Development of nanohole-based sensors for early detection of ovarian cancer
Bibliographic record
Abstract
Ovarian cancer has very high mortality because it is hard to diagnosis in early stages. Many ovarian cancer biomarkers (such as HE4, CA 125) are available and had been suggested as potential tools for early cancer detection. However, early cancer detection using serological markers will only become widely used if a new generation of sensors that can be handled in a clinical setting can be developed. A detection technology that is promising for miniaturization and integration in biomedical sensing devices is based on the phenomenon of the extraordinary light transmission (EOT) through arrays of nanoholes on metal films. EOT is an increase in light transmission observed at certain wavelengths that satisfy the surface plasmon resonance (SPR) condition of the nanostructure. The position of this resonance is affected by surface adsorption phenomenon, which is the basis for the biosensor. In this dissertation, the detection of the HE4 biomarker was demonstrated using EOT. The EOT-based detection was compared to two state-of-the-art analytical methods (ELISA and commercial SPR). Based on our experiments, it was found that ELISA has lowest detection limit, around 0.5 ng/mL for that particular protein (HE4). The detection limits for the commercial SPR, around 0.13 μg/mL was comparable to the nanohole-based detection limit, around 1.76 μg/mL. In contrast to ELISA, the SPR-based methods were label free, more time efficient, and more environmental friendly. An extra advantage of the nanohole scheme was that multiple samples could be analyzed simultaneously and in real time. Adsorption kinetic experiments were also performed to evaluate the rate constants of the HE4 binding to a surface coated with anti-HE4. The adsorption equilibrium constant for the HE4 – anti-HE4 system was determined to be (4.3 ± 2.1) x 107 M-1.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".