Development and Performance Evaluation of an Antibody-Based Technology for Detection of E. coli O157 in Meat Samples and Its Potential Evolution Using Antibody Engineering
Bibliographic record
Abstract
Escherichia coli O157 is a persistent pathogen linked to food and waterborne infectious outbreaks with severe health consequences such as hemorrhagic colitis and hemolyticuremic syndrome (HUS). Because it is considered one of the major pathogens that contributes to the global burden of foodborne disease, its early detection within the food chain is an important milestone towards reducing foodborne diseases and economic losses due to contaminated food. Herein, the development and validation of a lateral flow immunoassay (LFIA) point-of-care (POC) device is described. Application of the LFIA test kit was focused on detection of E. coli O157 in raw meat products due to the fact that ground beef has been one of the major food items implicated in E. coli outbreaks and recalls within Canada. Moreover, the LFIA Test Kit was subjected to an independent validation study based upon Health Canada’s guidelines for the validation of alternative microbiological methods as established in the Compendium of Analytical Methods. The protocol comprised a pre-collaborative study, where the LFIA Test Kit was compared against the reference culture method, MFHPB-10, using eight different raw meat products following an unpaired samples experimental layout. The results demonstrated that the newly developed LFIA Test Kit exceeds the performance parameters criteria established by the Microbiological Methods Committee (MMC), thus suggesting that the LFIA Test Kit represents a reliable alternative for meat producers in order to obtain presumptive presence/absence results in less than one day. The design and expression of a single-chain variable fragment (scFv) targeting E. coli O157 is also presented. Recombinant antibody fragments such as scFv have not been extensively exploited within food safety diagnostics, especially for pathogen detection. Thus, in this project the anti-O157 mouse monoclonal antibody (mAb) used as the detection reagent in the LFIA Test Kit was genetically sequenced prior to bioengineering a scFv that could potentially be used to improve the performance of the LFIA Test Kit.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".