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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".