Effects Different Levels of Nanoparticles Chromium Picolinate Supplementation on Growth Performance, Mineral Retention, and Immune Responses in Broiler Chickens
Bibliographic record
Abstract
This study was conducted to investigate the effects of different levels nanoparticles of chromium picolinate (NanoCrPic) on the performance, immune responses, mineral retention, and tissues accumulation of chickens. A total of 180 broilers were randomly allocated into 0 (control), 500 ppb (µg kg-1) Cr and 3000 ppb Cr groups with 6 replicates (10 birds/pen) for a 35-day experiment, the Cr is nanoparticles of chromium picolinate (NanoCrPic). In addition, 36 birds were used for metabolic experimental investigation. The results of the experimentation indicated that there were no significant differences in average body weight gain between groups, but feed conversion ratio (FCR) in 3000 ppb group was better than control group during 1-21 days. The carcass yields slightly lower in 3000 ppb group than control (p<0.1). Retention ratio of Zn, Fe, Mn, Ca, and P were significantly (p<0.05) increased in the 500 ppb Cr group. The addition of NanoCrPic caused increased mineral concentrations, such as Cr, Ca and P in the subjects’ livers. Furthermore, the addition of NanoCrPic significantly increased lymphocytes and decreased both heterophils and H/L ratio (p<0.05). The ND (Newcastle disease) antibody titer was not affected in the broilers. In conclusion, supplemental NanoCrPic improved the retention of Zn, Fe Ca, notably it increased the concentration of Cr and Ca in the liver, and also increased the number of lymphocytes in broiler chickens.
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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) | 0.000 | 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".