Evidence map›Paper›PMID 41799497›Full record

ReviewVeterinary and animal science2026

Biosensors for the detection of arthropod-borne veterinary viruses: A comprehensive review.

Hamid Staji, Seyedeh Fatemeh Angoshtan

Abstract readReview
In one paragraph

Review in Veterinary and animal science, 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

2 authors.

Hamid StajiSemnan University IRAN, ISLAMIC REPUBLIC OF, Semnan City, Iran.
Seyedeh Fatemeh AngoshtanSemnan University IRAN, ISLAMIC REPUBLIC OF, Semnan City, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Veterinary arboviruses pose serious threats to animal health, especially in endemic areas. Traditional methods used for the diagnosis of these viruses are expensive, time-consuming, and require specialized personnel. This review discusses biosensor technologies for veterinary arboviruses, analyzing specific targets, mechanisms, sensitivity, specificity, and utility for use in the field. The remaining gaps were also mentioned. A comprehensive literature search was carried out in the major databases from 2015 to 2025. We used the keywords "Biosensing Technologies"Arboviruses," "Veterinary Medicine," "Viral Diseases," and "Point-of-Care Systems," and names of veterinary arbovirus families and their viruses. The findings reveal that biosensors have shown great sensitivity, specificity, and fast detection for various veterinary arbovirus families in the field. The technologies used were electrochemical, optical, CRISPR-based, and microfluidic formats, which are often improved by the use of nanomaterials and smartphone integration. The platforms directed against Japanese encephalitis, Rift Valley fever, and African swine fever had the lowest detection limits of femtomolar and also had the capability for multiplexing. Nevertheless, the technological development of biosensors remains restricted to a few viruses only, thus revealing substantial veterinary diagnostic gaps. Overall, biosensors have great potential in veterinary virology, with the capability of early diagnosis and, consequently, better outbreak response. Many of these sensors are specific, sensitive, and portable, making them great options for point-of-care applications. Future attention should be given to less-studied viruses, large-scale field validation, and multitarget biosensing platforms to improve veterinary surveillance globally.

Indexed as

ArbovirusesBiosensing techniquesDiagnostic techniques and proceduresVeterinary medicineZoonoses

Identifiers

PMID41799497
PMCPMC12964046

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.