Surface‐Based Multimeric Aptamer Generation and Bio‐Functionalization for Electrochemical Biosensing Applications
Bibliographic record
Abstract
Abstract Multimeric aptamers have gained more attention than their monomeric counterparts due to providing more binding sites for target analytes, leading to increased affinity. This work attempted to engineer the surface‐based generation of multimeric aptamers by employing the room temperature rolling circle amplification (RCA) technique and chemically modified primers for developing a highly sensitive and selective electrochemical aptasensor. The multimeric aptamers, generated through surface RCA, are hybridized to modified spacer primers, facilitating the positioning of the aptamers in the proximity of sensing surfaces. These multimeric aptamers can be used as bio‐receptors for capturing specific targets. The surface amplification process was fully characterized, and the optimal amplification time for biosensing purposes was determined, using SARS‐CoV‐2 spike protein (SP). Interestingly, multimeric aptasensors produced considerably higher response signals and affinity (more than 10‐fold), as well as higher sensitivity (almost 4‐fold) compared to monomeric aptasensors. Furthermore, the impact of surface structures on the response signals was studied by utilizing both flat working electrodes (WEs) and nano‐/microislands (NMIs) WEs. The NMIs multimeric aptasensors showed significantly higher sensitivity in buffer and saliva media with the limit of detection less than 2 fg/ml. Finally, the developed NMIs multimeric aptasensors were clinically challenged with several saliva patient samples.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".