Cytokeratins Biosensing Using Tilted Fiber Gratings
Bibliographic record
Abstract
Optical fiber gratings have widely proven their applicability in biosensing, especially when they are coupled with antibodies for specific antigen recognition. While this is customarily done with fibers coated by a thin metal film to benefit from plasmonic enhancement, in this paper, we propose to study their intrinsic properties, developing a label-free sensor for the detection of biomarkers in real-time without metal coatings for surface plasmon resonances. We focus on the inner properties of our modal sensor by immobilizing receptors directly on the silica surface, and reporting the sensitivity of bare tilted fiber Bragg gratings (TFBGs) used at near infrared wavelengths. We test different strategies to build our sensing surface against cytokeratins and show that the most reliable functionalization method is the electrostatic adsorption of antibodies on the fiber, allowing a limit of detection reaching 14 pM by following the guided cladding modes near the cut-off area. These results present the biodetection performance that TFBGs bring through their modal properties for different functionalizations and data processing strategies.
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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 | 0.000 | 0.000 |
| 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 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".