Evidence map›Paper›PMID 30729137›Full record

ArticleJournal of immunology research2019

Functionalisation of Virus-Like Particles Enhances Antitumour Immune Responses.

Katrin Kramer, Farah Al-Barwani, Margaret A Baird, Vivienne L Young, David S Larsen, Vernon K Ward, Sarah L Young

Abstract read
In one paragraph

Article in Journal of immunology research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Virus-like Particles: Fundamentals and Biomedical Applications.International journal of molecular sciences · 2022
    Review
  5. Review
  6. Article
  7. Article
  8. Article
  9. Review
  10. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Katrin KramerDepartment of Pathology, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand.ORCID 0000-0001-9491-3625
Farah Al-BarwaniBiology Department, Sultan Qaboos University, Muscat, Oman.
Margaret A BairdDepartment of Pathology, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand.
Vivienne L YoungDepartment of Microbiology and Immunology, School of Biomedical Sciences, University of Otago, Dunedin, New Zealand.
David S LarsenDepartment of Chemistry, Division of Sciences, University of Otago, Dunedin, New Zealand.ORCID 0000-0002-3563-1086
Vernon K WardDepartment of Microbiology and Immunology, School of Biomedical Sciences, University of Otago, Dunedin, New Zealand.
Sarah L YoungDepartment of Pathology, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand.ORCID 0000-0003-0374-2221

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Virus-like particles (VLP) from the rabbit haemorrhagic disease virus (RHDV) can deliver tumour antigens to induce anticancer immune responses. In this study, we explored how RHDV VLP can be functionalised to enhance the immune response by increasing antigen loading, incorporating linkers to enhance epitope processing, and targeting receptor-mediated internalisation of VLP. RHDV VLP were developed to deliver up to three copies of gp100

Indexed as

AnimalsAntigens, NeoplasmCancer VaccinesCD8-Positive T-LymphocytesCell Culture TechniquesEpitopes, T-LymphocyteHemorrhagic Disease Virus, RabbitImmunotherapyLectins, C-TypeLymphocyte ActivationMannose-Binding LectinsMannose ReceptorMelanomaMiceMice, Inbred C57BLReceptors, Cell SurfaceAntigens, NeoplasmCancer VaccinesEpitopes, T-LymphocyteLectins, C-TypeMannose-Binding LectinsMannose ReceptorReceptors, Cell SurfaceVaccines, Virus-Like ParticleViral Proteins

Identifiers

PMID30729137
PMCPMC6341245

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.