Molecular cloning of the two very low-density lipoprotein receptor (VLDLR) subtypes in geese and the effect of overfeeding on their MRNA levels
Bibliographic record
Abstract
The objectives of this study were to verify the existence of two subtypes of the very low-density lipoprotein receptor (VLDLR) gene in geese, to investigate the effect of overfeeding on the plasma concentration of triglycerides (TG) and the very low density lipoprotein (VLDL), the activity of lipoprotein lipase (LPL), and the mRNA level of VLDLR in Sichuan White geese and Landes geese. The results indicate that there are two subtypes of the VLDLR gene in geese, and that they share a high similarity with those of other species. The expression of VLDLR I and VLDLR II was found in both tissues examined. After overfeeding, the expression level of VLDLR I in adipose tissue showed about a onefold increase (P < 0.05) in both breeds. Overfeeding induced a significant decrease of VLDLR I in skeletal muscle of both breeds, and a significant decrease of VLDLR II in Sichuan White geese (P < 0.05), but an obvious increase of VLDLR II in Landes geese (P < 0.05). In addition, overfeeding induced the increase of plasma VLDL, TG concentration and plasma LPL activity. It was concluded that VLDLR may participate in the metabolism of VLDL-TG by regulating the LPL-mediated TG hydrolysis in geese. Key words: Gene expression, geese, molecular cloning, overfeeding, very low-density lipoprotein receptor
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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 | 0.001 |
| 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 | 0.001 |
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".