Reduction of Background Generated from Template-Template Hybridizations in the Exponential Amplification Reaction
Bibliographic record
Abstract
The Exponential Amplification Reaction (EXPAR) enables isothermal amplification of nucleic acids. However, applications of EXPAR for the amplification of trace amounts of nucleic acids are hindered by high background. The mechanism of background generation is currently not well understood, although it is assumed to involve nonspecific extension of EXPAR templates by DNA polymerase. We present here a study of the mechanisms of triggering EXPAR background amplification. We show that interactions of EXPAR templates lead to background amplification via polymerase extension of the templates. We further designed and tested two strategies to minimize background amplification: blocking of the 3'-end of the template and sequence-independent weakening of the template-template interactions. Sequence-specific 3'-end blocking showed reduced background, suggesting that 3'-end template interactions are a contributing factor to background amplification. Sequence-independent binding of the whole EXPAR template substantially reduced background amplification by competing with template-template interactions along the entire template sequence. This study provided evidence that nonspecific template interactions and extension by DNA polymerase triggered the amplification of background in EXPAR. The addition of single stranded binding protein to bind nonspecifically with the EXPAR template decreased background by 3 orders of magnitude.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".