Research Progress on Rolling Circle Amplification (RCA)-Based Biomedical Sensing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Enhancing the limit of detection (LOD) is significant for crucial diseases. Cancer development could take more than 10 years, from one mutant cell to a visible tumor. Early diagnosis facilitates more effective treatment and leads to higher survival rate for cancer patients. Rolling circle amplification (RCA) is a simple and efficient isothermal enzymatic process that utilizes nuclease to generate long single stranded DNA (ssDNA) or RNA. The functional nucleic acid unit (aptamer, DNAzyme) could be replicated hundreds of times in a short period, and a lower LOD could be achieved if those units are combined with an enzymatic reaction, Surface Plasmon Resonance, electrochemical, or fluorescence detection, and other different kinds of biosensor. Multifarious RCA-based platforms have been developed to detect a variety of targets including DNA, RNA, SNP, proteins, pathogens, cytokines, micromolecules, and diseased cells. In this review, improvements in using the RCA technique for medical biosensors and biomedical applications were summarized and future trends in related research fields described.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it