Interleukin-1ra increases growth performance and body protein accretion and decreases the cytokine response in a model of subclinical disease in growing pigs
Bibliographic record
Abstract
Abstract There is a growing body of evidence to suggest direct involvement of the cytokines interleukin-1 (IL-1), interleukin-6 (IL-6) and tumour necrosis factor-α (TNF-α) in the initiation of skeletal muscle catabolism and depressions in growth performance as a result of diseases, while TNF-α and IL-6 are presumably under the control of IL-1. An experiment was conducted to investigate whether an interleukin-1 receptor antagonist (IL-1ra) could reduce growth depressions in a model of subclinical disease in growing pigs. In this study, 24 crossbred barrows and gilts were randomly assigned to three treatments: (1) healthy control (HC), saline infusion; (2) sick control (SC), infection withMycoplasma hyopneumoniaeand vaccination with porcine reproductive and respiratory syndrome modified live virus (PRRSV) vaccine and saline infusion; (3) SC plus infusion with IL-1ra (SC+IL-1ra). An additional six pigs were killed at the initiation of the trial to determine initial body composition for estimation of carcass nutrient retention rates. During the 28-day experimental period, the SC pigs had growth performance similar to that of the HC pigs and the highest levels of plasma cytokines (P<0·10). The SC+IL-1ra pigs had growth performance similar to the HC pigs, but the SC+Il-1ra pigs tended to have higher growth rates than the SC pigs (P<0·10), the highest carcass protein accretion rate (P<0·10) and similar levels of plasma cytokines compared with the HC pigs. Plasma levels of insulin-like growth factor-1 (IGF-1) on day 22 tended to be higher (P<0·10) in the HC pigs compared with the SC or SC+IL-1ra pigs and treatment×sex interaction was observed on day 29; the IGF-1 levels tended to be higher in SC+IL-1ra gilts and SC barrows (P<0·10). These results indicate that infusion with IL-1ra in a model of subclinical disease helps attenuate the catabolic effects of immune system stimulation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".