Combination of glycyrrhizic acid and compound probiotics alleviates deoxynivalenol-induced damage to weaned piglets
Bibliographic record
Abstract
Deoxynivalenol (DON) can affect health and growth performance of pigs, resulting in significant economic losses in swine production. The aim of this study was to investigate the effect of glycyrrhizic acid combined with compound probiotics, i.e. Enterococcus faecalis plus Saccharomyces cerevisiae (GAP) on improving growth performance, intestinal health and its fecal microbiota composition change of piglets challenged with DON. A total of 160 42-day-old weaned piglets (Landrace × Large White) were used and the experimental period was 28 d. The results showed that supplementing GAP in the diet significantly improved the growth performance of piglets challenged with DON and alleviate DON-induced intestinal damage by reducing ALT, AST and LDH concentrations in serum, increasing the morphological parameters of jejunum, and decreasing DON residues in serum, liver and feces. Moreover, GAP could significantly decrease the expressions of inflammation and apoptosis genes and proteins (IL-8, IL-10, TNF-α, COX-2, Bax, Bcl-2 and Caspase 3), and increase the expressions of tight-junction proteins and nutrient transport factor genes and proteins (ZO-1, Occludin, Claudin-1, ASCT2 and PePT1). In addition, it was also found that GAP supplementation could significantly increase the diversity of gut microbiota, maintain microbial flora balance and promote piglet growth by significantly increasing the abundance of beneficial bacterium such as Lactobacillus and reducing the abundance of harmful bacterium such as Clostridium_sensu_stricto_1. In conclusion, GAP addition to piglet diets contaminated with DON could significantly promote the health and growth performance of piglets though alleviating DON-induced hazards. This study provided a theoretical basis for the application of GAP to alleviate DON toxicity for animals.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".