Colorimetric Nucleic Acid Detection on Paper Microchip Using Loop Mediated Isothermal Amplification and Crystal Violet Dye
Bibliographic record
Abstract
Nucleic acid detection is of paramount importance in monitoring of microbial pathogens in food safety and infectious disease diagnostic applications. To address these challenges, a rapid, cost-effective label-free technique for nucleic acid detection with minimal instrumentations is highly desired. Here, we present paper microchip to detect and quantify nucleic acid using colorimetric sensing modality. The extracted DNA from food samples of meat as well as microbial pathogens was amplified utilizing loop-mediated isothermal amplification (LAMP). LAMP amplicon was then detected and quantified on a paper microchip fabricated in a cellulose paper and a small wax chamber utilizing crystal violet dye. The affinity of crystal violet dye toward dsDNA and positive signal were identified by changing the color from colorless to purple. Using this method, detection of Sus scrofa (porcine) and Bacillus subtilis (bacteria) DNA was possible at concentrations as low as 1 pg/μL (3.43 × 10 –1 copies/μL) and 10 pg/μL (2.2 × 10 3 copies/μL), respectively. This strategy can be adapted for detection of other DNA samples, with potential for development of a new breed of simple and inexpensive paper microchip at the point-of-need.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".