Elucidation of the pathway for biosynthesis of saponin adjuvants from the soapbark tree.

The Chilean soapbark tree (Quillaja saponaria) produces soap-like molecules called QS saponins that are important vaccine adjuvants. These highly valuable compounds are sourced by extraction from the bark, and their biosynthetic pathway is unknown. Here, we sequenced the Q. saponaria genome. Through genome mining and combinatorial expression in tobacco, we identified 16 pathway enzymes that together enable the production of advanced QS pathway intermediates that represent a bridgehead for adjuvant bioengineering. We further identified the enzymes needed to make QS-7, a saponin with excellent therapeutic properties and low toxicity that is present in low abundance in Q. saponaria bark extract. Our results enable the production of Q. saponaria vaccine adjuvants in tobacco and open the way for new routes to access and engineer natural and new-to-nature immunostimulants.

Emergency medicine patient wait time multivariable prediction models: a multicentre derivation and validation study.

OBJECTIVE: Patients, families and community members would like emergency department wait time visibility. This would improve patient journeys through emergency medicine. The study objective was to derive, internally and externally validate machine learning models to predict emergency patient wait times that are applicable to a wide variety of emergency departments. METHODS: Twelve emergency departments provided 3 years of retrospective administrative data from Australia (2017-2019). Descriptive and exploratory analyses were undertaken on the datasets. Statistical and machine learning models were developed to predict wait times at each site and were internally and externally validated. Model performance was tested on COVID-19 period data (January to June 2020). RESULTS: There were 1 930 609 patient episodes analysed and median site wait times varied from 24 to 54 min. Individual site model prediction median absolute errors varied from±22.6 min (95% CI 22.4 to 22.9) to ±44.0 min (95% CI 43.4 to 44.4). Global model prediction median absolute errors varied from ±33.9 min (95% CI 33.4 to 34.0) to ±43.8 min (95% CI 43.7 to 43.9). Random forest and linear regression models performed the best, rolling average models underestimated wait times. Important variables were triage category, last-k patient average wait time and arrival time. Wait time prediction models are not transferable across hospitals. Models performed well during the COVID-19 lockdown period. CONCLUSIONS: Electronic emergency demographic and flow information can be used to approximate emergency patient wait times. A general model is less accurate if applied without site-specific factors.

The Impact of the COVID-19 Pandemic on Demand for Emergency Ambulances in Victoria, Australia.

Objective: Relatively little has been reported about the impact of COVID-19 restrictions on emergency ambulance services. We describe the influence of the COVID-19 pandemic on the emergency ambulance system in Victoria, Australia.Methods: We performed an interrupted time series analysis of consecutive calls for ambulance from January 2018 to February 2021, including two waves of COVID-19. The COVID-19 lockdown period included seven months of stay-at-home restrictions (16/03/2020-18/10/2020). Nineteen weeks of post-lockdown data were included (19/10/2020-28/02/2021).Results: In total, 2,356,326 consecutive calls were included. COVID-19 lockdown was associated with an absolute reduction of 64,991 calls (almost 2,100 calls/week). According to time series analysis, lockdown was associated with a 12.6% reduction in weekly calls (IRR = 0.874 [95% CI 0.811, 0.941]), however no change in long-term trend (IRR = 1.000 [95% CI 0.996, 1.003]). During lockdown, the long-term trend of attendances to patients with suspected acute coronary syndromes (ACS, IRR = 1.006 [95% CI 1.004, 1.009]) and mental health-related issues (IRR = 1.005 [95% CI 1.002, 1.008]) increased. After lockdown, the call volume was 5.6% below pre-COVID-19 predictions (IRR = 0.944 [95% CI 0.909, 0.980]), however attendances for suspected ACS were higher than predicted (IRR = 1.069 [95% CI 1.009, 1.132]). Ambulance response times deteriorated, and total case times were longer than prior to the pandemic, driven predominantly by extended hospital transfer times.Conclusion: The COVID-19 pandemic had a dramatic impact on the emergency ambulance system. Despite lower call volumes post-lockdown than predicted, we observed deteriorating ambulance response times, extended case times and hospital delays. The pattern of attendance to patients with suspected ACS potentially highlights the collateral burden of delaying treatment for urgent conditions.

Family Medicine With Refugee Newcomers During the COVID-19 Pandemic.

Certain members of society are disproportionately affected by the COVID-19 crisis and the added strain being placed on already overextended health care systems. In this article, we focus on refugee newcomers. We outline vulnerabilities refugee newcomers face in the context of COVID-19, including barriers to accessing health care services, disproportionate rates of mental health concerns, financial constraints, racism, and higher likelihoods of living in relatively higher density and multigenerational dwellings. In addition, we describe the response to COVID-19 by a community-based refugee primary health center in Ontario, Canada. This includes how the clinic has initially responded to the crisis as well as recommendations for providing services to refugee newcomers as the COVID-19 crisis evolves. Recommendations include the following actions: (1) consider social determinants of health in the new context of COVID-19; (2) provide services through a trauma-informed lens; (3) increase focus on continuity of health and mental health care; (4) mobilize International Medical Graduates for triaging patients based on COVID-19 symptoms; and (5) diversify communication efforts to educate refugees about COVID-19.