Real-time isothermal DNA amplification monitoring in picoliter volumes using an optical fiber sensor
Bibliographic record
Abstract
Rolling circle amplification (RCA) of DNA can be considered as a great alternative to the gold standard polymerase chain reaction (PCR), especially during this pandemic period, where rapid, sensitive, and reliable test results for hundreds of thousands of samples are required daily. This work presents the first research to date on direct, real-time and label-free isothermal DNA amplification monitoring using a microcavity in-line Mach-Zehnder interferometer (μIMZI) fabricated in an optical fiber. The solution based on μIMZI offers a great advantage over many other sensing concepts - making possible optical analysis in just picoliter sample volumes. The selectivity of the biosensor is determined by DNA primers immobilized on the microcavity's surface that act as selective biorecognition elements and trigger initiation of the DNA amplification process. In this study, we verified the sensing concept using circular DNA designed to target the H5N1 influenza virus. The developed biosensor exhibits an ultrahigh refractive index sensitivity reaching 14 000 nm per refractive index unit and a linear detection range between 9.4 aM and 94 pM of the target DNA sequence. Within a 30 min period, the amplification of as little as 9.4 aM DNA can be effectively detected, with a calculated limit of detection of as low as 0.2 aM DNA, suggesting that this methodology holds great promise in practical disease diagnosis applications in the future.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".