Genomic Approaches for the Characterization of Shiga-toxigenic E. coli (STEC)
Bibliographic record
Abstract
Foodborne illness in Canada and worldwide often results in unnecessary suffering, long-term disability, and loss of life.Shiga-toxigenic E. coli (STEC) are a significant cause of foodborne illness in Canada, and as a result, it is of high priority to improve detection, isolation, and characterization methods to mitigate their impact on public health.While current methods generally provide accurate strain characterization at an unprecedented resolution, they occasionally face limitations that may have significant implications on risk assessment.Traditionally, STEC methods have primarily focused on O157 STEC; however, there is a need to shift towards methodologies that target STEC of all serotypes, as the role of non-O157 STEC in clinical illness has become clear and the STEC risk assessment criteria transition to virulence factors such as Shiga toxins.To support this, a comprehensive Shiga toxin Database (STxDB) was curated.The STxDB will be used in conjunction with PoreSippR: a rapid method for the characterization of STEC using Nanopore sequencing, which together, will provide comprehensive characterization of STEC within 1-2 hours, in addition to ensuring full-length Shiga toxin genes are elucidated each time an STEC strain is characterized which will be accurately subtyped.Finally, an STEC benchmark dataset consisting of 158 STEC strains of a diverse range of serotypes and virulence profiles was curated for use in the development of novel STEC methods, which will ensure the detection a wide variety of STEC in Canadian foods.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.007 |
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".