Digital DNA detection based on a compact optofluidic laser with ultra-low sample consumption
Bibliographic record
Abstract
DNA lasers self-amplify optical signals from a DNA analyte as well as thermodynamic differences between sequences, allowing quasi-digital DNA detection. However, these systems have drawbacks, such as relatively large sample consumption and complicated dye labelling. Moreover, although the lasing signal can detect the target DNA, it is superimposed on an unintended fluorescence background, which persists for non-target DNA samples as well. From an optical point of view, it is thus not truly digital detection and requires spectral analysis to identify the target. In this work, we propose and demonstrate an optofluidic laser that has a single layer of DNA molecules as the gain material. A target DNA produces intensive laser emission comparable to existing DNA lasers, while any unnecessary fluorescence background is successfully suppressed. As a result, the target DNA can be detected with a single laser pulse, in a truly digital manner. Since the DNA molecules cover only a single layer on the surface of the laser microcavity, the DNA sample consumption is a few orders of magnitude lower than that of existing DNA lasers. Furthermore, the DNA molecules are stained by simply immersing the microcavity in the intercalating dye solution, and thus the proposed DNA laser is free of any complex dye-labelling process prior to analysis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".