SPR platform based on image acquisition for HER2 antigen detection
Bibliographic record
Abstract
Abstract HER2 antigen is a marker used for breast cancer diagnosis and prevention. Its determination has great importance since breast cancer is one of the most insidious types of cancer in women. HER2 antigen assessment in human serum is traditionally achieved by enzyme-linked immunosorbent assay (ELISA method), but it has some disadvantages, such as suppressing the thermodynamic–kinetic studies regarding the antibody–antigen interaction, and the use of labeled molecules that can promote false positive responses. Biosensors based on surface plasmon resonance (SPR) are sensitive optical techniques widely applied on bioassays. The plasmonic devices do not operate with labeled molecules, overcoming conventional immunoassay limitations, and enabling a direct detection of target analytes. In this way, a new SPR biosensor to assess HER2 antigen has been proposed, using nanohole arrays on a gold thin film by signal transduction of transmitted light measurements from array image acquisitions. These metallic nanostructures may couple the light directly on surface plasmons using a simple collinear arrangement. The proposed device reached an average sensitivity for refractive index (RI) variation on a metal surface of 4146 intensity units/RIU (RIU = RI units). The device feasibility on biomolecular assessment was evaluated. For this, 3 ng ml −1 known HER2 antigen concentration was efficiently flowed (using a microfluidic system) and detected from aqueous solutions. This outcome shows that the device may be a powerful apparatus for bioassays, particularly toward breast cancer diagnosis and prognosis.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".