Evidence map›Paper›PMID 41227463›Full record

ReviewAnimals : an open access journal from MDPI2025

Technologies in Biomarker Discovery for Animal Diseases: Mechanisms, Classification, and Diagnostic Applications.

Salwa Eman, Raza Mohai Ud Din, Muhammad Hammad Zafar, Mengke Zhang, Xin Wen, Jiayu Ma, Ahmed A Saleh, Hosameldeen Mohamed Husien, Mengzhi Wang, Xiaodong Guo

Abstract readReview
In one paragraph

Review in Animals : an open access journal from MDPI, 2025. 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. Review
  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

10 authors.

Salwa EmanCollage of Animal Science and Technology, Yangzhou University, Yangzhou 225009, China.ORCID 0009-0009-2728-9805
Raza Mohai Ud DinCollage of Animal Science and Technology, Yangzhou University, Yangzhou 225009, China.ORCID 0009-0001-0459-7946
Muhammad Hammad ZafarCollage of Animal Science and Technology, Yangzhou University, Yangzhou 225009, China.ORCID 0000-0002-6135-9987
Mengke ZhangCollege of Food Science and Light Industry, Nanjing Tech University, Nanjing 211816, China.ORCID 0000-0002-9585-8477
Xin WenCollage of Animal Science and Technology, Yangzhou University, Yangzhou 225009, China.
Jiayu MaCollage of Animal Science and Technology, Yangzhou University, Yangzhou 225009, China.
Ahmed A SalehCollage of Animal Science and Technology, Yangzhou University, Yangzhou 225009, China.ORCID 0000-0003-1314-8066
Hosameldeen Mohamed HusienCollage of Animal Science and Technology, Yangzhou University, Yangzhou 225009, China.ORCID 0009-0008-8993-3507
Mengzhi WangCollage of Animal Science and Technology, Yangzhou University, Yangzhou 225009, China.ORCID 0000-0002-6256-8166
Xiaodong GuoCollage of Animal Science and Technology, Yangzhou University, Yangzhou 225009, China.

Funding

This work was supported by the Jiangsu Natural Science Foundation (Grant number BK20240916), the Project of Yangzhou Lvyang Golden Phoenix Talent Plan 1086 (137013446), the Top Talents Award Plan of Yangzhou University (2023), and the National Key Researc BK20240916, 137013446, 2024YFD1300204
6 · The paper itself

Abstract

Animal diseases remain a major constraint to livestock productivity and public health, necessitating accurate, early diagnostic methods. This review examines the classification and mechanisms of diagnostic, prognostic, and predictive biomarkers in veterinary medicine and evaluates how advanced technologies enable their discovery. Mechanistically, biomarkers function as molecular indicators of disease presence, progression, or therapeutic response, and are essential in species where clinical signs often appear late or are non-specific. We detail the contribution of high-throughput omics platforms, genomics (NGS, RNA-Seq), proteomics (LC-MS/MS, DIGE), and metabolomics (NMR, LC-MS/MS) in identifying disease-specific molecular signatures. Emerging technologies, including CRISPR/Cas9, AI-enhanced imaging, aptamer-based biosensors, and microfluidic devices, show significant diagnostic potential. Case studies, including canine melanoma, bovine respiratory disease complex (BRDC), and congenital portosystemic shunts in dogs, illustrate the real-world applicability of biomarkers. Challenges such as a lack of standardization, species variability, and poor clinical translation are acknowledged. The review concludes that integrating biomarker mechanisms with advanced analytical technologies is key to advancing veterinary diagnostics and disease control.

Indexed as

animal diseasebiomarkersearly diagnosistechnologies

Identifiers

PMID41227463
PMCPMC12609278

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.