Strain-specific probiotic (<i>Lactobacillus helveticus</i>) inhibition of<i>Campylobacter jejuni</i>âinvasion of human intestinal epithelial cells
Bibliographic record
Abstract
Campylobacter jejuni is the most common bacterial cause of enterocolitis in humans, leading to diarrhoea and chronic extraintestinal diseases. Although probiotics are effective in preventing other enteric infections, beneficial microorganisms have not been extensively studied with C. jejuni. The aim of this study was to delineate the ability of selected probiotic Lactobacillus strains to reduce epithelial cell invasion by C. jejuni. Human colon T84 and embryonic intestine 407 epithelial cells were pretreated with Lactobacillus strains and then infected with two prototypic C. jejuni pathogens. Lactobacillus helveticus, strain R0052 reduced C. jejuni invasion into T84 cells by 35-41%, whereas Lactobacillus rhamnosus R0011 did not reduce pathogen invasion. Lactobacillus helveticus R0052 also decreased invasion of one C. jejuni isolate (strain 11168) into intestine 407 cells by 55%. Lactobacillus helveticus R0052 adhered to both epithelial cell types, which suggest that competitive exclusion could contribute to protection by probiotics. Taken together, these findings indicate that the ability of selected probiotics to prevent C. jejuni-mediated disease pathogenesis depends on the pathogen strain, probiotic strain and the epithelial cell type selected. The data support the concept of probiotic strain selectivity, which is dependent on the setting in which it is being evaluated and tested.
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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) | 0.002 | 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".