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Record W4225341297 · doi:10.1080/19768354.2022.2059566

Characterization of differential gene expression of broiler chicken to thermal stress in discrete developmental stages

2022· article· en· W4225341297 on OpenAlexfundno aff
Hyun Seung Kim, Ji-Min Kim, Jaemin Kim, Yang Ho Choi

Bibliographic record

VenueAnimal Cells and Systems · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Physiology
Canadian institutionsnot available
FundersClean Air Regulatory AgendaRural Development Administration
KeywordsTranscriptomeBiologyBroilerGene expressionMicroarrayGeneMicroarray analysis techniquesRNA-SeqGene expression profilingAndrologyAnimal scienceGeneticsCell biologyMedicine

Abstract

fetched live from OpenAlex

Prolonged exposure to high temperatures is linked to a range of physiological responses in broiler chickens including reduced disease resistance, low growth rate, and high mortality rate. In this study, we investigated the effect of heat stress on gene expression levels in 4-week-old and 6-week-old chickens each exposed to environments conditioned at thermoneutral (21 °C) and high (32 °C) temperatures. The analysis of differentially expressed genes (DEGs) using microarray revealed that genes underlying reactive oxygen species (ROS) production, cell nutrient intake, glucose metabolism, and circadian rhythm were differentially regulated in association with heat stress. We also found that the deviation in expression levels across the transcriptome in response to heat stress was significantly stronger (P< 2.2×10−16) in 6-week-olds compared to younger chickens. We finally observed a significant trend (r = 0.78, P< 2.2×10−16) that genes with a higher estimate of expression in the microarray were more likely to have a higher expression level in RNA-sequencing. Together, our findings provide comprehensive insights into the physiology involved in stress responses at varying developmental stages, which may facilitate chicken breeding to maximize their productivity under adverse conditions.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.514
Threshold uncertainty score0.163

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.016
GPT teacher head0.204
Teacher spread0.188 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2022
Admission routes1
Has abstractyes

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