Persistent DNA Contamination in Competitive RT-PCR Using cRNA Internal Standards: Identity, Quantity, and Control
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Accurate quantification of mRNA by competitive RT-PCR demands that the quality of the cRNA internal standard be strictly controlled and that at least two criteria should be satisfied. First, genomic DNA should be removed from the total RNA being analyzed; second, template DNA should be removed from the cRNA internal standard following in vitro transcription. We observed that the routine use of RNase-free DNase I is insufficient for removing template DNA from cRNA samples and can degrade cRNA. Furthermore, reducing the template DNA before digestion, selectively extracting template DNA, and gel fractionation are all ineffective at completely eliminating template DNA contamination in cRNA standards. A strategy was developed ("inverted" competitive RT-PCR) to quantify template DNA contamination in cRNA standards. Regardless of treatment method, a small percentage of DNA contamination remained in the products of in vitro transcription. Without correction, the number of mRNA copies calculated from competitive RT-PCR is systematically overestimated. The number of template DNAs contaminating the cRNA samples was remarkably large, though as a percentage of the total cRNA, DNA contamination was small and could be easily corrected.
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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 it