Effect of bovine lactoferrin and lactoferrin-derived peptides on planktonic cells and abiotic surface biofilms of <i>Salmonella enterica</i>
Bibliographic record
Abstract
Abstract Salmonella enterica is a Gram-negative bacterium responsible approximately for 155,000 deaths annually. S. enterica is one of the most important foodborne pathogens, affecting mainly people in developed countries. The human immune system produces antibacterial peptides and proteins like lactoferrin (LF). This work addresses the hypothesis that bovine lactoferrin (bLF) and its derivative peptides bLactoferricin17-30, bD-Lactoferricin17-30, bLactoferrampin265-284, bD-Lactoferrampin265-284 and bLF-chimera have antimicrobial activity on planktonic cells and pre-formed biofilms of S. enterica. Planktonic Salmonella enterica ATCC 14028 were treated with bLF and bLF-peptides for two hours, and bacterial viability was determined by counting colony-forming units/ml. In addition, S. enterica biofilms were pre-formed or established on an abiotic surface, and viability or disruption was assessed in the presence of bLF and bLF-peptides by counting colony-forming units/ml or using the live/dead viability kit. We observed that bLF and bLF-peptides were bactericidal against planktonic S. enterica, killing more than 80% of cultures after two hours of treatment. The bactericidal effect was concentration and time-dependent. In addition, bLF, bLFampin165-284, and bLF-chimera showed an anti-biofilm effect against Salmonella biofilms pre-formed during 8 and 12 hours on the abiotic surface, disorganizing more than 50% of the biofilms after 4 or 6 hours of treatment. We conclude that bLF and its peptides show antimicrobial activity against planktonic cells and pre-formed biofilms of S. enterica on abiotic surfaces and could potentially be a therapeutic solution to combat Salmonella infections.
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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".