Immobilization of Fluorescent Aptamer Biosensors on Magnetic Microparticles and Its Potential Application for Ocean Sensing
Bibliographic record
Abstract
Many important analytes are present in the ocean water and primary examples include various marine toxins. The unique marine environment possesses an extremely high ionic strength, posing a significant analytical challenge for biosensor design. Protein-based enzymes and antibodies are likely to denature under such non-physiological conditions. Aptamers are nucleic acid-based binding molecules that can be obtained using a combinatorial in vitro selection technique. Since such selections are carried out in the absence of living cells, it is possible to obtain aptamers that work optimally under high salt conditions. Similarly selections in low pH and high temperatures have already been carried out. The high salt concentration in marine samples may also cause significant fluorescence quenching, reducing the sensitivity of fluorescent aptamer sensors. We propose that this problem may be solved by immobilization of aptamer-based biosensors on magnetic microparticles, allowing spatial separation of the target binding and the fluorescence detection steps. In this chapter, we describe a protocol for the detection of adenosine and ATP in high salt buffers and in human blood serum. Compared to the non-immobilized sensor, more consistent results with reduced interference were achieved after immobilization. Future research directions of using such immobilized sensors for marine detection 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".