Evidence map›Paper›PMID 38543629›Full record

ReviewMicroorganisms2024

Yeast-Based Screening of Anti-Viral Molecules.

Vartika Srivastava, Ravinder Kumar, Aijaz Ahmad

Abstract readReview
In one paragraph

Review in Microorganisms, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

3 authors.

Vartika SrivastavaDepartment of Clinical Microbiology and Infectious Diseases, Faculty of Health Sciences, School of Pathology, University of Witwatersrand, Johannesburg 2193, South Africa.ORCID 0000-0002-8636-3413
Ravinder KumarDepartment of Pathology, University of Tennessee Health Science Center, Memphis, TN 38163, USA.ORCID 0000-0003-1922-0030
Aijaz AhmadDepartment of Clinical Microbiology and Infectious Diseases, Faculty of Health Sciences, School of Pathology, University of Witwatersrand, Johannesburg 2193, South Africa.ORCID 0000-0003-2845-0727

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Viruses are minuscule infectious agents that reproduce exclusively within the living cells of an organism and are present in almost every ecosystem. Their continuous interaction with humans poses a significant threat to the survival and well-being of everyone. Apart from the common cold or seasonal influenza, viruses are also responsible for several important diseases such as polio, rabies, smallpox, and most recently COVID-19. Besides the loss of life and long-term health-related issues, clinical viral infections have significant economic and social impacts. Viral enzymes, especially proteases which are essential for viral multiplication, represent attractive drug targets. As a result, screening of viral protease inhibitors has gained a lot of interest in the development of anti-viral drugs. Despite the availability of anti-viral therapeutics, there is a clear need to develop novel curative agents that can be used against a given virus or group of related viruses. This review highlights the importance of yeasts as an in vivo model for screening viral enzyme inhibitors. We also discuss the advantages of yeast-based screening platforms over traditional assays. Therefore, in the present article, we discuss why yeast is emerging as a model of choice for in vivo screening of anti-viral molecules and why yeast-based screening will become more relevant in the future for screening anti-viral and other molecules of clinical importance.

Indexed as

anti-viraldrug discoveryproteasescreening assaysvirusyeast

Identifiers

PMID38543629
PMCPMC10974075

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.