The differentiation of preadipocytes and gene expression related to adipogenesis in ducks (Anas platyrhynchos)
Bibliographic record
Abstract
Meat quality is closely related to adipose tissues in ducks, and adipogenesis is controlled by a complex network of transcription factors tightly acting at different stages of differentiation especially in ducks. The aim of this study was to establish the preadipocyte in vitro culture system and understand the biological characteristics of expansion of duck adipocyte tissue at the cellular and molecular level. We isolated pre-adipocytes from the subcutaneous fat of three breeds of duck and differentiated them into mature adipocytes using a mixture of insulin, rosiglitazone, dexamethasone, 3-isobutyl-1-methylxanthine, and oleic acid over 0,2, 4, 6, and 8 days. Successful differentiation was confirmed from the development of lipid droplets and their response to Oil Red O, and increasing numbers of lipid droplets were stained red over time. The expression of key marker genes, including peroxisome proliferator activated receptor γ (PPARγ), CCAAT/enhancer binding protein-α (C/EBPα), adipocyte fatty acid binding protein 4 (FABP4), and fatty acid synthetase (FAS), gradually increased during pre-adipocyte differentiation. Furthermore, it was verified by interference experiments that the knockdown of PPARγ directly reduced lipid production. Meanwhile we analyzed the role of unsaturated fatty acids in the production of poultry fat using different concentrations of oleic acid and found that lipid droplet deposition was highest when the concentration of oleic acid was 300 μM. We also compared the level of differentiated pre-adipocytes that were isolated from Jianchang ducks (fatty-meat duck), Cherry Valley ducks (lean-meat duck) and White-crested ducks (egg-producing duck). The proliferation and differentiation rate of pre-adipocytes derived from Jianchang ducks was higher than that of White-crested ducks. These results provide the foundation for further research into waterfowl adipogenesis.
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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".