Bovine lactoferrin and chimera lactoferrin prevent and destroy <i>Salmonella</i> Typhimurium biofilms in Caco-2 cells
Bibliographic record
Abstract
Salmonellosis is a common foodborne disease caused by Salmonella bacteria. The emergence of multidrug-resistant (MDR) Salmonella serotypes, such as Typhimurium, and Salmonella’s ability to form biofilms contribute to their resistance and persistence in host and non-host environments. New strategies are needed to treat or prevent Salmonella infections. This work aimed to determine the effect of the bovine lactoferrin (bLF) and lactoferrin chimera (LFchimera) in preventing or disrupting biofilms formed on abiotic surfaces or Caco-2 cells by S. Typhimurium ATCC 14028 or an MDR strain. The inhibitory activity of planktonic bacteria, prevention of biofilm formation, and destruction of biofilms of S. Typhimurium (ATCC 14028 or MDR strain) on the abiotic surface and Caco-2 cells of bLF and LFchimera were quantified by CFU/mL and visualized by microscopy using Giemsa-stained samples. bLF (75–1000 µM) and LFchimera (1–20 µM) inhibited more than 95% of S. Typhimurium planktonic growth cultures (ATCC 14028 and MDR). In addition, bLF (600, 800, and 1000 µM) and LFchimera (10 and 20 µM) prevented more than 98% of S. Typhimurium adherence and biofilm formation on Caco-2 cells. Finally, bLF (600 and 1000 µM) and LFchimera (10 and 20 µM) destroyed more than 80% of S. Typhimurium biofilms established on abiotic and Caco-2 cells. In conclusion, bLF and LFchimeras have the potential to inhibit and destroy S. Typhimurium biofilms.
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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".