How Discoloration of Porcine Cruor Hydrolysate Allowed the Identification of New Antifungal Peptides
Bibliographic record
Abstract
Porcine blood is an important by-product from slaughterhouses and an abundant source of proteins. Indeed, cruor, the solid part of blood, is mainly composed of hemoglobin. Its enzymatic hydrolysis with pepsin generates a diversity of peptides, particularly antimicrobials. One of the downsides of using these hydrolysates as food bio-preservatives is the color brought by the heme, which can be removed by discoloration. Nonetheless, the effects of this procedure on the antimicrobial peptide population have not been completely investigated. In this study, its impacts were evaluated on the final antibacterial and antifungal activities of a cruor hydrolysate. The results demonstrated that 38 identified and characterized peptides showed a partial or total decrease in the hydrolysate, after discoloration. Antifungal activities were observed for the raw and discolored hydrolysates: MICs vary between 0.1 and 30.0 mg/mL of proteins, and significant differences were detected between both hydrolysates for the strains S. boulardii, C. guilliermondii, K. marxianus, M. racemosus and P. chrysogenum. The raw hydrolysate showed up to 12 times higher antifungal activities. Hence, peptides with the highest relative abundance decrease after discoloration were synthesized and tested individually. In total, eight new antifungal peptides were characterized as active and promising. To our knowledge, this is the first time that effective antifungal peptide sequences have been reported from porcine cruor hydrolysates.
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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".