A mutated rabbit defensin NP-1 produced by Chlorella ellipsoidea can improve the growth performance of broiler chickens
Bibliographic record
Abstract
The demand for alternatives to antibiotics to improve the growth performance of food animals is increasing. Defensins constitute the first line of defence against pathogens in the innate immune system of animals and humans. A transgenic Chlorella ellipsoidea strain producing mNP-1 (a mutated rabbit defensin NP-1) was previously obtained in our laboratory. In this study, a process for producing the transgenic strain on a large scale was developed, and the C. ellipsoidea strain producing mNP-1 was used as a feed additive to improve the health and growth performance of chickens. The volume of C. ellipsoidea producing mNP-1 can be scaled up to 10,000 L with approximately 100 g/L dry biomass, and the mNP-1 content of transgenic microalgal powder (TMP) was 90-105 mg/L. A TMP-to-regular feed ratio of 1‰, as the optimal effective dose, can promote the growth of broiler chickens by increasing weight by 9.27-12.95%. mNP-1 can improve duodenum morphology by promoting long and thin villi and affect the microbial community of the duodenum by increasing the diversity and abundance of beneficial microbes. These results suggested that transgenic Chlorella producing mNP-1 can be industrially produced and used as an effective feed additive and an alternative to antibiotics for improving the health and growth performance of broiler chickens or other types of food animals/poultry.
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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".