P-117. Wastewater-Based Surveillance (WBS) of Shiga Toxin-Producing <i>Escherichia coli</i> (STEC) in Alberta, Canada
Bibliographic record
Abstract
Abstract Background Shigatoxin-producing E. coli (STEC) are responsible for significant human morbidity, with the potential to cause severe food-borne illness and outbreaks. STEC incidence varies between communities and peaks in summer months (PMID 31652648). Leveraging a SARS-CoV-2 WBS program, we sought to explore genomic targets for STEC WBS.Figure 1.Prevalence of STEC differs across Alberta’s municipal sewer sheds Methods Composite-24h wastewater (WW) was collected from geographically disparate, and socioeconomically diverse Alberta communities (n=5) at the level of municipal WW treatment plants. From 04/2022-03/2024, monthly WW underwent pelleting and DNA extraction by Qiagen DNeasy PowerSoil Pro kit. WW extracts were assessed for four potential genomic STEC targets: Shiga Toxin 1 (stx1), Shiga Toxin 2 (stx2), Intimin (eae) and LPS O antigen gene specific for O157 (rfbEO157), by multiplex PCR. Each target was normalized by 16S rRNA-for total bacterial burden. WW STEC targets were assessed for correlation using Spearman’s and compared between communities and seasonality (July-Sept vs Jan-Mar) by Mann-Whitney U-test.Figure 2.STEC WBS demonstrates seasonal prevalence trends Results Of 111 WW samples assessed, 108 (97%) were positive for all targets, and individual targets were identified in 110 (99%) stx1; 110 (99%;) stx2; 111 (100%) eae, and 110 (99%) rfbEO157. Gene abundance for each STEC target exhibited strong correlations across sites (stx1 vs stx2, r=0.802, p< 0.0001; stx2 vs rfbEO157, r=0.634, p< 0.0001; eae vs rfbEO157, r =0.551, p< 0.0001; stx2 vs. eae r=0.542, p< 0.0001). WW measured STEC gene targets exhibited significant differences between municipalities (Figure 1) with strong seasonal trends (Figure 2). Conclusion WBS for STEC yielded patterns consistent with established patterns of disease (PMID 31652648). All four STEC genomic targets demonstrated significant correlation across sewersheds. STEC WBS may represent a novel tool to understand and monitor population-level activity and prevent disease. Disclosures All Authors: No reported disclosures
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".