Immobilized Reporter Phage on Electrospun Polymer Fibers for Improved Capture and Detection of <i>Escherichia coli</i> O157:H7
Bibliographic record
Abstract
Escherichia coli O157:H7 is a foodborne Shiga-toxin-producing bacterium that leads to millions of cases of illness every year. The development of a portable biosensor that rapidly detects this pathogen can greatly reduce both the time required to identify the source of contamination within the food production chain and the scale of outbreaks. Here we propose a swab for the detection of E. coli O157:H7 made from electrospun poly-3-hydroxybutyrate (PHB) on which the reporter bacteriophage (phage), PP01—engineered to express the bioluminescent protein Nanoluc upon infection of the pathogen—has been immobilized. In the development of the material, the properties of phage-immobilized electrospun porous, nonwoven PHB mats were compared to those of phage-immobilized flat PHB films produced by solvent casting. Dynamics of E. coli infections initiated with the phage-immobilized materials showed the electrospun mats had the highest infectivity, indicative of higher phage surface density (confirmed by microscopy) and activity—two important properties for the sensitivity and response time of biosensors. In sample tests, successful detection of E. coli O157:H7 cells in both milk and Tryptic Soy Broth at concentrations of 10 5 CFU/mL within 1 h and 10 1 CFU/mL within 3 h demonstrates the promising potential of the system as a food safety assessment tool.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".