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Three-dimensional renal tissues that emulate the cellular composition, geometry, and function of native kidney tissue would enable fundamental studies of filtration and reabsorption. Here, we have created 3D vascularized proximal tubule models composed of adjacent conduits that are lined with confluent epithelium and endothelium, embedded in a permeable ECM, and independently addressed using a closed-loop perfusion system to investigate renal reabsorption. Our 3D kidney tissue allows for coculture of proximal tubule epithelium and vascular endothelium that exhibits active reabsorption via tubular–vascular exchange of solutes akin to native kidney tissue. Using this model, both albumin uptake and glucose reabsorption are quantified as a function of time. Epithelium–endothelium cross-talk is further studied by exposing proximal tubule cells to hyperglycemic conditions and monitoring endothelial cell dysfunction. This diseased state can be rescued by administering a glucose transport inhibitor. Our 3D kidney tissue provides a platform for in vitro studies of kidney function, disease modeling, and pharmacology.

Many studies report the seroprevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibodies. We aimed to synthesize seroprevalence data to better estimate the level and distribution of SARS-CoV-2 infection, identify high-risk groups, and inform public health decision making. In this systematic review and meta-analysis, we searched publication databases, preprint servers, and grey literature sources for seroepidemiological study reports, from January 1, 2020 to December 31, 2020. We included studies that reported a sample size, study date, location, and seroprevalence estimate. We corrected estimates for imperfect test accuracy with Bayesian measurement error models, conducted meta-analysis to identify demographic differences in the prevalence of SARS-CoV-2 antibodies, and meta-regression to identify study-level factors associated with seroprevalence. We compared region-specific seroprevalence data to confirmed cumulative incidence. PROSPERO: CRD42020183634. We identified 968 seroprevalence studies including 9.3 million participants in 74 countries. There were 472 studies (49%) at low or moderate risk of bias. Seroprevalence was low in the general population (median 4.5%, IQR 2.4-8.4%); however, it varied widely in specific populations from low (0.6% perinatal) to high (59% persons in assisted living and long-term care facilities). Median seroprevalence also varied by Global Burden of Disease region, from 0.6% in Southeast Asia, East Asia and Oceania to 19.5% in Sub-Saharan Africa (p<0.001). National studies had lower seroprevalence estimates than regional and local studies (p<0.001). Compared to Caucasian persons, Black persons (prevalence ratio [RR] 3.37, 95% CI 2.64-4.29), Asian persons (RR 2.47, 95% CI 1.96-3.11), Indigenous persons (RR 5.47, 95% CI 1.01-32.6), and multi-racial persons (RR 1.89, 95% CI 1.60-2.24) were more likely to be seropositive. Seroprevalence was higher among people ages 18-64 compared to 65 and over (RR 1.27, 95% CI 1.11-1.45). Health care workers in contact with infected persons had a 2.10 times (95% CI 1.28-3.44) higher risk compared to health care workers without known contact. There was no difference in seroprevalence between sex groups. Seroprevalence estimates from national studies were a median 18.1 times (IQR 5.9-38.7) higher than the corresponding SARS-CoV-2 cumulative incidence, but there was large variation between Global Burden of Disease regions from 6.7 in South Asia to 602.5 in Sub-Saharan Africa. Notable methodological limitations of serosurveys included absent reporting of test information, no statistical correction for demographics or test sensitivity and specificity, use of non-probability sampling and use of non-representative sample frames. Most of the population remains susceptible to SARS-CoV-2 infection. Public health measures must be improved to protect disproportionately affected groups, including racial and ethnic minorities, until vaccine-derived herd immunity is achieved. Improvements in serosurvey design and reporting are needed for ongoing monitoring of infection prevalence and the pandemic response.

ObjectiveTo describe and synthesise studies of SARS-CoV-2 seroprevalence by occupation prior to the widespread vaccine roll-out.MethodsWe identified studies of occupational seroprevalence from a living systematic review (PROSPERO CRD42020183634). Electronic databases, grey literature and news media were searched for studies published during January–December 2020. Seroprevalence estimates and a free-text description of the occupation were extracted and classified according to the Standard Occupational Classification (SOC) 2010 system using a machine-learning algorithm. Due to heterogeneity, results were synthesised narratively.ResultsWe identified 196 studies including 591 940 participants from 38 countries. Most studies (n=162; 83%) were conducted locally versus regionally or nationally. Sample sizes were generally small (median=220 participants per occupation) and 135 studies (69%) were at a high risk of bias. One or more estimates were available for 21/23 major SOC occupation groups, but over half of the estimates identified (n=359/600) were for healthcare-related occupations. ‘Personal Care and Service Occupations’ (median 22% (IQR 9–28%); n=14) had the highest median seroprevalence.ConclusionsMany seroprevalence studies covering a broad range of occupations were published in the first year of the pandemic. Results suggest considerable differences in seroprevalence between occupations, although few large, high-quality studies were done. Well-designed studies are required to improve our understanding of the occupational risk of SARS-CoV-2 and should be considered as an element of pandemic preparedness for future respiratory pathogens.

Wearable sensors can continuously and passively detect potential respiratory infections before or absent symptoms. However, the population-level impact of deploying these devices during pandemics is unclear. We built a compartmental model of Canada’s second COVID-19 wave and simulated wearable sensor deployment scenarios, systematically varying detection algorithm accuracy, uptake, and adherence. With current detection algorithms and 4% uptake, we observed a 16% reduction in the second wave burden of infection; however, 22% of this reduction was attributed to incorrectly quarantining uninfected device users. Improving detection specificity and offering confirmatory rapid tests each minimized unnecessary quarantines and lab-based tests. With a sufficiently low false positive rate, increasing uptake and adherence became effective strategies for scaling averted infections. We concluded that wearable sensors capable of detecting presymptomatic or asymptomatic infections have potential to help reduce the burden of infection during a pandemic; in the case of COVID-19, technology improvements or supporting measures are required to keep social and resource costs sustainable.

Wearable sensors can detect potential respiratory infections before or absent symptoms through continuous, passive monitoring of pathogen-elicited physiological changes. While numerous efforts have been made to develop wearable sensor-based infection detection algorithms, the population-level impact of deploying such technology during a pandemic has not been explored. In this thesis, we used mathematical modelling to study wearable sensor- based pandemic mitigation strategies. Using SARS-CoV-2 as an illustrative example, we constructed a compartmental model of Canada’s second COVID-19 wave, simulated counterfactual wearable sensor deployment scenarios, and systematically investigated the role of detection algorithm accuracy, uptake, and adherence. With currently available detection algorithms and 4% uptake, we observed a 16% reduction in the second wave burden of infection; however, 22% of this reduction was attributed to incorrectly quarantining uninfected device users. Improving detection specificity and offering confirmatory rapid tests each minimised unnecessary quarantines and lab-based tests. With a sufficiently low false positive rate, increasing uptake and adherence became effective strategies for scaling averted infections. We concluded that wearable sensors capable of detecting presymptomatic or asymptomatic infections have potential to help reduce the burden of infection during pandemics. In the case of COVID-19, technology improvements or supporting measures are required to keep social and resource costs sustainable.

Abstract Population-level immune surveillance, which includes monitoring exposure and assessing vaccine-induced immunity, is a crucial component of public health decision-making during a pandemic. Serosurveys estimating the prevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibodies in the population played a key role in characterizing SARS-CoV-2 epidemiology during the early phases of the pandemic. Existing serosurveys provide infrastructure to continue immune surveillance but must be adapted to remain relevant in the SARS-CoV-2 vaccine era. Here, we delineate how SARS-CoV-2 serosurveys should be designed to distinguish infection- and vaccine-induced humoral immune responses to efficiently monitor the evolution of the pandemic. We discuss how serosurvey results can inform vaccine distribution to improve allocation efficiency in countries with scarce vaccine supplies and help assess the need for booster doses in countries with substantial vaccine coverage.

Wearable sensors can continuously and passively detect potential respiratory infections before or absent symptoms. However, the population-level impact of deploying these devices during pandemics is unclear. We built a compartmental model of Canada’s second COVID-19 wave and simulated wearable sensor deployment scenarios, systematically varying detection algorithm accuracy, uptake, and adherence. With current detection algorithms and 4% uptake, we observed a 16% reduction in the second wave burden of infection; however, 22% of this reduction was attributed to incorrectly quarantining uninfected device users. Improving detection specificity and offering confirmatory rapid tests each minimized unnecessary quarantines and lab-based tests. With a sufficiently low false positive rate, increasing uptake and adherence became effective strategies for scaling averted infections. We concluded that wearable sensors capable of detecting presymptomatic or asymptomatic infections have potential to help reduce the burden of infection during a pandemic; in the case of COVID-19, technology improvements or supporting measures are required to keep social and resource costs sustainable. Author summary Find-Test-Trace-Isolate (FTTI) systems reliant on lab-based tests are important components of pandemic mitigation but can miss infectious individuals that do not have symptoms and may be limited by slow test result turnaround times. Wearable sensors show promise in continuous, passive detection of respiratory infections, before or absent symptoms. Here, we used a mathematical model to study the counterfactual impact of deploying wearable sensors to detect SARS-CoV-2 infections during Canada’s second COVID-19 wave. We observed a meaningful reduction in the burden of infection but also found that false positive alerts resulting from imperfect detection specificity resulted in high social and resource costs. Improving detection specificity and offering rapid antigen tests to confirm positive alerts both helped minimize unnecessary quarantines and lab-based tests. We found that once the false positive rate was sufficiently reduced, increasing uptake and adherence became effective strategies to scale the number of averted infections. Our study demonstrates that wearable sensors capable of detecting infections before or absent symptoms are promising pandemic mitigation tools. It also provides intuition around how detection performance, uptake, adherence, and supporting policies might shape the impact of broad scale wearable sensor deployment.

One of the best ways to improve care for patients affected by Parkinson's disease is to increase the knowledge and awareness of those who could potentially be caregivers for patients. The aim of this study was to evaluate the awareness of young adults in Waterloo, Ontario, Canada. A survey was circulated via social media outlets to young adults in Waterloo, Canada. The survey was voluntary. The consent form on the first page acknowledged that there are no benefits/costs to participants, and required participants to consent to the study. The second page collected demographic information and the third page was used to determine the actual and perceived awareness of Parkinson's disease. A total of 19 consenting participants completed the survey. 58% of young adults are knowledgeable, which is higher than the statistics that 32% have a family member affected by the disease and that 26% perceived themselves to be knowledgeable. Rigorous educational programs can be developed to inform the community more about the disease, so that young adults can be better caregivers for those currently and who may in the future be affected by the disease.

Research has shown a correlation between helmet wearing rates and age - the older adolescents got, the less they wear their helmets. This study was conducted to determine whether the negative correlation continues among young adults in Waterloo, Canada. A questionnaire was developed, inquiring about bicycle and helmet use, and circulated to young adults in Waterloo. There was an overall 46% helmet wearing rate during commute to school and 49% rate when commuting during recreational time. Despite the differences in helmet wearing rates among the age groups, all age groups were equally able to identify that it is safer to wear a helmet. It is alarming that the negative correlation noted amongst adolescents continues in the youngest cohort in this study, but one may take relief that rates seem to rebound as young adults age. To increase awareness about the importance of wearing a bike helmet, steps may be taken towards advocating for safer cycling practices and perhaps findings solutions to the difficulties related with storing helmets.

Studies conducted amongst young adults revealed that there existed some difference in helmet-wearing rates within Canada, between the different regions. However, it would be interesting to see whether different rates persist in different countries. The aim of this study was to survey bicycle and helmet use in New York State in the United States of America. A survey was circulated in the New York State region, targeted toward young adults. It was advertised as anonymous, and had two components - the first portion aimed to determine bicycle and helmet use of young adults during their commute to university, while the latter half looked into the usage rate during young adults' recreational times. The survey was completed by 750 consenting young adults, of which 358 were 19 years and below, 274 between the ages of 20 and 22 years old, and 118 above the age of 22 years old. A smaller proportion of young adults in New York State identify themselves as a student cyclist, while a much larger group identifies themselves as a recreational cyclist, compared to previous studies. More frequent helmet use and shorter commutes show that New York State young adults have a different mindset than young adults previously reported in the literature. The differences may be accounted for by different climate, cultural and environmental factors, and may be investigated in future studies.