Inhibition of Shiga toxin-producing <i>Escherichia coli</i> O157:H7 attachment to human intestinal cells by single or combined lytic bacteriophages
Bibliographic record
Abstract
We reported phage cocktails of AHP24 (T1), wV7 (T4), AKFV33 (T5), and AHP24S (rV5) had superior efficacy against STEC O157:H7 strains in broth culture and beef matrices, but it is unknown if they can lyse the pathogens in the context of intestinal epithelial cells, which may reduce Shiga toxin-producing Escherichia coli (STEC) attachment, an initial step for STEC invasion. The objective of this study was to compare efficacy of lytic phages T1, T4, T5, and rV5 as individuals or cocktails in preventing STEC attachment to human intestinal epithelial cells. Two intestinal epithelial cell lines, Caco2 and T84, that are susceptible to STEC attachment were used. There were ∼2–3 log 10 colony forming units/mL reductions ( P < 0.0001) in STEC attachment when these epithelial cells were exposed to individual or cocktails of phages 1 h before inoculation. The phage cocktail (T5 + T1 + rV5 + T4) significantly reduced STEC attachment onto T84 cells when compared to individual phage treatments T4 and T1 ( P < 0.0001). Notably, applying three- (excluding T5) or four-phage cocktails concurrent with STEC inoculation did not significantly different from phage pre-exposure. Phages may be a viable approach for preventing and treating STEC infection in human.
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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".