Antimicrobial activity of buttermilk and lactoferrin peptide extracts on poultry pathogens
Bibliographic record
Abstract
Antibiotics are commonly used in poultry feed as growth promoters. This practice is questioned given the arising importance of antibiotic resistance. Antimicrobial peptides can be used as food additives for a potent alternative to synthetic or semi-synthetic antibiotics. The objective of this study was to develop a peptide production method based on membrane adsorption chromatography in order to produce extracts with antimicrobial activity against avian pathogens (Salmonella enterica var. Enteritidis, Salmonella enterica var. Typhimurium, and two Escherichia coli strains, O78:H80 and TK3 O1:K1) as well as Staphylococcus aureus. To achieve this, buttermilk powder and purified lactoferrin were digested with pepsin. The peptide extracts (<10 kDa) were fractionated depending on their charges through high-capacity cation-exchange and anion-exchange adsorptive membranes. The yields of cationic peptide extracts were 6·3 and 15·4% from buttermilk and lactoferrin total peptide extracts, respectively. Antimicrobial activity was assessed using the microdilution technique on microplates. Our results indicate that the buttermilk cationic peptide extracts were bactericidal at less than 5 mg/ml against the selected avian strains, with losses of 1·7 log CFU/ml (Salm. Typhimurium) to 3 log CFU/ml (E. coli O78:H80); viability decreased by 1·5 log CFU/ml for Staph. aureus, a Gram-positive bacterium. Anionic and non-adsorbed peptide extracts were inactive at 5 mg/ml. These results demonstrate that membrane adsorption chromatography is an effective way to prepare a cationic peptide extract from buttermilk that is active against avian pathogens.
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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) | 0.000 | 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 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".