SARS-CoV-2 exposure. Our results demonstrate that perivascular cells, particularly smooth muscle cells (SMCs), are a specifically susceptible vascular target for SARS-CoV-2 infection. Utilizing RNA sequencing, we characterized the transcriptomic changes accompanying SARS-CoV-2 infection of SMCs, and endothelial cells (ECs). We observed that infected human SMCs shift to a pro-inflammatory state and increase the expression of key mediators of the coagulation cascade. Further, we showed human ECs exposed to the secretome of infected SMCs produce hemostatic factors that can contribute to vascular dysfunction, despite not being susceptible to direct infection. The findings here recapitulate observations from patient sera in human COVID-19 patients and provide mechanistic insight into the unique vascular implications of SARS-CoV-2 infection at a cellular level.

Medical Education During COVID-19: A Global Cross-Sectional Survey of Medical Student Experiences with Remote Learning (preprint)

Background: The COVID-19 pandemic impacted more than one billion medical students worldwide, transforming e-learning into a convenient solution. This study aims to determine how medical schools have adapted their curriculums during the pandemic.Methods: A cross-sectional study was performed using an internet-based survey distributed to medical students in multiple languages in November 2020. Descriptive analysis was performed comparing changes in the delivery of medical education by national economic status.Findings: 1,746 responses were received from 79 different countries. Most respondents reported their institution stopped in-person lectures: 75%(n=40) respondents from low-income countries (LICs), 88%(n=244) from lower-middle income countries (LMICs), 93%(n=882) from upper-middle income countries (UMICs), and 90%(n=238) from high income countries (HICs). A minority of respondents (36%, n=551) reported their medical school used e-learning tools before the pandemic, however, most students report using e-learning tools since (92%, n=1430). 89%(n=1039) of the students enrolled in clinical rotations reported their rotations were paused during the pandemic. In-person clinical rotations were substituted for 25% (n=13) of respondents from LICs vs 58% (n=152) from HICs. 42% (n=23) of students from LICs reported their internet connection was not sufficient for online classes compared to 11% (n=28) in HICs. Interpretation: Most medical schools transitioned their curriculum to e-learning due to COVID-19, with students from LICs and LMICs facing significant challenges due to lack of quality internet connection. Specific policies are needed to ensure equity in e-learning for all, regardless of socioeconomic status.Funding Statement: None.Declaration of Interests: None.Ethics Approval Statement: This study was considered exempt by the Institutional Review Board at the Boston Children's Hospital Ethical Committee (IRB-P00036561) on September 9, 2020.

Quantifying Face Mask Comfort (preprint)

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.

Risk quantification for SARS-CoV-2 infection through airborne transmission in university settings (preprint)

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.