Evidence map›Paper›PMID 38318007›Full record

ArticleHeliyon2024

Discovering conserved epitopes of Monkeypox: Novel immunoinformatic and machine learning approaches.

Mohammad Izadi, Fatemeh Mirzaei, Mohammad Aref Bagherzadeh, Shamim Ghiabi, Alireza Khalifeh

Abstract read
In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

5 authors.

Mohammad IzadiStudent Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran.
Fatemeh MirzaeiStudent Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran.
Mohammad Aref BagherzadehStudent Research Committee, Jahrom University of Medical Sciences, Jahrom, Iran.
Shamim GhiabiDepartment of Medical Chemistry, Faculty of Pharmacy, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran.
Alireza KhalifehDepartment of Pathology, Faculty of Dentistry, Shiraz Branch, Islamic Azad of University, Shiraz, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The Monkeypox virus, an Orthopoxvirus with zoonotic origins, has been responsible for a growing number of human infections reminiscent of smallpox since May 2022, as reported by the World Health Organization. As of now, there are no established medical treatments for managing Monkeypox infections. In this study, we used machine learning to select conserved epitopes. Proteins were determined using Reverse Vaccinology and Gene Ontology subcellular localization, and their epitopes were predicted. NextClade was used to calculate the number of mutations in each amino acid position using 2433 Monkeypox sequences. The Unsupervised Nearest Neighbor machine learning algorithm and ideal matrix [0 0] were used to calculate the conservancy score of epitopes. Six proteins were determined for epitope prediction. Finally, 47 MHC-I epitopes, 5 MHC-II epitopes, and 10 Linear B cell epitopes were discovered. Our method can select epitopes for vaccine design to prevent viruses with accelerated evolution and high mutation rate.

Indexed as

Conserved epitopesImmunoinformaticMachine learningMonkeypox

Identifiers

PMID38318007
PMCPMC10839993

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.