HER2 biosensing through SPR-envelope tracking in plasmonic optical fiber gratings
Bibliographic record
Abstract
In the biomedical detection context, plasmonic tilted fiber Bragg gratings (TFBGs) have been demonstrated to be a very accurate and sensitive sensing tool, especially well-adapted for biochemical detection. In this work, we have developed an aptasensor following a triple strategy to improve the overall sensing performances and robustness. Single polarization fiber (SPF) is used as biosensor substrate while the demodulation is based on tracking a peculiar feature of the lower envelope of the cladding mode resonances spectrum. This method is highly sensitive and yields wavelength shifts several tens of times higher than the ones reported so far based on the tracking of individual modes of the spectrum. An amplification of the response is further performed through a sandwich assay by the use of specific antibodies. These improvements have been achieved on a biosensor developed for the detection of the HER2 (Human Epidermal Growth Factor Receptor-2) protein, a relevant breast cancer biomarker. These advanced developments can be very interesting for point-of-care biomedical measurements in a convenient practical way.
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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".