An epidemiological investigation into the association between biomarkers and growth performance in nursery pigs
Bibliographic record
Abstract
BACKGROUND: Biomarkers are useful tools in research and clinical practice where they are often used to detect and monitor differences in the physiological state of an animal. The proteins IGF-1, IGFBP-3, GHR, CRP, SAA, Hp, IFN-α, IFN-γ, TNF-α, IL-1β, IL-6, IL-10, and IL-18 have been proposed as potential biomarkers for monitoring growth in livestock. The objective of this study was to determine whether hepatic gene expression of these proposed biomarkers is associated with growth performance in nursery pigs. Herd information and growth parameters were collected for 168 piglets from 8 commercial farms in southern Ontario. From these pigs, a subset of liver tissue samples (n = 74) was used for gene expression analysis of the proposed biomarkers. Multivariable linear regression methods were used to determine whether genetic expression of the proposed biomarkers was associated with growth performance in the nursery. RESULTS: Modelling the herd information and individual piglet traits in relation to growth performance revealed that the weight at weaning and the age at weaning are significantly associated with nursery performance. Average daily gain (ADG) was significantly associated with hepatic IGFBP-3 and GHR expression in the liver (P < 0.05), and tended to be associated with hepatic IGF-1 expression (P = 0.071). Similarly, 9-week body weight was significantly associated with hepatic expression of IGFBP-3 and GHR expression (P < 0.05), and tended to be associated with hepatic expression of IGF-1 (P = 0.055). CONCLUSION: The age and weight at which pigs are weaned is an important determinant for nursery performance. Hepatic gene expression of IGF-1, IGFBP-3, and GHR can be useful biomarkers for monitoring growth performance in nursery pigs.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".