The Influence of Ethanolic Extract of Hypericum Perforatum (St. John's wort) on Growth Performance, Serum Metabolites, Fat Deposition, Immunity, and Lipid Peroxidation in Broiler Chickens
Bibliographic record
Abstract
BACKGROUND: Having bioactive components such as hypericin, hyperforin and quercetin has enabled Hypericum perforatum (HP) to show antioxidant, antiviral and hypocholesterolemic effects in different animal species. OBJECTIVES: This study aimed to investigate the effects of different levels of Hypericum perforatum extract (HPE) additions on performance, immune response, serum metabolites, and lipid peroxidation in broiler chickens. METHODS: A total of 250 one-day-old broiler chicks were randomly allocated to five treatments with five replicates and ten chicks each. Experimental rations consisted of a basal diet with no supplement (control group) and a basal diet with 0.1%, 0.5%, 1%, and 1.5% HPE. RESULTS: Results showed that the addition 1.5% HPE in the broiler diet increased high-density lipoproteins (HDL) compared to the control diet. While the total cholesterol (TC) was significantly reduced by HPE supplementation, only feeding 1% and 1.5% HPE significantly lowered the low-density lipoproteins (LDL). HPE addition to the diet significantly reduced abdominal and breast fat at 0.5%, 1%, and 1.5% levels. However, thigh fat was significantly decreased by dietary all HPE levels supplementation. Moreover, in the group fed with 1.5% HPE, the immunoglobulin G (IgG) and thymus weight significantly increased compared to the control group. Compared with the control group, HPE-fed groups showed significantly lower malondialdehyde (MDA) concentrations in breast muscle. The total antioxidant capacity (T-AOC) was improved significantly by HPE supplementation. CONCLUSIONS: In general, while there was no significant difference among treatments in the case of growth performance parameters, dietary supplementation of HPE at 1.5% was the most effective dose to improve serum biochemical metabolites, fat metabolism, immune response, and oxidative stability in broiler chicks.
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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".