Surface-layer protein extracts from Lactobacillus helveticus inhibit enterohaemorrhagic Escherichia coli O157:H7 adhesion to epithelial cells
Bibliographic record
Abstract
Adherence of intestinal pathogens, including Escherichia coli O157:H7, to human intestinal epithelial cells is a key step in pathogenesis. Probiotic bacteria, including Lactobacillus helveticus R0052 inhibit the adhesion of E. coli O157:H7 to epithelial cells, a process which may be related to specific components of the bacterial surface. Surface-layer proteins (Slps) are located in a paracrystalline layer outside the bacterial cell wall and are thought to play a role in tissue adherence. However, the ability of S-layer protein extract derived from probiotic bacteria to block adherence of enteric pathogens has not been investigated. Human epithelial (HEp-2 and T84) cells were treated with S-layer protein extract alone, infected with E. coli O157:H7, or pretreated with S-layer protein extract prior to infection to determine their importance in the inhibition of pathogen adherence. The effects of S-layer protein extracts were characterized by phase-contrast and immunofluorescence microscopy and measurement of the transepithelial electrical resistance of polarized monolayers. Pre-treatment of host epithelial cells with S-layer protein extracts prior to E. coli O157:H7 infection decreased pathogen adherence and attaching-effacing lesions in addition to preserving the barrier function of monolayers. These in vitro studies indicate that a non-viable constituent derived from a probiotic strain may prove effective in interrupting the infectious process of an intestinal pathogen.
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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".