Local bacteriophage isolates showed anti-<i>Escherichia coli</i>O157:H7 potency in an experimental ligated rabbit ileal loop model
Bibliographic record
Abstract
Escherichia coli O157:H7 is considered among the most important recently emerged food-borne bacteria causing severe hemorrhagic diarrhea. Antibiotic treatment is not recommended as a prospective curative agent against this pathogen. Therefore, potency assessment of the local lytic phage isolates infecting E. coli O157:H7 as an alternate remedy to antibiotics was the principal concern of this study. Phage isolates against E. coli O157:H7 were checked by polymerase chain reaction for the presence of the virulence genes stx1 and stx2, and the safe phages were further screened in vitro for their capacity as biocontrol agents. Two bacteriophage strains, namely PAH6 and P2BH2, that had expressed potential antibacterial activity (P < 0.05) in vitro were selected for in vivo testing in ligated rabbit ileal loop models. Both phage isolates were capable of decreasing fluid accumulation in rabbit ileal loops along with reducing bacterial growth (r = 0.992). Combined application of the phages was found most satisfactory, reducing seven log cycles of bacterial growth. Consistent results in both in vivo and in vitro experiments demonstrate the applicability of bacteriophages as a rapid response tool against E. coli O157:H7. To our knowledge, this is the first successful application of the rabbit ileal loop test for therapeutic evaluation of bacteriophages.
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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".