Seasonal and Sexual Variation in mRNA Expression of Selected Adipokine Genes Affecting Fat Deposition and Metabolism of the Emu (Dromaius Novaehollandiae)
Bibliographic record
Abstract
Abstract Emus are farmed for fat production. Oil rendered from their back and abdominal fat pads has good anti-oxidant and anti-inflammatory properties and has ingredients that promote cell growth. Our objective is to examine the mRNA expression of 7 emu adipokine genes (eFABP4, eSCD1, eAdipoQ, eAdipoR1, eAdipoR2, Lept and eLepR) to identify gene markers that may help improve emu fat production. Back and abdominal fat tissues from 11 adult emus were biopsied at four time points (April, June, August and November). Total RNA was isolated and cDNA was synthesized. Gene specific primers were designed for partial cloning fragments to amplify the open reading frame of the 7 genes. Lept was not expressed in emu fat tissue. Nucleotides and amino acids sequences of the 6 expressed gene were compared with homologs from other species and phylogenetic relationships established. Seasonal mRNA expression of each gene was assessed by quantitative RT-PCR and differential expression analysed by the 2−ΔΔCT method. The temporal mRNA expression pattern of the genes and the fat gain (kg) between time points association with gene expression level were determined. More whole-genome scanning studies are needed to develop novel molecular markers that can be applied to improve fat production in emus.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".