F1000Research: Rapid dissemination of research when it’s needed most
F1000Research facilitates the rapid and transparent sharing of research by providing fast, open publication alongside access to any underlying data, software code and resources. All research articles are reviewed post publication, with expert peer review reports freely available.
F1000Research is particularly suited to fast-paced areas of research such as infectious diseases research and epidemiology, where immediate access to robust and rigorous science is essential for informing clinical and public health responses in real time.
Research on any aspect of epidemiology or infectious disease outbreaks is welcome including but not limited to:
- Epidemiological modelling
- Disease transmission
- Pathogen genetics and genomics
- Antimicrobial resistance
- Immune defense mechanisms
- Guidelines on protective equipment
- Clinical case reports
- In silico, in vitro and in vivo candidate therapeutics studies
Articles published on F1000Research are subject to our Article Processing Charges.
Why publish on F1000Research
1. Rapid publication
Our rapid publication model offers an innovative alternative to traditional journals publishing, where long delays between submission and publication are expected. On F1000Research, your article could be published online, peer-review ready and in a fully citable format, in as few as 14 days, so your research can make an impact sooner
2. Maximize the impact of your work with Open Access
With fully open publication as standard on F1000Research, anyone can read, cite, and use your work – including policymakers, practitioners, and fellow researchers. Alongside open publication of the article itself, F1000Research also advocates a rigorous Open Data policy so that others can replicate your study, reproduce the original findings, or reuse the data to support their own work.
3. Support transparency and reproducibility
The F1000Research publication model puts transparency and reproducibility at the heart of what we do. Our platform facilitates transparent sharing of research by publishing all articles openly, alongside access to any underlying data, software, code, and other resources – even the peer review reports. This focus on transparent, reproducible research supports the credibility and rigor of your published work.
4. Wide range of research outputs
At F1000Research, we really hate research waste. That's why we support the publication of Study Protocols (as Registered Reports as required), Data Notes, Software Tool Articles and Case Reports, alongside Systematic Reviews and Research Articles. We welcome both confirmatory and negative results, as well as null studies.
5. Article versioning
Our versioned publication system means that updates to studies can be made easily and quickly, without the need to publish a new paper. Versions are linked and are individually citable, and older versions will display a clear notification that a new version has been uploaded so that readers are always kept up to date with the latest developments in your work.
6. Increase the visibility of your research
Once your article has passed peer review, inclusion in Scopus, PubMed, Google Scholar and other major indexers will help it reach more readers. On F1000Research, your article is supported by machine-readable metadata to boost discoverability
Introducing the Disease Outbreaks Gateway
Robert L. Kruse
A novel coronavirus (2019-nCoV) originating in Wuhan, China presents a potential respiratory viral pandemic to the world population. Current efforts are focused on containment and quarantine of infected individuals. Ultimately, the outbreak could be controlled with a protective vaccine to prevent 2019-nCoV infection. While vaccine research should be pursued intensely, there exists today no therapy to treat 2019-nCoV upon infection, despite an urgent need to find options to help these patients and preclude potential death. Herein, I review the potential options to treat 2019-nCoV in patients, with an emphasis on the necessity for speed and timeliness in developing new and effective therapies in this outbreak. I consider the options of drug repurposing, developing neutralizing monoclonal antibody therapy, and an oligonucleotide strategy targeting the viral RNA genome, emphasizing the promise and pitfalls of these approaches. Finally, I advocate for the fastest strategy to develop a treatment now, which could be resistant to any mutations the virus may have in the future. The proposal is a biologic that blocks 2019-nCoV entry using a soluble version of the viral receptor, angiotensin-converting enzyme 2 (ACE2), fused to an immunoglobulin Fc domain (ACE2-Fc), providing a neutralizing antibody with maximal breath to avoid any viral escape, while also helping to recruit the immune system to build lasting immunity. The ACE2-Fc therapy would also supplement decreased ACE2 levels in the lungs during infection, thereby directly treating acute respiratory distress pathophysiology as a third mechanism of action. The sequence of the ACE2-Fc protein is provided to investigators, allowing its possible use in recombinant protein expression systems to start producing drug today to treat patients under compassionate use, while formal clinical trials are later undertaken. Such a treatment could help infected patients before a protective vaccine is developed and widely available in the coming months to year(s).
Joshua M. Pearce
Coronavirus Disease 2019 (COVID-19) threatens to overwhelm our medical infrastructure at the regional level causing spikes in mortality rates because of shortages of critical equipment, like ventilators. Fortunately, with the recent development and widespread deployment of small-scale manufacturing technologies like RepRap-class 3-D printers and open source microcontrollers, mass distributed manufacturing of ventilators has the potential to overcome medical supply shortages. In this study, after providing a background on ventilators, the academic literature is reviewed to find the existing and already openly-published, vetted designs for ventilators systems
Zhian N. Kamvar, Jun Cai, Juliet R.C. Pulliam, Jakob Schumacher, Thibaut Jombart
The epidemiological curve (epicurve) is one of the simplest yet most useful tools used by field epidemiologists, modellers, and decision makers for assessing the dynamics of infectious disease epidemics. Here, we present the free, open-source package incidence for the R programming language, which allows users to easily compute, handle, and visualise epicurves from unaggregated linelist data. This package was built in accordance with the development guidelines of the R Epidemics Consortium (RECON), which aim to ensure robustness and reliability through extensive automated testing, documentation, and good coding practices. As such, it fills an important gap in the toolbox for outbreak analytics using the R software, and provides a solid building block for further developments in infectious disease modelling.
Yu Wai Chen, Chin-Pang Bennu Yiu, Kwok-Yin Wong
We prepared the three-dimensional model of the SARS-CoV-2 (aka 2019-nCoV) 3C-like protease (3CLpro) using the crystal structure of the highly similar (96% identity) ortholog from the SARS-CoV. All residues involved in the catalysis, substrate binding and dimerisation are 100% conserved. Comparison of the polyprotein PP1AB sequences showed 86% identity. The 3C-like cleavage sites on the coronaviral polyproteins are highly conserved. Based on the near-identical substrate specificities and high sequence identities, we are of the opinion that some of the previous progress of specific inhibitors development for the SARS-CoV enzyme can be conferred on its SARS-CoV-2 counterpart. With the 3CLpro molecular model, we performed virtual screening for purchasable drugs and proposed 16 candidates for consideration. Among these, the antivirals ledipasvir or velpatasvir are particularly attractive as therapeutics to combat the new coronavirus with minimal side effects, commonly fatigue and headache. The drugs Epclusa (velpatasvir/sofosbuvir) and Harvoni (ledipasvir/sofosbuvir) could be very effective owing to their dual inhibitory actions on two viral enzymes.
Open data on F1000Research | Policies and principles
F1000Research advocates an Open Data Policy. Others need access to the original, raw data to be able to replicate a given study, reproduce the original findings, or reuse the data to support their own research. Therefore, all articles on the F1000Research platform should include citations to repositories that host the data underlying the results, together with details of the software tools used to process these. Of course, there may be exceptional cases where openly sharing research data is not feasible – for example, due to ethical, data protection, or confidentiality reasons. You can find out more in our Data Guidelines.
Alongside our Open Data policy, F1000Research endorses the FAIR Data Principles which aim to make research data Findable, Accessible, Interoperable, and Reusable; together these provide a framework to promote the broadest possible reuse of scientific data.
Download our FAIR Data Guide
Sensitive Data Guide
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F1000Research is a fully open access publishing platform, offering rapid publication of articles and other research outputs without editorial bias. All articles benefit from transparent post-publication peer review, and editorial guidance on making source data openly available.
F1000Research advocates for transparency and reproducibility in research, and our unique publishing model supports this at every stage.
Articles can be published in as few as 14 days, with post-publication peer review creating an open dialogue between authors and their research community. This generates feedback which can be used to improve the article and develop the author's skills.