Characterization of Non-O157 STEC Infecting Bacteriophages Isolated from Cattle Faeces in North-West South Africa
Bibliographic record
Abstract
Non-O157 Shiga toxin-producing Escherichia coli (STEC) E. coli are emerging pathotypes that are frequently associated with diseases in humans around the world. The consequences of these serogroups for public health is a concern given the lack of effective prevention and treatment measures. In this study, ten bacteriophages (phages; SA20RB, SA79RD, SA126VB, SA30RD, SA32RD, SA35RD, SA21RB, SA80RD, SA12KD and SA91KD) isolated from cattle faeces collected in the North-West of South Africa were characterized. Activity of these phages against non-O157 STEC isolates served as hosts for these phages. All of the phages except SA80RD displayed lytic against non-O157 E. coli isolates. Of 22 non-O157 E. coli isolates, 14 were sensitive to 9 of the 10 phages tested. Phage SA35RD was able to lyse 13 isolates representing a diverse group of non-O157 E. coli serotypes including a novel O-antigen Shiga toxigenic (wzx-Onovel5:H19) strain. However, non-O157 E. coli serotypes O76:H34, O99:H9, O129:H23 and O136:H30 were insensitive to all phages. Based on transmission electron microscopy, the non-O157 STEC phages were placed into Myoviridae (n = 5) and Siphoviridae (n = 5). Genome of the phage ranged from 44 to 184.3 kb. All but three phages (SA91KD, SA80RD and SA126VB) were insensitive to EcoRI-HF and HindIII nucleases. This is the first study illustrating that cattle from North-West South Africa harbour phages with lytic potentials that could potentially be exploited for biocontrol against a diverse group of non-O157 STEC isolated from the same region.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".