Use of Multiple-Locus Variable-Number Tandem Repeat Analysis to Evaluate <i>Escherichia coli</i> O157 Subtype Distribution and Transmission Dynamics Following Natural Exposure on a Closed Beef Feedlot Facility
Bibliographic record
Abstract
To better understand the epizootiology of Escherichia coli O157:H7 among cattle, all E. coli O157 isolates recovered on a research feedlot during a single feeding period were characterized by multiple-locus variable-number tandem repeat analysis (MLVA). Three distinct MLVA subtypes (A, B, C), accounting for 24%, 15%, and 64% of total isolates, respectively, were identified. Subtypes A and B were isolated at the initiation of sampling, but their prevalence waned and subtype C, first isolated on the third sampling date, became the predominant subtype on the feedlot. Supershedding events, however, occurred with equal frequency for all three MLVA-types. Using a multilevel logistic regression model, we investigated whether the odds of shedding subtype C relative to subtypes A or B were associated with time, diet, or the presence of a penmate shedding high numbers of subtype C. Only time and exposure to an animal shedding MLVA-type C at 10³ colony-forming units or greater in the pen at the time of sampling were significantly associated with increased shedding of subtype C. High-level shedding of those E. coli O157 subtypes better suited for survival in the environment and/or in the host appear to play a significant role in the development of predominant E. coli O157 subtypes. Supershedding events alone are neither required nor sufficient to drive the epidemiology of specific E. coli O157 subtypes. Additional factors are necessary to direct successful on-farm transmission of E. coli O157.
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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 | 0.001 | 0.001 |
| 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".