Who funded the research behind the Oxford-AstraZeneca COVID-19 vaccine?

Objectives The Oxford-AstraZeneca COVID-19 vaccine (ChAdOx1 nCoV-19, Vaxzevira or Covishield) builds on two decades of research and development (R&D) into chimpanzee adenovirus-vectored vaccine (ChAdOx) technology at the University of Oxford. This study aimed to approximate the funding for the R&D of ChAdOx and the Oxford-AstraZeneca vaccine and to assess the transparency of funding reporting mechanisms. Methods We conducted a scoping review and publication history analysis of the principal investigators to reconstruct R&D funding the ChAdOx technology. We matched award numbers with publicly accessible grant databases. We filed freedom of information (FOI) requests to the University of Oxford for the disclosure of all grants for ChAdOx R&D. Results We identified 100 peer-reviewed articles relevant to ChAdOx technology published between January 2002 and October 2020, extracting 577 mentions of funding bodies from acknowledgements. Government funders from overseas (including the European Union) were mentioned 158 times (27.4%), the UK government 147 (25.5%) and charitable funders 138 (23.9%). Grant award numbers were identified for 215 (37.3%) mentions; amounts were publicly available for 121 (21.0%). Based on the FOIs, until December 2019, the biggest funders of ChAdOx R&D were the European Commission (34.0%), Wellcome Trust (20.4%) and Coalition for Epidemic Preparedness Innovations (17.5%). Since January 2020, the UK government contributed 95.5% of funding identified. The total identified R&D funding was £104 226 076 reported in the FOIs and £228 466 771 reconstructed from the literature search. Conclusion Our study approximates that public and charitable financing accounted for 97%-99% of identifiable funding for the ChAdOx vaccine technology research at the University of Oxford underlying the Oxford-AstraZeneca vaccine until autumn 2020. We encountered a lack of transparency in research funding reporting

Burundi’s ‘Worst Enemy’: the Country’s Fight Against COVID-19

Coronavirus disease 2019 (COVID-19) has proved to be a severe global public health threat, causing high infection rates and mortality worldwide. Burundi was not spared the adverse health outcomes of COVID-19. Although Burundi’s initial response to the COVID-19 pandemic was criticized, hope arose in June 2020 when the new government instituted a plan to slow virus transmission that included public health campaigns, international travel restrictions, and mass testing, all of which proved effective. Burundi has faced many challenges in containing the virus, the first of which was the lack of initial preparedness and appropriate response to COVID-19. This was exacerbated by factors including shortages of personal protective equipment (PPE), limited numbers of life-saving ventilators (around 12 ventilators as of April 2020), and the presence of only one COVID-19 testing center with less than ten technicians in July 2020. Moreover, as Burundi is amongst the poorest countries in the world, some citizens were unable to access necessities such as water and soap, required for compliance with government recommendations regarding hygiene. Interestingly, Burundi did not implement a nationwide lockdown, allowing mass gatherings and public services to continue as usual due to a firm belief in God’s protection. As the daily confirmed cases have tripled since December 2020, Burundi must prepare itself for the threat of a new wave. Establishing precautionary measures to contain the virus and strengthening the health surveillance system in Burundi would significantly positively impact the prevention and management of COVID-19

Synthesis and performance of a thermosetting resin: Acrylated epoxidized soybean oil curing with a rosin‐based acrylamide

A synthesized rosin‐based polymeric monomer, N‐dehydroabietic acrylamide (DHA‐AM), was introduced into an acrylated epoxidized soybean oil (AESO)/DHA‐AM system to afford a thermosetting resin through thermocuring. Different molar ratios of the thermosetting AESO/DHA‐AM samples were obtained through curing in the presence of an initiator, and the curing processes of the AESO/DHA‐AM systems were evaluated by differential scanning calorimetry. The structures and performances of the resulting thermosets were characterized by Fourier transform infrared spectroscopy, dynamic mechanical analysis, elemental analysis, thermogravimetric analysis, and contact angle (θ) analysis. The analyses showed that with increasing content of DHA‐AM introduced into the copolymer, the storage modulus, glass‐transition temperature, thermal stability, and θ values of the cured samples all increased. Moreover, the copolymers changed from hydrophilic materials to hydrophobic materials. The results also demonstrate that the rosin acid derivatives showed comparable properties to those of reported petroleum‐based rigid compounds for the preparation of soybean‐oil‐based thermosets. The presence of DHA‐AM moieties in the composite structures could expand the use of AESO into the development of heat‐resistant and hydrophobic materials. © 2016 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017, 134, 44545.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135053/1/app44545_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/135053/2/app44545.pd

Infection and mortality of healthcare workers worldwide from COVID-19: a systematic review

To estimate COVID-19 infections and deaths in healthcare workers (HCWs) from a global perspective during the early phases of the pandemic. Systematic review. Two parallel searches of academic bibliographic databases and grey literature were undertaken until 8 May 2020. Governments were also contacted for further information where possible. There were no restrictions on language, information sources used, publication status and types of sources of evidence. The AACODS checklist or the National Institutes of Health study quality assessment tools were used to appraise each source of evidence. Publication characteristics, country-specific data points, COVID-19-specific data, demographics of affected HCWs and public health measures employed. A total of 152 888 infections and 1413 deaths were reported. Infections were mainly in women (71.6%, n=14 058) and nurses (38.6%, n=10 706), but deaths were mainly in men (70.8%, n=550) and doctors (51.4%, n=525). Limited data suggested that general practitioners and mental health nurses were the highest risk specialities for deaths. There were 37.2 deaths reported per 100 infections for HCWs aged over 70 years. Europe had the highest absolute numbers of reported infections (119 628) and deaths (712), but the Eastern Mediterranean region had the highest number of reported deaths per 100 infections (5.7). COVID-19 infections and deaths among HCWs follow that of the general population around the world. The reasons for gender and specialty differences require further exploration, as do the low rates reported in Africa and India. Although physicians working in certain specialities may be considered high risk due to exposure to oronasal secretions, the risk to other specialities must not be underestimated. Elderly HCWs may require assigning to less risky settings such as telemedicine or administrative positions. Our pragmatic approach provides general trends, and highlights the need for universal guidelines for testing and reporting of infections in HCWs

An Empirical Comparison of Meta- and Mega-Analysis With Data From the ENIGMA Obsessive-Compulsive Disorder Working Group

Objective: Brain imaging communities focusing on different diseases have increasingly started to collaborate and to pool data to perform well-powered meta- and mega-analyses. Some methodologists claim that a one-stage individual-participant data (IPD) mega-analysis can be superior to a two-stage aggregated data meta-analysis, since more detailed computations can be performed in a mega-analysis. Before definitive conclusions regarding the performance of either method can be drawn, it is necessary to critically evaluate the methodology of, and results obtained by, meta- and mega-analyses. Methods: Here, we compare the inverse variance weighted random-effect meta-analysis model with a multiple linear regression mega-analysis model, as well as with a linear mixed-effects random-intercept mega-analysis model, using data from 38 cohorts including 3,665 participants of the ENIGMA-OCD consortium. We assessed the effect sizes and standard errors, and the fit of the models, to evaluate the performance of the different methods. Results: The mega-analytical models showed lower standard errors and narrower confidence intervals than the meta-analysis. Similar standard errors and confidence intervals were found for the linear regression and linear mixed-effects random-intercept models. Moreover, the linear mixed-effects random-intercept models showed better fit indices compared to linear regression mega-analytical models. Conclusions: Our findings indicate that results obtained by meta- and mega-analysis differ, in favor of the latter. In multi-center studies with a moderate amount of variation between cohorts, a linear mixed-effects random-intercept mega-analytical framework appears to be the better approach to investigate structural neuroimaging data