In Situ Biosensing with a Surface Plasmon Resonance Fiber Grating Aptasensor
Bibliographic record
Abstract
Surface plasmon resonance (SPR) biosensors prepared using optical fibers can be used as a cost-effective and relatively simple-to-implement alternative to well established biosensor platforms for monitoring biomolecular interactions in situ or possibly in vivo. The fiber biosensor presented in this study utilizes an in-fiber tilted Bragg grating to excite the SPR on the surface of the sensor over a large range of external medium refractive indices, with minimal cross-sensitivity to temperature and without compromising the structural integrity of the fiber. The label-free biorecognition scheme used demonstrates that the sensor relies on the functionalization of the gold-coated fiber with aptamers, synthetic DNA sequences that bind with high specificity to a given target. In addition to monitoring the functionalization of the fiber by the aptamers in real-time, the results also show how the fiber biosensor can detect the presence of the aptamer's target, in various concentrations of thrombin in buffer and serum solutions. The findings also show how the SPR biosensor can be used to evaluate the dissociation constant (K(d)), as the binding constant agrees with values already reported in the literature.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".