Bibliographic record
Abstract
Foods are important vehicles for transmission of infectious bacterial pathogens. Reliable methods for detecting these organisms would ultimately result in fewer contaminated products released into the marketplace. Leveraging genomics tools such as whole genome sequencing (WGS) and metagenomics within the domain of food microbiology presents promising opportunities for enhancing diagnostic methods. This thesis demonstrates the value of genomics-based approaches for improving microbiological methods for foodborne pathogen detection. Two examples of recovery methods developed using WGS-informed antimicrobial resistance (AMR) traits are presented. Furthermore, the application of metabarcoding analysis investigating microbial compositions in food enrichments through 16s rDNA sequencing, is explored to gain a deeper understanding of pathogen growth relative to the background microbiota of food samples. The first case exemplifies a custom (strain-specific) selective enrichment approach for Shigella recovery from outbreak-associated food. This method incorporates enrichment media supplemented with antibiotics chosen based on the WGS-predicted AMR features of the target pathogen. Chapter 2 evaluated the feasibility of the antibiotic-supplemented custom enrichment media in enhancing the recovery of viable drug-resistant Shigella during competition with interfering microorganisms. Chapter 3 further investigated the performance of the custom selective enrichment media for Shigella recovery from baby carrots linked to historical shigellosis outbreak. The addition of the appropriate antibiotics in food enrichment media reduced the relative proportion of competing bacteria in the enrichment cultures and significantly enhanced the recovery of drug-resistant S. sonnei. This demonstrates the potential of genomically-informed selective enrichment in aiding foodborne shigellosis outbreak investigations. To explore whether the application of the genomcally-informed selective enrichment approach could be expanded for an entire pathogen species, the second example (chapter 4) presents an overview of a novel Salmonella selective enrichment broth (Minimal Salts Medium supplemented with amikacin) developed based on a species-specific aminoglycoside resistance genotype (aac(6’)-Iy or aac(6’)-Iaa). The performance of this medium relative to the current enrichment methods by Health Canada was evaluated using chicken feed contaminated with S. Enteritidis. While the novel approach did not provide improved selectivity relative to current methods, 16S rDNA sequencing provided insight into enrichment dynamics in the media evaluated that could be applied to further refinement of this methodology.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".