Anti-adhesion of probiotic <i>Enterococcus faecium</i> WEFA23 against five pathogens and the beneficial effect of its S-layer proteins against <i>Listeria monocytogenes</i>
Bibliographic record
Abstract
Enterococcus faecium WEFA23 is a potential probiotic strain isolated from Chinese infant feces. In this study, the antagonistic activity of E. faecium WEFA23 on adhesion to pathogens was investigated. Enterococcus faecium WEFA23 was able to compete, exclude, and displace the adhesion of Escherichia coli O157:H7, Salmonella Typhimurium ATCC 13311, Listeria monocytogenes CMCC54007, Staphylococcus aureus CMCC26003, and Shigella sonnei ATCC 25931 to Caco-2 cells. Among them, L. monocytogenes achieved the strongest inhibition rate in both competition and displacement assays. Those anti-adhesion capacities were related to the bacterial physicochemical properties (hydrophobicity, auto-aggregation, and co-aggregation) of the bacterial surface. For L. monocytogenes, the anti-adhesion capacity was affected by the heat treatment, cell density, and growth phase of E. faecium WEFA23; 108 colony-forming units of viable cells per millilitre at the stationary phase exhibited the strongest anti-adhesion activity. In addition, removal of S-layer proteins of E. faecium WEFA23 by treatment with 5 mol/L LiCl significantly decreased its adhesion capacity, and those S-layer proteins were able to compete, displace, and exclude L. monocytogenes at different levels. Both cells and S-layer proteins of E. faecium WEFA23 significantly reduced the apoptosis of Caco-2 cells induced by L. monocytogenes, which was mediated by caspase-3 activation. This study might be helpful in understanding the anti-adhesion mechanism of probiotics against 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) | 0.001 | 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".