Fatty Acids Have Different Adipogenic Differentiation Potentials in Stromal Vascular Cells Isolated from Abdominal Fat in Laying Hens
Bibliographic record
Abstract
This study was conducted to examine the effects of fatty acids (FA) with/without chicken serum (CS) on the expression of adipogenic transcripts and adipogenesis in chicken stromal vascular cells (SVC). In experiment 1, SVC were grown in DMEM containing 10% FBS (Control) and treated with 300 µM oleic acid (OLA) + FBS, linoleic acid (LNA) + FBS, palmitic acid (PAM) + FBS, or stearic acid (STA) + FBS for 48 h. In experiment 2, cells were grown in DMEM containing 5% CS and treated with 300 µM OLA (CS + OLA), PAM (CS + PAM), STA (CS + STA) or 200 µM LNA (CS + LNA) for 48 h. Adipogenesis was determined using Oil Red O staining and glycerol-3-phosphate dehydrogenase (GPDH) activity. The proportion of OLA, PAM, or STA was increased (P < 0.05) in SVC grown in either FBS or CS with OLA, PAM or STA. Adipogenesis was induced in FBS + OLA, FBS + LNA, FBS + PAM, FBS + STA, CS + OLA, CS + LNA, CS + PAM, or CS + SAT compared to FBS. GPDH activity was significantly higher in FBS + OLA and FBS + LNA than one in FBS. Compared to FBS, the expression of FABP4 mRNA increased (P < 0.05) in FBS + OLA, FBS + LNA, or FBS + PAM, whereas that of C/EBPα, C/EBPβ, and ATGL increased (P < 0.05) in FBS + OLA or FBS + LNA cells. Expression of FABP4 and C/EBPβ mRNA was higher in CS, CS + OLA, CS + LNA, CS + PAM, or CS + SAT compared with (FBS, whereas the expression of ATGL and C/EBPα was higher in CS, CS + OLA, or CS + LNA than FBS cells. In conclusion, these results showed that FA have different potentials to induce adipogenesis, LNA is the most potent among the tested FA, and these potentials can be improved in the presence of CS.
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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".