Messenger RNA levels of growth factors, ligands, receptors, and proteins affecting lipid metabolism in pigs
Bibliographic record
Abstract
The Northern blot technique was used for mRNA phenotyping of 19 growth factors, ligands, receptors, and proteins involved in lipid metabolism in two populations of pigs with different fat deposition capabilities. The mRNA levels were measured in backfat, liver, and muscle tissue at different slaughter weights, taking backfat thickness, gender and breed of the animals into consideration. Of all the RNA patterns measured in the Landrace population, only the mRNA transcript level of low density lipoprotein receptor-related protein (also called alpha 2-macroglobulin receptor) was associated with the pig's backfat thickness phenotype in muscle and backfat tissues. In the population composed of purebred Yorkshire and Hampshire, epidermal growth factor receptor, malic enzyme, platelet derived growth factor β and insulin-like growth factor binding protein 3 show different mRNA patterns associated with backfat thickness phenotypes. When analyzing the data using the gender or the breed as the main effect, the insulin receptor and insulin-like growth factor binding protein 1 were different between genders whereas insulin-like growth factor binding protein 3, malic enzyme, epidermal growth factor receptor and low density lipoprotein receptor-related protein were different between breeds. Analysis of this type should be helpful in understanding the regulation of fat deposition. Key words: mRNA levels, marker genes, backfat, pig
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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 | 0.001 | 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".