Aptamer-Based Electrochemical Biosensors for the Detection of Small Molecules and Plasma Proteins
Bibliographic record
Abstract
The ability to detect the presence of certain molecular analytes inside the human body is vital to the ability of a medical professional to assess a patient’s health. The use of sensitive and selective biosensors would be a tremendous asset to the field of medical screening and diagnosis. The fabrication of aptamer-based electrochemical biosensors as an emerging technology has made the detection of both small and macromolecular analytes easier, faster, and more suited for the ongoing transition from fundamental analytical science to the early detection of diseases. Aptamers are synthetic oligonucleotides that have undergone iterative rounds of in vitro selection for binding with high affinity to specific analytes of choice; a sensitive yet simple method to utilize aptamers as recognition entities for the development of biosensors is to transduce the signal electrochemically. In this chapter, we will summarize the state-of-the-art research progresses on aptamer-based electrochemical biosensors for the detection of both plasma proteins and small molecules. The targeted portable electronic devices, as they can be operated by the patients during their day-to-day lives, have the potential to revolutionize the delivery of medical treatment.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.013 |
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".