Ultrasensitive Analysis of <i>Escherichia coli</i> O157:H7 Based on Immunomagnetic Separation and Labeled Surface-Enhanced Raman Scattering with Minimized False Positive Identifications
Bibliographic record
Abstract
It is a big challenge to monitor pathogens in food with high selectivity. In this study, we reported an ultrasensitive method for Escherichia coli O157:H7 detection based on immunomagnetic separation and labeled surface-enhanced Raman scattering (SERS). The bacterium was identified by heterogeneous recognition elements, monoclonal antibody (mAb), and aptamer. E. coli O157:H7 was separated and enriched by magnetic nanoparticles modified by mAb, and then a plasmonic nanostructure functionalized by aptamers with embedded Raman tags and interior gaps was utilized for further discrimination and detection. The selectivity was enhanced by two binding sites. The higher Raman enhancement was obtained by strong local electromagnetic field oscillation in the gap and the firm embedment of 4-mercaptopyridine (4-Mpy). Optimum experiments created that SERS signals of 4-Mpy at 1010 cm –1 had a good linearity with E. coli O157:H7 at a large range of 10 to 10 7 CFU/mL with a limit of detection of 2 CFU/mL. This method has great potential for on-site food pathogenic bacterial detection.
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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 | 0.000 |
| 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 | 0.001 | 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".