Evidence map›Paper›PMID 42883035›Full record

ReviewAnimals : an open access journal from MDPI2026

Emerging and Novel Ovine Viruses: Molecular Diagnostics, Genomic and Metagenomic Surveillance, and One Health Perspectives.

Gul Muhammad Shahbaz, Zeeshan Ashraf, Shuxin Chen, Chaofan Wang, Chenglong He, Huiping Sun, Lexiao Zhu, Lei Liu, Mingcheng Wang, Linglong Wu and 3 more

Abstract readReview
In one paragraph

Review in Animals : an open access journal from MDPI, 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

13 authors.

Gul Muhammad ShahbazKey Laboratory of Tarim Animal Husbandry Science and Technology, Xinjiang Production and Construction Group, School of Animal Science and Technology, Tarim University, Alaer 843300, China.ORCID 0009-0008-0886-8863
Zeeshan AshrafKey Laboratory of Livestock and Forage Resources Utilization around Tarim, Ministry of Agriculture and Rural Affairs, School of Animal Science and Technology, Tarim University, Alar 843300, China.ORCID 0009-0000-6424-036X
Shuxin ChenKey Laboratory of Tarim Animal Husbandry Science and Technology, Xinjiang Production and Construction Group, School of Animal Science and Technology, Tarim University, Alaer 843300, China.
Chaofan WangKey Laboratory of Tarim Animal Husbandry Science and Technology, Xinjiang Production and Construction Group, School of Animal Science and Technology, Tarim University, Alaer 843300, China.
Chenglong HeKey Laboratory of Tarim Animal Husbandry Science and Technology, Xinjiang Production and Construction Group, School of Animal Science and Technology, Tarim University, Alaer 843300, China.
Huiping SunKey Laboratory of Tarim Animal Husbandry Science and Technology, Xinjiang Production and Construction Group, School of Animal Science and Technology, Tarim University, Alaer 843300, China.ORCID 0009-0004-5545-6344
Lexiao ZhuKey Laboratory of Tarim Animal Husbandry Science and Technology, Xinjiang Production and Construction Group, School of Animal Science and Technology, Tarim University, Alaer 843300, China.ORCID 0009-0009-6400-7483
Lei LiuKey Laboratory of Tarim Animal Husbandry Science and Technology, Xinjiang Production and Construction Group, School of Animal Science and Technology, Tarim University, Alaer 843300, China.
Mingcheng WangKey Laboratory of Tarim Animal Husbandry Science and Technology, Xinjiang Production and Construction Group, School of Animal Science and Technology, Tarim University, Alaer 843300, China.
Linglong WuKey Laboratory of Tarim Animal Husbandry Science and Technology, Xinjiang Production and Construction Group, School of Animal Science and Technology, Tarim University, Alaer 843300, China.
Ruohuai GuKey Laboratory of Tarim Animal Husbandry Science and Technology, Xinjiang Production and Construction Group, School of Animal Science and Technology, Tarim University, Alaer 843300, China.ORCID 0009-0006-4447-3178
Wei LiKey Laboratory of Tarim Animal Husbandry Science and Technology, Xinjiang Production and Construction Group, School of Animal Science and Technology, Tarim University, Alaer 843300, China.
Feng XingKey Laboratory of Tarim Animal Husbandry Science and Technology, Xinjiang Production and Construction Group, School of Animal Science and Technology, Tarim University, Alaer 843300, China.

Funding

Joint Funds of the National Natural Science Foundation of China No. U25A20699President's Fund Project of Tarim University No. TDZKCX202401the Major Science and Technology Special Project of Xinjiang Uygur Autonomous Region No. 2024A02004-3the Postgraduate Practical Innovation Project of Xinjiang Production and Construction Corps No. BTYJXM-2024-S23
6 · The paper itself

Abstract

Emerging and novel ovine viruses pose increasing threats to animal health, livestock productivity, trade, food security, and public health preparedness. Their emergence is driven by complex interactions among animal movement, mixed-species production systems, wildlife-livestock interfaces, arthropod vectors, environmental changes, and viral evolution. This review critically compares current strategies for identifying major, emerging, re-emerging, zoonotic, and newly recognized ovine viruses, with emphasis on their analytical sensitivity, turnaround time, throughput, operational cost, accessibility, field applicability, validation status, and capacity for novel-virus detection. Conventional diagnostic approaches, including virus isolation, serology, antigen detection, histopathology, and immunohistochemistry, remain essential for confirmation and flock-level surveillance but may be limited by slow turnaround, dependence on specialized facilities, reduced sensitivity at low viral loads, and an inability to identify highly divergent or unknown viruses. Targeted molecular assays, including PCR, RT-PCR, qPCR, multiplex assays, digital PCR, isothermal amplification, and CRISPR-based diagnostics, have improved detection speed and sensitivity but generally require prior knowledge of viral genomic targets. Genomic and metagenomic approaches, including whole-genome sequencing, next-generation sequencing, nanopore sequencing, viral metagenomics, bioinformatics, and phylogenetic analysis, provide broader detection capabilities by enabling characterization of viral diversity, outbreak tracing, co-infection identification, and discovery of previously unrecognized viruses. However, their interpretation remains challenging due to low viral abundance, poor sample quality, host nucleic acid background, contamination, incomplete reference databases, limited computational capacity, and the inability of sequence detection alone to confirm disease causality. Therefore, future ovine virus surveillance requires integration of molecular diagnostics with active, passive, outbreak-based, risk-based, vector, wildlife, and animal-movement surveillance within a One Health framework. Linking genomic information with ecological, epidemiological, and environmental data will be essential for transforming ovine virus surveillance from reactive diagnosis toward proactive preparedness. Advances in standardized sampling, validated field diagnostics, affordable sequencing, curated databases, bioinformatics capacity, and cross-sector data sharing will strengthen early recognition, risk assessment, and preparedness against emerging viral threats in sheep.

Indexed as

emerging viral diseasesgenomic surveillancemolecular diagnosticsnanopore sequencingOne HealthOvine virusesveterinary epidemiologyviral metagenomics

Identifiers

PMID42883035
PMCPMC13663559

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.