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 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 | 0.002 | 0.001 |
| 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".