A purification-free nucleic acid amplification platform for diverse samples
Bibliographic record
Abstract
Abstract An enzyme-free nucleic acid amplification method based on toehold-mediated strand displacement reaction (TMSDR) was evaluated under a variety of conditions with the aim of eliminating conventional purification steps and streamlining diagnostic workflows. By operating directly in lysis buffers, the TMSDR assay enhances target recovery and confers protection against nuclease degradation. Amplification performance was examined in the presence of diverse denaturing chemicals, lysis buffers, and sample matrices—including blood, saliva, wastewater, and soil—and the results demonstrated broad versatility and robust amplification under most conditions. The assay-maintained efficacy even in the presence of common PCR inhibitors, such as polyphenols in plant extracts, immunoglobulin G in blood, and complex constituents in environmental samples. Furthermore, a proof-of-concept assay targeting the 16S rRNA of Escherichia coli DH5α was established using specifically designed probe and displacer sequences, with specificity confirmed by the absence of amplification in mutated target controls. Collectively, these findings underscore the potential of the TMSDR assay as a highly adaptable and efficient alternative for rapid nucleic acid detection in point-of-care and field applications. This enzyme-free, purification-independent platform opens the door for rapid diagnostic development in resource-limited settings
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".