A Surface Plasmon Resonance Based Inhibition Immunoassay for Sensitive and Selective Detection of 17β-Estradiol
Bibliographic record
Abstract
Quantitative measurement of small-molecule metabolites is now emerging as an effective way to link the metabolite profile to disease state. Surface plasmon resonance (SPR) is a sensing platform that has demonstrated applicability for a large range of biomolecules. However, direct detection of small molecules with SPR challenges the refractive index based detection mechanism. Herein, we utilized an indirect detection format and developed an inhibition immunoassay for the quantitative measurement of 17β-estradiol (E2) using SPR. One competitor, BSA-E2 conjugate, was immobilized to the SPR chip via the reaction between the primary amino group of the conjugate and the succinimide group (NHS) introduced by the formation of a thiol-NHS monolayer on gold surface. Free E2 molecules compete with BSA-E2 on chip surface for binding sites provided by a monoclonal anti-E2 antibody. It was found the binding affinity of the antibody to BSA-E2 conjugate increases with decreasing surface coverage of BSA-E2 conjugate. Under optimal conditions, a sigmoidal calibration curve with a negative slope and a dynamic range from 10 pM to 2 nM was generated. The detection limit of the immunoassay is estimated to be 0.3 pM. Moreover, the immunoassay exhibits high specificity for E2 detection using estrone (E1) as a potential interference.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".