Protective effect of Bacillus subtilis ANSB060 on egg quality, biochemical and histopathological changes in layers exposed to aflatoxin B1
Bibliographic record
Abstract
Bacillus subtilis ANSB060 from the fish gut had strong ability to detoxify aflatoxins. The aim of this research was to investigate the protective effect of B. subtilis ANSB060 (ANSB060) on egg quality and biochemical and histopathological changes of liver and kidney in laying hens when exposed to aflatoxin B(1). Treatments (C20, C40, and C60) were prepared by substituting corn contaminated by aflatoxin B(1) (AFB1) at different proportions (20, 40, and 60%) for normal corn in basic diets. The aflatoxin degradation enzyme (E) treatments (E20, E40, and E60) were mixed with the fermentation liquor of ANSB060 with C20, C40, and C60, respectively. The results showed that ANSB060 can improve the eggshell strength in E60 compared with C60 (P ≤ 0.05), and toxin reduced the content of total protein (in groups C20, C40, and C60) and albumin (in C20 and C40; P < 0.05) and heightened the activities of GPT (in C60) and GOT (in C40 and C60) in serum (P < 0.05). In the liver, AFB1 inhibited the activity of superoxide dismutase and glutathione peroxidase (C40 and C60; P < 0.05) and increased the content of malonaldehyde (in C40 and C60), which induced the damage in the liver and kidney as shown in the photomicrographs of hematoxylin and eosin-stained sections. The addition of ANSB060 can enhance the activity of antioxidant enzymes, and it recovered the protein synthesis in liver. Moreover, ANSB060 also ameliorated the damage of liver and kidney tissue and restored them to normal. Hence, ANSB060 had the ability to inhibit the damage induced by AFB1; it will have a great potential in industrial applications.
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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) | 0.000 | 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".