Exploring novel antifungal peptides from peptic hydrolysis of chicken cruor protein via regression-based machine learning approach
Bibliographic record
Abstract
There is a growing interest in natural preservatives driven by consumer demand for clean-label products. In Canada, approximately 48 million liters of blood are produced annually during chicken slaughter, offering an opportunity to valorize cruor, the solid blood component rich in hemoglobin, for use in food preservation. This study investigated the hydrolysis of chicken cruor with pepsin at pH 2, 3, 4, and 5 for 180 min to produce antimicrobial peptides. The highest degree of hydrolysis (11.70 ± 0.77 %) was observed at pH 2, similar to pH 3 where the enzyme exhibited a zipper mechanism. Hydrolysates at pH 2 and 3 inhibited fungal strains (Paecilomyces spp., Rhodotorula mucilaginosa, and Mucor racemosus) with MIC: 0.63 mM, while no antibacterial activity was observed. Partial Least Square-Discriminant Analysis (PLS-DA) allowed the identification of 31 antifungal peptides, including LARKYH, active against R. mucilaginosa (MIC: 0.63 mM), highlighting chicken cruor's potential as a source of bio-preservatives.
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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".