Relationship of physiological resilience to weaning stress and NR3C1 expression to carcass merit and pork quality
Bibliographic record
Abstract
Pre-slaughter stress due to transportation, fighting between animals, or lairage time can have a negative impact on meat quality. However, it is unknown if post-natal early-life stress influences carcass merit or pork quality. Given that weaning is a major stressor, this study aimed to examine if gilt piglets exhibiting divergent stress response patterns (resilient or vulnerable) at weaning had differences in carcass or meat quality traits as well as underlying molecular differences in cortisol receptors (NR3C1, NR3C2). Stress resilient gilts exhibited significantly lower live weights at slaughter ( P = 0.045) and smaller loin muscle area ( P = 0.008) than stress vulnerable gilts. Additionally, several associations were observed between tissue-specific transcript abundance and meat quality traits. Lower NR3C1 transcript abundance in subcutaneous adipose was significantly associated with higher marbling score ( P = 0.028), higher 45 min pH ( P = 0.012), greater pH decline ( P = 0.003), higher subjective color score ( P = 0.027), and higher Minolta L* values ( P = 0.049). In conclusion, this study indicates weaning stress is not associated with substantive differences in carcass merit or meat quality, and revealed insights into potential molecular mechanisms by which stress affects meat quality through adipose-specific expression of the glucocorticoid and mineralocorticoid receptors.
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 | 0.000 | 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".