Simple Enzymatic Means to Neutralize DNA Contamination in Nucleic Acid Amplification
Bibliographic record
Abstract
Reverse transcription PCR (RT-PCR) is prone to false positives when contaminating DNA molecules are present at the start of a reaction. Contaminants that derive from earlier work using a given primer pair (carryover PCR products) are of particular concern when those primers are used routinely, as in clinical diagnostics or environmental monitoring. In addition, contamination by genomic DNA can significantly interfere with quantitative and qualitative analysis of RNAs by RT-PCR. Here we describe contaminant restriction (ConR), a method that can be used to neutralize carryover and genomic DNA contamination in RT-PCR studies. Restriction enzymes (REs) added to the amplification cocktail cleave contaminant DNA molecules while sparing the intended target nucleic acid. Restriction, reverse transcription, and amplification steps all take place in the same sealed vessel, thus avoiding any danger of recontamination. ConR eliminates carryover contamination in PCR without compromising target sequence amplification. Because the method is effective against both genomic and carryover contamination, it can be employed routinely in one-step RT-PCR, whatever the RNA target or the nature of the potential DNA contaminant. A variation of this decontamination method, amplicon primer site restriction (APSR), is effective specifically against carryover contamination. APSR, unlike ConR, can be applied during PCR-based amplification of DNA target molecules.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.007 |
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".