Insights into protein synthesis dynamics of gilts from the same genetic background and age differing in protein deposition
Bibliographic record
Abstract
Abstract Protein synthesis in Low and High protein deposition (PD) gilts, exploring regulatory pathways within the same genetic background and age were studied. Gilts in Low (157 g/d) and High (219 g/d) PD groups underwent jugular vein cannulation to assess insulin, IGF-I and glucose postprandial responses to the same nutrient intake. 13 C-valine administration enabled measuring protein synthesis rate and efficiency. Results showed 94% greater ( P < 0.05) fractional synthesis rates in the longissimus dorsi and tended ( P = 0.10) to a greater (11%) absolute synthesis rate in the liver of High PD gilts. High PD gilts tended ( P = 0.10) to be more sensitive to insulin. Transcriptomics analyses in muscle identified 67 up-regulated and 102 down-regulated unique genes. Among the up-regulated genes, four olfactory receptors (OR4L1, OR5D13, OR6B2, OR10R2) and one ribosomal protein (RPS15A) present the highest fold-changes in High vs Low PD gilts. Functional analyses identified six enriched gene ontology terms relate to muscle development, three to protein metabolism and four to signaling pathways. Rap1 signaling and regulation of actin cytoskeleton were over-represented KEGG pathways. High PD gilts exhibit greater protein synthesis and efficiency, with transcriptomic evidence suggesting improved insulin sensitivity and reduced muscle protein degradation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".