Synergistic Inhibition of Nonspecific Binding for Accurate Detection of <i>Escherichia coli</i> O157:H7 and Multilevel Signal Discrimination
Bibliographic record
Abstract
Pathogenic Escherichia coli ( E. coli ), particularly E. coli O157:H7, is a major foodborne pathogen with significant clinical relevance, necessitating accurate and rapid subtype identification. However, the high genetic variability and biological similarity among E. coli strains pose challenges for conventional signal-strain detection methods, often resulting in false-positive outcomes. In this study, we developed a novel biosensing strategy based on plasmonic nanostructures functionalized with heterogeneous recognition elements that target two distinct epitopes of E. coli O157:H7. The sensor incorporates biological silent Raman tags for ratiometric signal output and magnetic enrichment to improve selectivity and minimize interference from nontarget bacteria. This design ensures excellent reproducibility and operational stability. The biosensor demonstrated an impressive limit of detection (LOD) of 1.2 CFU/mL, outperforming most existing methods. Furthermore, a cutoff value of 0.32 for the signal ratio yielded a positive predictive value of 98% and a negative predictive value of 94%, demonstrating a clear signal boundary and high accuracy for various types of signals. These results highlight the potential of our plasmonic biosensor as a rapid, ultrasensitive, and reliable point-of-care diagnostic tool for pathogen detection in complex food matrices.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".