Rapid on-chip postcolumn labeling and high-resolution separations of DNA
Bibliographic record
Abstract
When performing genetic analysis on microfluidic systems, labeling the sample DNA for detection is a critical preparation step. Labeling procedures often involve fluorescently tagged primers and PCRs, which lengthen experimental run times and introduce higher levels of complexity, increasing the overall cost per analysis. Alternatively, on-chip labeling techniques based on intercalating dyes permit rapid labeling of DNA fragments. However, as noted in the literature, the stochastic nature of dye-DNA complex formation hinders the native electrophoretic migration of DNA fragments, degrading the separation resolution. In this study, we present a novel method of controllably labeling DNA fragments at the end of the electrophoretic separation channel in a glass microfluidic chip. Permitting the DNA to separate and labeling just before detection, achieves the rapid labeling associated with intercalators while maintaining the high resolution of native DNA separations. Our analyses are completed in minutes, rather than the hours typical of sample prelabeling. We demonstrate an electrophoretic microchip-based intercalator labeling technique that achieves higher resolution performance than reported in the literature to date.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".