Rolling circle amplification and its application in microfluidic systems for <i>Escherichia coli</i> O157:H7 detections
Bibliographic record
Abstract
Abstract Rolling circle amplification (RCA) has been widely used to enhance detection signals as its long single‐stranded RCA products can provide multiple binding sites for signal probes for sensitive detections. In the current study, we employ atomic force microscopy (AFM) to monitor the RCA products during the course of the RCA process over time. Subsequently, the results of the RCA obtained from the AFM study are combined with those from the conventional electrophoresis method to optimize RCA reactions for rapid and sensitive detection. We show that there appears to be an inhomogeneous RCA initiation phase in early to mid‐stage of the RCA reaction where some chains grow faster while others grow slower or remain dormant, an observation that has not been reported in the literature. Furthermore, we demonstrate that the RCA can significantly enhance detection signals by up to 100‐fold. We also show that the Escherichia coli O157:H7 detection with the RCA can be carried out in different food matrices with excellent detection sensitivities and specificities. In conclusion, these results suggest that a microfluidic device in combination with RCA signal enhancement is a simple and robust approach to sensitive whole‐cell detection in food samples. Practical applications The current research has exciting potentials for applications in sensitive detections of food samples for food safety inspections.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".