448 CHARACTERIZATION OF SECRETED PROTEINS OF NON-O157:H7 ENTEROHEMORRHAGIC ESCHERICHIA COLI SEROTYPES AND THEIR USE IN BOVINE VACCINE STUDIES.
Bibliographic record
Abstract
Purpose Secreted proteins (SPs) of enterohemorrhagic Escherichia coli (EHEC), a diarrhegenic pathogen, are central to the infection process, accounting not only for EHEC9s ability to invade the host and evade host defense systems but also for many clinical manifestations of the disease. To learn more about EHEC pathogenesis, the SPs of various EHEC human and bovine isolates were studied. Methods SPs of all serotypes used in the study were analyzed by SDS-PAGE and Western blotting. Additionally, some SPs of EHEC O103:H2 were analyzed by mass spectrometry and sequenced. SPs of 5 common EHEC serotypes were concentrated by ultrafiltration for use in bovine vaccine studies. Results SPs of non-O157:H7 serotypes were found to be sufficiently dissimilar to prevent their recognition by monoclonal antibodies directed against O157:H7 SPs, with the exception of the SPs of O55:H7. Polyclonal antibodies directed against EHEC O157:H7 SPs detected the SPs of most other serotypes. O103:H2 Z2338 was completely sequenced and found to be 96% identical to O157:H7 Z2338; however, Z2149 of O103:H2, which was partially sequenced, was only 40% identical to its O157:H7 homologue. Both proteins were secreted in greater amounts by O103:H2 than by O157:H7. Western blotting of SP from different EHEC serotypes using sera of 2 convalescent patients from 2 different EHEC outbreaks demonstrated obvious variations in patients9 serological responses. SPs of five serotypes commonly implicated in EHEC outbreaks were prepared for future use in bovine vaccine trials. Conclusions These results demonstrate the heterogeneity of SPs of different EHEC serotypes and highlight the diversity in serotype composition of different EHEC outbreaks. They further our knowledge of EHEC SP arsenal and can play a role in the design of effective bovine and potentially human vaccines against EHEC.
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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) | 0.000 | 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".