Upcycling calf cruor slaughterhouse by-product by peptic hydrolysis and identification of three novel antifungal peptides following a regression based-machine learning approach
Bibliographic record
Abstract
• Hydrolysis of Calf-cruor followed the same enzymatic mechanisms as adult bovine hemoglobin. • Peptic hydrolysis of Calf-cruor at pH 2 and pH 3 for 180 min promoted antibacterial activity. • Peptic hydrolysis of Calf-cruor at pH 3 for 30 min promoted antifungal activity. • PLS-DA allowed the identification of antimicrobial peptides from complex solutions. • Three new antifungal peptide sequences α(47–80), β(130–145), and β(102–109) were identified. Calf cruor (C-cru) is an understudied source of antimicrobial peptides that can be used for meat product biopreservation under a circular economy framework. This study assessed the impact of pH and peptic hydrolysis duration of C-cru on the enzymatic mechanism, peptide population, and antimicrobial activities (antibacterial, antifungal, and anti-yeast). The results showed that peptic hydrolysis of C-cru has similar enzymatic mechanisms to adult bovine hemoglobin, fostering the development of a zipper mechanism at pH 2 and 3, while the one-by-one mechanism is promoted at pH 4 and 5. The antimicrobial activities suggested that peptic hydrolysis at pH 2 and 3 for 180 min fostered higher antibacterial activity, while peptic hydrolysis at pH 3 for 30 min favored both antifungal and anti-yeast activities. Furthermore, a supervised machine learning tool was applied for the first time to peptidomic data enabling the elucidation of α(47–80), β(130–145), and β(102–109) as new antifungal peptide sequences.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".