Evidence map›Paper›PMID 40725468›Full record

ArticleGenes2025

Intelligent Multi-Modeling Reveals Biological Mechanisms and Adaptive Phenotypes in Hair Sheep Lambs from a Semi-Arid Region.

Robson Mateus Freitas Silveira, Fábio Augusto Ribeiro, João Pedro Dos Santos, Luiz Paulo Fávero, Luis Orlindo Tedeschi, Anderson Antonio Carvalho Alves, Danilo Augusto Sarti, Anaclaudia Alves Primo, Hélio Henrique Araújo Costa, Neila Lidiany Ribeiro and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Genes, 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. Article
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.

Robson Mateus Freitas SilveiraDepartment of Animal Science, Luiz de Queiroz College of Agriculture (ESALQ), University of São Paulo (USP), Piracicaba 13418-900, SP, Brazil.ORCID 0000-0003-2285-9695
Fábio Augusto RibeiroMBA in Data Science and Analytics, Luiz de Queiroz College of Agriculture (ESALQ), University of São Paulo (USP), 11 Pádua Dias Ave, Piracicaba 13418-900, SP, Brazil.
João Pedro Dos SantosMBA in Data Science and Analytics, Luiz de Queiroz College of Agriculture (ESALQ), University of São Paulo (USP), 11 Pádua Dias Ave, Piracicaba 13418-900, SP, Brazil.
Luiz Paulo FáveroMBA in Data Science and Analytics, Luiz de Queiroz College of Agriculture (ESALQ), University of São Paulo (USP), 11 Pádua Dias Ave, Piracicaba 13418-900, SP, Brazil.ORCID 0000-0001-8516-6701
Luis Orlindo TedeschiDepartment of Animal Science, Texas A&M University, College Station, TX 77843, USA.
Anderson Antonio Carvalho AlvesDepartment of Animal and Dairy Science, University of Georgia, Athens, GA 30602, USA.ORCID 0000-0001-8306-0487
Danilo Augusto SartiHamilton Institute, Department of Mathematics and Statistics, Maynooth University, County Kildare W23 F2H6, Ireland.ORCID 0000-0001-8148-2803
Anaclaudia Alves PrimoDepartment of Animal Science, Vale do Acaraú State University (UVA), Sobral 62040-370, CE, Brazil.
Hélio Henrique Araújo CostaDepartment of Animal Science, Vale do Acaraú State University (UVA), Sobral 62040-370, CE, Brazil.ORCID 0000-0002-8875-8515
Neila Lidiany RibeiroDepartment of Animal Science, Federal University of Paraíba, Campus II, PB-079 Highway (km 12), Areia 58397-000, PB, Brazil.ORCID 0000-0002-6410-244X
Amanda Felipe ReitenbachInstitute of Chemistry, University of Brasília, Brasília 70904-970, DF, Brazil.
Fabianno Cavalcante de CarvalhoDepartment of Animal Science, Vale do Acaraú State University (UVA), Sobral 62040-370, CE, Brazil.
Aline Vieira LandimDepartment of Animal Science, Vale do Acaraú State University (UVA), Sobral 62040-370, CE, Brazil.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHeat stress challenges small ruminants in semi-arid regions, requiring integrative multi-modeling approaches to identify adaptive thermotolerance traits. This study aimed to identify phenotypic biomarkers and explore the relationships between thermoregulatory responses and hematological, behavioral, morphometric, carcass, and meat traits in lambs.

methodsTwenty 4-month-old non-castrated male lambs, with an average body weight of 19.0 ± 5.11 kg, were evaluated under natural heat stress.

resultsThermoregulatory variables were significantly associated with non-carcass components (

conclusionsThese findings highlight the complex biological relationships underlying heat adaptation and emphasize the potential of combining phenomic data with computational tools to support genomic selection for climate-resilient and welfare-oriented breeding programs.

Indexed as

Adaptation, PhysiologicalThermotoleranceAnimalsBody Temperature RegulationHeat-Shock ResponseMalePhenotypeSheepadaptationadvanced analyticsmeat traitsphenotypic biomarkerthermoregulatory responses

Identifiers

PMID40725468
PMCPMC12294602

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.