Strategies for the Voltammetric Detection of Loop-Mediated Isothermal Amplification
Bibliographic record
Abstract
Loop-mediated isothermal amplification (LAMP) is rapidly developing into an important tool for the point-of-use detection of pathogens for both clinical and environmental samples, largely due to its sensitivity, rapidity, and adaptability to portable devices. Many methods are used to monitor LAMP, but not all are amenable to point-of-use applications. Common methods such as fluorescence often require bulky equipment, whereas colorimetric and turbidimetric methods can lack sensitivity. Electrochemical biosensors are becoming increasingly important for these applications due to their potential for low cost, high sensitivity, and capacity for miniaturization into integrated devices. This review provides an overview of the use of voltammetric sensors for monitoring LAMP, with a specific focus on how electroactive species are used to interface between the biochemical products of the LAMP reaction and the voltammetric sensor. Various strategies for the voltammetric detection of DNA amplicons as well as pyrophosphate and protons released during LAMP are presented, ranging from direct DNA binding by electroactive species to the creative use of pyrophosphate-detecting aptamers and pH-sensitive oligonucleotide structures. Hurdles for adapting these devices to point-of-use applications are also discussed.
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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 | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".