Evaluation of Dietary Curcumin Nanospheres in a Weaned Piglet Model
Bibliographic record
Abstract
Curcumin is a polyphenolic compound present in turmeric with extensive uses in cooking foods and biomedical applications. However, due to its hydrophobic nature, it is poorly soluble in water and its bioavailability is very low on oral administration in organisms. In this study, we investigated the dietary curcumin nanospheres in a weaned piglet model based on the growth, serum biochemistry, proteomics, fecal coliform bacteria, and malodors in the feces of piglets. A total of 135 weaned piglets (Duroc × [Yorkshire × Landrace]) with an average initial body weight of 7.0 ± 1.0 kg (28 ± 1 days of age) were randomly distributed in 9 pens (15 pigs in each pen) fed the dietary curcumin nanospheres (CN) at 0 (control), 0.5 (T1), and 1.0 mL (T2) CN/kg of diet in triplicates for 21 days. At the end of the feeding trial, the results showed piglets fed 1.0 mL CN/kg diet had significantly higher growth performance and feed utilization than control diet (without CN). However, there were no significant differences in growth and feed utilization between piglets fed T1 and T2 diets. Serum glucose, gamma-glutamyl transferase, total bilirubin, amylase, and lipase contents were unaffected in piglets fed the experimental diets. Interestingly, piglets fed T1 and T2 diets showed significantly lower total cholesterol levels than control diet. In serum proteomics, a total of 103 differentially expressed proteins (DEPs) were identified in the piglets fed control, T1, and T2 diets, of which 14 DEPs were upregulated and 4 DEPs were downregulated. Fecal coliform bacteria and ammonia gas were significantly reduced in piglets fed T1 and T2 diets. Overall, the results indicated dietary supplementation of CN could enhance the growth, feed utilization, and immunity-and reduce fecal pathogenic bacteria as well as ammonia gas emissions-in weaned piglets.
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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 | 0.001 |
| 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 | 0.001 | 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".