Transition Metal Dichalcogenide Nanosheets for Visual Monitoring PCR Rivaling a Real-Time PCR Instrument
Bibliographic record
Abstract
Monitoring the progress of polymerase chain reactions (PCRs) is of critical importance in bioanalytical chemistry and molecular biology. Although real-time PCR thermocyclers are ideal for this purpose, their high cost has limited their applications in resource-poor areas. Direct visual detection would be a more attractive alternative. To monitor the PCR amplification, DNA-staining dyes, such as SYBR Green I (SG), are often used. Although these dyes give higher fluorescence when binding to double-stranded DNA products, they also yield strong background fluorescence in the presence of a high concentration of single-stranded (ss) DNA primers. In this work, we screened various nanomaterials and found that graphene oxide (GO), reduced GO, molybdenum disulfide (MoS 2 ), and tungsten disulfide (WS 2 ) can quench the fluorescence of nonamplified negative samples while still retaining strong fluorescence of positive ones. The signal ratio of positive-over-negative samples was enhanced by around 50-fold in the presence of these materials. In particular, MoS 2 and WS 2 nearly fully retained the fluorescence of the positive samples. The mechanism for MoS 2 and WS 2 to enhance PCR signaling is attributed to the adsorption of both the ssDNA PCR primers and SG with an appropriate strength. MoS 2 can also suppress nonspecific amplification caused by excess polymerase. Finally, this method was used to detect extracted transgenic soya GTS 40-3-2 DNA after PCR amplification. Compared with the samples without nanomaterials, the addition of MoS 2 could better distinguish the concentration difference of the template DNA, and the sensitivity of visual detection rivaled that from a real-time PCR instrument.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".