Promising bioactive peptides from turkey cruor: How hydrolysis duration and pH could affect the antimicrobial activity and peptide population
Bibliographic record
Abstract
: Recently, slaughterhouse blood, particularly its solid part (cruor), predominantly composed of hemoglobin, has attracted increasing attention as a potential source of antimicrobial peptides released by enzymatic hydrolysis. Among the different blood sources, bovine and porcine ones were the most extensively studied. This study explores, for the first time, turkey cruor as a substrate for peptic hydrolysis and evaluates the impact of different pH values on the peptide population, the enzymatic mechanism, and antimicrobial activities of hydrolysates. Different final degrees of hydrolysis were observed among the different pH values. In addition, two distinct enzymatic mechanisms (zipper and one by one) were identified. These two variations induce the generation of different peptide populations. A total of 39, 42, 34, and 11 potential antimicrobial peptides were identified by reversed-phase ultrahigh-performance liquid chromatography with tandem mass spectrometry and bioinformatics tools at t 30min. Those peptides were present with higher relative molar concentrations at pH 2 and 3 compared to pH 4 and 5. Turkey cruor hydrolysates demonstrated anti-yeast and antifungal activities; the lowest minimum inhibitory concentration (MIC) for Paecilomyces spp was observed at pH 2 and pH 3 after 30 min of hydrolysis (0.63 mg.ml -1 ). For Mucor racemosus and Rhodotorula mucilaginosa , the lowest MIC were observed at pH 3 after 30 min of hydrolysis (1.25 mg.ml -1 and 0.63 mg.ml -1 , respectively). In addition, no antibacterial activity was detected. This study demonstrated for the first time the antimicrobial potential of turkey cruor hydrolysates, highlighting their potential role as a natural preservative in food industry products.
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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 | 0.000 | 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) | 0.001 | 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".