Development and uptake of an online systematic review platform: the early years of the CAMARADES Systematic Review Facility (SyRF)

Preclinical research is a vital step in the drug discovery pipeline and more generally in helping to better understand human disease aetiology and its management. Systematic reviews (SRs) can be powerful in summarising and appraising this evidence concerning a specific research question, to highlight areas of improvements, areas for further research and areas where evidence may be sufficient to take forward to other research domains, for instance clinical trial. Guidance and tools for preclinical research synthesis remain limited despite their clear utility. We aimed to create an online end-to-end platform primarily for conducting SRs of preclinical studies, that was flexible enough to support a wide variety of experimental designs, was adaptable to different research questions, would allow users to adopt emerging automated tools and support them during their review process using best practice. In this article, we introduce the Systematic Review Facility (https://syrf.org.uk), which was launched in 2016 and designed to support primarily preclinical SRs from small independent projects to large, crowdsourced projects. We discuss the architecture of the app and its features, including the opportunity to collaborate easily, to efficiently manage projects, to screen and annotate studies for important features (metadata), to extract outcome data into a secure database, and tailor these steps to each project. We introduce how we are working to leverage the use of automation tools and allow the integration of these services to accelerate and automate steps in the systematic review workflow

Building a Systematic Online Living Evidence Summary of COVID-19 Research

Throughout the global coronavirus pandemic, we have seen an unprecedented volume of COVID-19 researchpublications. This vast body of evidence continues to grow, making it difficult for research users to keep up with the pace of evolving research findings. To enable the synthesis of this evidence for timely use by researchers, policymakers, and other stakeholders, we developed an automated workflow to collect, categorise, and visualise the evidence from primary COVID-19 research studies. We trained a crowd of volunteer reviewers to annotate studies by relevance to COVID-19, study objectives, and methodological approaches. Using these human decisions, we are training machine learning classifiers and applying text-mining tools to continually categorise the findings and evaluate the quality of COVID-19 evidence

A quasi-one-dimensional bubbly cavitating flow model and comparison with experiments

A bubbly cavitating flow model is constructed for unsteady quasi-one-dimensional and two-dimensional nozzle flows. In each case, the system of model equations is reduced to evolution equations for the flow velocity and bubble radius and the initial and boundary value problems of the evolution equations are formulated. The rest of the flow variables are then related to the solution of the evolution equations. Nozzle flow experiments are also carried out using water. The static wall pressures are measured at different locations of the nozzle and the partial cavitation cloud cycle is recorded using a high speed camera. Results of the numerical simulations obtained for quasi-one-dimensional nozzle flows, seem to capture the measured pressure losses due to cavitation, but they turn out to be insufficient in describing the two-dimensional cavitation cloud structures, suggesting the need for two-dimensional numerical solution of the model equations.Publisher's Versio

Sanki-bir-boyutlu kavitasyonlu lüle akışlarının yeni kabarcık gaz basıncı yasasıyla modellenmesi

Bu çalışmanın amacı, deney sonuçlarıyla uyumlu ve ticari yazılımlara uyarlanabilen bir hidrodinamik kavitasyon modelinin geliştirilmesidir. Bunun için sanki-bir-boyutlu kabarcıklı kavitasyonlu lüle akışları için yeni kabarcık gaz basıncı yasası kullanılarak kabarcık sönüm mekanizmalarını içerecek şekilde bir hidrodinamik kavitasyon modeli geliştirilmiştir. Bu modelde kabarcıklı sıvı iki-fazlı homojen karışım olarak ele alınmış, kabarcık dinamiği için Rayleigh-Plesset denklemi kullanılmış ve kabarcık çekirdekleşmesi göz önünde bulundurulmamıştır. Böylece sanki-bir-boyutlu kavitasyonlu lüle akışları için elde edilen denklem sistemi, kabarcık yarıçapı ve basınç katsayısı için birinci mertebeden denklem sistemi için başlangıç değer problemine dönüştürülmüştür. Başlangıç değer probleminin sayısal çözümü için Runge-Kutta-Fehlberg uyarlanmış adım büyüklüğü yöntemi kullanılmış ve elde edilen sonuçlar deney sonuçlarıyla karşılaştırılarak yorumlanmıştır.The aim of this study is to develop a hydrodynamic cavitation model that is compatible with the results of the experiments and that can be adapted to commercial software. For this reason a hydrodynamic cavitation model that takes into account all of the damping mechanisms using the novel bubble gas pressure law is developed for quasi-one-dimensional bubbly cavitating nozzle flows. In this model the bubbly liquid is assumed to be a twophase homogeneous mixture, the Rayleigh-Plesset equation is employed for bubble dynamics, and bubble nucleation process is neglected. The first order system of equations thus obtained for quasi-one-dimensional cavitating nozzle flows is transformed into an initial value problem for the bubble radius and the pressure coefficient. A numerical code is then written to solve this initial value problem by the adaptive step size RungeKutta-Fehlberg method. Results obtained at the experimental conditions were compared and interpreted with the results of experiments.TR - Dizi

A “LIVING” EVIDENCE SUMMARY OF PRIMARY RESEARCH RELATED TO COVID-19

We aim to generate a “living” evidence summary of all preclinical and clinical primary studies related to COVID-19 for which we will exploit automation tools

Building a Systematic Online Living Evidence Summary of COVID-19 Research

Throughout the global coronavirus pandemic, we have seen an unprecedented volume of COVID-19 researchpublications. This vast body of evidence continues to grow, making it difficult for research users to keep up with the pace of evolving research findings. To enable the synthesis of this evidence for timely use by researchers, policymakers, and other stakeholders, we developed an automated workflow to collect, categorise, and visualise the evidence from primary COVID-19 research studies. We trained a crowd of volunteer reviewers to annotate studies by relevance to COVID-19, study objectives, and methodological approaches. Using these human decisions, we are training machine learning classifiers and applying text-mining tools to continually categorise the findings and evaluate the quality of COVID-19 evidence