Effects of Compound Mycotoxin Detoxifier on Alleviating Aflatoxin B1-Induced Inflammatory Responses in Intestine, Liver and Kidney of Broilers
Bibliographic record
Abstract
In order to alleviate the toxic effects of aflatoxins B1 (AFB1) on inflammatory responses in the intestine, liver, and kidney of broilers, the aflatoxin B1-degrading enzyme, montmorillonite, and compound probiotics were selected and combined to make a triple-action compound mycotoxin detoxifier (CMD). The feeding experiment was divided into two stages. In the early feeding stage (1−21 day), a total of 200 one-day-old Ross broilers were randomly divided into four groups; in the later feeding stage (22−42 day), 160 broilers aged at 22 days were assigned to four groups: Group A: basal diet (4.31 μg/kg AFB1); Group B: basal diet with 40 μg/kg AFB1; Group C: Group A plus 1.5 g/kg CMD; Group D: Group B plus 1.5 g/kg CMD. After the feeding experiment, the intestine, liver, and kidney tissues of the broilers were selected to investigate the molecular mechanism for CMD to alleviate the tissue damages. Analyses of mRNA abundances and western blotting (WB) of inflammatory factors, as well as immunohistochemical (IHC) staining of intestine, liver, and kidney tissues showed that AFB1 aggravated the inflammatory responses through NF-κB and TN-α signaling pathways via TLR pattern receptors, while the addition of CMD significantly inhibited the inflammatory responses. Phylogenetic investigation showed that AFB1 significantly increased interleukin-1 receptor-associated kinase (IRAK-1) and mitogen-activated protein kinase (MAPK) activities (p < 0.05), which were restored to normal levels by CMD addition, indicating that CMD could alleviate cell inflammatory damages induced by AFB1.
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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".