Condensed Tannins Improve Glycolipid Metabolism but Induce Liver Injury of Chinese Seabass (Lateolabrax maculatus)
Bibliographic record
Abstract
A 63-day feeding trial with 640 juvenile L. maculatus was conducted to assess the effects of dietary CT on serum metabolites, antioxidant and immune response, liver histomorphology and glycometabolism enzyme activities of fish. Four diets were formulated to contain 0 (T1), 1 (T2), 2 (T3), and 2 g/kg of CT plus 4 g/kg of polyethylene glycol (PEG) (T4). PEG specifically binds with CT to neutralize CT activity. Fish were randomly distributed into 16 tanks (4 tanks per group and 40 fish per tank) and were fed to apparent satiation twice daily. Results indicated that fish fed T2 and T3 had lower (P<0.05) concentrations of serum albumin, total cholesterol and triacylglycerol, but higher (P<0.05) alanine aminotransferase activity than T1 and T4. T3 had lower (P<0.05) serum glucose but higher (P<0.05) insulin concentrations than other groups. Fish fed T2 and T3 had higher (P<0.05) liver superoxide dismutase, catalase, lysozyme and immune globulin M than T1 and T4. Compared with T1 and T4, hepatocytes in T2 and T3 were shown to have obvious vacuolar degeneration along with different degree of inflammatory cell infiltration. Fish fed T2 and T3 had higher (P<0.05) values of eosinophilic infiltrate and necrosis and greater (P<0.05) pyruvate kinase, but lower (P<0.05) glucokinase activities than those of fish fed T1 and T4. All parameters were similar (P>0.05) between T1 and T4. In summary, dietary CT up to 2 g/kg reduced serum lipid and glucose concentrations, enhanced liver antioxidant and immune response, improved glucose utilization but of L. maculatus. CT induced liver injury of L. maculatus which provided a caution for its application in aquaculture.
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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) | 0.000 | 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".