Evidence map›Paper›PMID 42637785›Full record

ArticleScientific reports2026

High-throughput in silico multi-epitope reverse vaccine design against hydatidosis.

Dania Skhal, Samar Al Nahhas, Osama Skhal, Abdul Qader Abbady

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Dania SkhalDepartment of Animal Biology, Faculty of Science, Damascus University, Damascus, Syria.
Samar Al NahhasDepartment of Animal Biology, Faculty of Science, Damascus University, Damascus, Syria. samaralnahhas6@gmail.com.ORCID 0000-0002-4306-259X
Osama SkhalMinistry of Interior, Damascus, Syria.
Abdul Qader AbbadyDivision of Molecular Biomedicine, Department of Molecular Biology and Biotechnology, Atomic Energy Commission of Syria (AECS), Damascus, Syria. aabady@aec.org.sy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The Middle East is a significant hotspot for human hydatidosis caused by Echinococcus granulosus, a complex multi-stage pathogen exhibiting antigenic variation with diverse epitopes. Multi-epitope vaccines, designed using immunoinformatics, represent a promising approach to effectively control this challenging parasite. A total of 1150 secreted, non-toxic, antigenic proteins were obtained from database and analyzed for their immunogenic potential, leading to the identification of 1024 CTL, 80 HTL, and 172 LBL prioritized epitopes, which were then ranked and clustered to prefer the most immunogenicity epitopes to include in novel vaccine construct. The chosen Construct (Con2) was modified for dual-plasmid insertion, facilitating the expression of three different vaccine constructs which were predicted by C-ImmSim to stimulate robust IFN-γ/IL-2 production and enhance IgM/IgG antibody secretion. Utilizing in silico, next-generation vaccine design, we propose several vaccine candidates that warrant further validation through in vitro laboratory testing and in vivo studies to assess their immunological efficacy.

Indexed as

EchinococcosisEchinococcus granulosusEpitopesVaccinesAnimalsAntibodies, HelminthAntigens, HelminthComputer SimulationEpitopes, T-LymphocyteHumansImmunoinformaticsProtein Subunit VaccinesAntibodies, HelminthAntigens, HelminthEpitopesEpitopes, T-LymphocyteProtein Subunit VaccinesVaccinesEchinococcus granulosusEpitopes predictionImmunoinformaticIn silico cloningMulti-epitope vaccines

Identifiers

PMID42637785
PMCPMC13503669

What OpenQuestion holds

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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.