Rolling circle amplification: a versatile tool for chemical biology, materials science and medicine
Bibliographic record
Abstract
Rolling circle amplification (RCA) is an isothermal enzymatic process where a short DNA or RNA primer is amplified to form a long single stranded DNA or RNA using a circular DNA template and special DNA or RNA polymerases. The RCA product is a concatemer containing tens to hundreds of tandem repeats that are complementary to the circular template. The power, simplicity, and versatility of the DNA amplification technique have made it an attractive tool for biomedical research and nanobiotechnology. Traditionally, RCA has been used to develop sensitive diagnostic methods for a variety of targets including nucleic acids (DNA, RNA), small molecules, proteins, and cells. RCA has also attracted significant attention in the field of nanotechnology and nanobiotechnology. The RCA-produced long, single-stranded DNA with repeating units has been used as template for the periodic assembly of nanospecies. Moreover, since RCA products can be tailor-designed by manipulating the circular template, RCA has been employed to generate complex DNA nanostructures such as DNA origami, nanotubes, nanoribbons and DNA based metamaterials. These functional RCA based nanotechnologies have been utilized for biodetection, drug delivery, designing bioelectronic circuits and bioseparation. In this review, we introduce the fundamental engineering principles used to design RCA nanotechnologies, discuss recently developed RCA-based diagnostics and bioanalytical tools, and summarize the use of RCA to construct multivalent molecular scaffolds and nanostructures for applications in biology, diagnostics and therapeutics.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".