Evidence map›Paper›PMID 40184169›Full record

ArticleAnimal biotechnology2025

Identification of key factors causing ketosis in dairy cows with low feed intake.

Xue Feng, Qi Feng, Sayed Haidar Abbas Raza, Fen Li, Yun Ma

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Animal biotechnology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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.

Xue FengKey Laboratory of Ruminant Molecular and Cellular Breeding of Ningxia Hui Autonomous Region, College of Animal Science and Technology, Ningxia University, Yinchuan, China.ORCID 0000-0001-8341-5217
Qi FengSchool of Computer and Information Engineering, Henan University, Kaifeng, China.
Sayed Haidar Abbas RazaResearch Center for Machining and Safety of Livestock and Poultry Products, South China Agricultural University, Guangzhou, China.
Fen LiKey Laboratory of Ruminant Molecular and Cellular Breeding of Ningxia Hui Autonomous Region, College of Animal Science and Technology, Ningxia University, Yinchuan, China.
Yun MaKey Laboratory of Ruminant Molecular and Cellular Breeding of Ningxia Hui Autonomous Region, College of Animal Science and Technology, Ningxia University, Yinchuan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Ketosis is a common metabolic disease in high-yield dairy cows. Key genes affecting ketosis need to be further explored by new methods. The gene expression profiling and clinical data of GSE92398, GSE104079, and GSE4304 were obtained from the gene expression omnibus (GEO) database. Core modules and genes associated with RFI (residual feed intake) and ADF (alternate day fasting) were identified by weighted gene co-expression network analysis (WGCNA). Subsequently, the key genes related to ketosis and RFI were determined by protein-protein interaction (PPI) networks, ROC curves, functional enrichment, and differential expression analysis, respectively. The results showed that the genes of ACACA, ELOVL6 and XPO7 could be used as regulators of ketosis induced by low feed intake in dairy cows. At the same time, three genes (HRFI, STAT3 and IFNAR1) were retained as additional RFI biomarkers that could be considered. We identified three key factors as candidate genes and biomarkers of ketosis and RFI, respectively. These factors may provide a theoretical basis for targeted therapy of ketosis in dairy cows.

Indexed as

Cattle DiseasesEatingKetosisAnimalsBiomarkersCattleFemaleGene Expression ProfilingProtein Interaction MapsBiomarkersdairy cowsfeed intakeKetosisRFIWGCNA

Identifiers

PMID40184169
PMCPMC12674265

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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.