Evidence map›Paper›PMID 32557648›Full record

ReviewJournal of cellular physiology2020

Vaccine development and therapeutic design for 2019-nCoV/SARS-CoV-2: Challenges and chances.

Mahnaz Ghaebi, Abdolreza Osali, Hamed Valizadeh, Leila Roshangar, Majid Ahmadi

Abstract readReview
In one paragraph

Review in Journal of cellular physiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.

0numbers the graph read from it
0cells of the map it votes in
41citing 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

41 citing papers in PubMed.

  1. Trial
  2. Trial
  3. Review
  4. Review
  5. Review
  6. Article
  7. Review
  8. Article
  9. Article
  10. Article
  11. Article
  12. Review
  13. Review
  14. Article
  15. Review
  16. Review
  17. Review
  18. Review
  19. Article
  20. Article
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

5 authors.

Mahnaz GhaebiDepartment of Immunology, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran.
Abdolreza OsaliDepartment of Immunology, School of Medicine, Zanjan University of Medical Sciences, Zanjan, Iran.
Hamed ValizadehTuberculosis and Lung Disease Research Center of Tabriz University of Medical Sciences, Tabriz, Iran.
Leila RoshangarStem Cell Research Center, Tabriz University of Medical Sciences, Tabriz, Iran.
Majid AhmadiStem Cell Research Center, Tabriz University of Medical Sciences, Tabriz, Iran.ORCID 0000-0003-4787-5261

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The ongoing outbreak of the recently emerged 2019 novel coronavirus (nCoV), which has seriously threatened global health security, is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with high morbidity and mortality. Despite the burden of the disease worldwide, still, no licensed vaccine or any specific drug against 2019-nCoV is available. Data from several countries show that few repurposed drugs using existing antiviral drugs have not (so far) been satisfactory and more recently were proven to be even highly toxic. These findings underline an urgent need for preventative and therapeutic interventions designed to target specific aspects of 2019-nCoV. Again the major factor in this urgency is that the process of data acquisition by physical experiment is time-consuming and expensive to obtain. Scientific simulations and more in-depth data analysis permit to validate or refute drug repurposing opportunities predicted via target similarity profiling to speed up the development of a new more effective anti-2019-nCoV therapy especially where in vitro and/or in vivo data are not yet available. In addition, several research programs are being developed, aiming at the exploration of vaccines to prevent and treat the 2019-nCoV. Computational-based technology has given us the tools to explore and identify potentially effective drug and/or vaccine candidates which can effectively shorten the time and reduce the operating cost. The aim of the present review is to address the available information on molecular determinants in disease pathobiology modules and define the computational approaches employed in systematic drug repositioning and vaccine development settings for SARS-CoV-2.

Indexed as

Antiviral AgentsBetacoronavirusBiomedical ResearchCoronavirus InfectionsCOVID-19Drug RepositioningHumansPandemicsPneumonia, ViralSARS-CoV-2Viral VaccinesAntiviral AgentsViral VaccinesbioinformaticsCOVID-19drug targetSARS-CoV-2treatmentvaccine

Identifiers

PMID32557648
PMCPMC7323389

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.