Exponential Isothermal Amplification of Nucleic Acids and Assays for Proteins, Cells, Small Molecules, and Enzyme Activities: An EXPAR Example
Bibliographic record
Abstract
Isothermal exponential amplification techniques, such as strand-displacement amplification (SDA), rolling circle amplification (RCA), loop-mediated isothermal amplification (LAMP), nucleic acid sequence based amplification (NASBA), helicase-dependent amplification (HDA), and recombinase polymerase amplification (RPA), have great potential for on-site, point-of-care, and in situ assay applications. These amplification techniques eliminate the need for temperature cycling, as required for the polymerase chain reaction (PCR), while achieving comparable amplification yields. We highlight here recent advances in the exponential amplification reaction (EXPAR) for the detection of nucleic acids, proteins, enzyme activities, cells, and metal ions. The incorporation of fluorescence, colorimetric, chemiluminescence, Raman, and electrochemical approaches enables the highly sensitive detection of a variety of targets. Remaining issues, such as undesirable background amplification resulting from nonspecific template interactions, must be addressed to further improve isothermal and exponential amplification techniques.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.004 |
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".