Valorization of animal waste proteins for agricultural, food production, and medicinal applications
Bibliographic record
Abstract
Introduction Animal waste proteins have been increasing in the past decade, along with consumer demands. Their huge volume and the environmental issues caused by improper treatment probably pose a massive threat to human health. These animal waste proteins contain many valuable bioactive peptides and can be used not only as nutrient substances but also as primary functional ingredients in many industries, including agriculture, food, and pharmaceuticals. However, the advancement of the value-added application of animal waste proteins within the past 10 years has not been elucidated yet. In this regard, this paper scrutinized the studies on the applications of hydrolysates and peptides from animal waste proteins throughout the last decade, hoping to display a whole picture of their value-adding applications. Methods The Web of Science and Google Scholar were searched from January 1, 2013, to December 12, 2023. This review included field trials, in vitro and in vivo assays, and in silico analysis based on literature surveys or proteolysis simulation. The quality of the included studies was evaluated by Journal Citation Reports, and the rationality of the discussion of studies included. Results Numerous studies were performed on the application potential of hydrolysates and peptides of animal waste proteins in agricultural, food, and medicinal industries. Particularly, due to the nutritional value, safety, and especially competitive effects, the peptide with antioxidant, antimicrobial, antihypertensive, antidiabetic, or antithrombotic activities can be used as a primary functional ingredient in food and pharmaceuticals. Discussion These value-added applications of animal waste proteins could be a step towards sustainable animal by-products management, and simultaneously, open new avenues in the rapid development of nutraceuticals and pharmaceuticals. However, further studies on the bioavailability and structure-activity relationship are required to verify their therapeutic effects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".