Generation and characterization of random and site-directed mutants of Shiga-like toxin 1A by Escherichia Coli O157:H7 in Saccharomyces Cerevisiae
Bibliographic record
Abstract
Food-borne illnesses are mainly associated with Shiga-like toxins (Stx1 and Stx2) produced by various serotypes of enterohemorrhagic Escherichia coli (EHEC). These serotypes are collectively known as STEC (Stx-producing E. coli). One of these serotypes E. coli O157:H7 has been the major cause of food-borne illnesses recently in US, Canada and Japan. The clinical manifestations of EHEC infections range from watery diarrhea, severe bloody diarrhea, abdominal cramps and hemorrhagic colitis (HC), to the most severe outcome, life-threatening hemolytic uremic syndrome (HUS) resulting in kidney failure.This study involved generation and characterization of random and site-directed mutants of Shiga-like toxin 1 in Saccharomyces cerevisiae. The mutants were characterized for protein expression, ribosome depurination and loss of cytotoxicity. The results from this study are crucial to understand the mechanism by which Shiga-like toxins exhibit their cytotoxicity to the cells. The results from this study can aid in development of treaments against the diseases caused by Shiga-like toxin 1.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".