(Invited) Plasmonic and Surface Plasmon Electrochemiluminescence Sensors for Detecting Biomarkers and Drugs
Bibliographic record
Abstract
In this presentation, a series of different plasmonic sensors for the detection of cancer biomarkers, cancer therapeutic drugs and the response of patients to oncological biological drugs will be presented. The plasmonic sensors are based on a portable 4-channel SPR instrument and surface chemistry developed to present fouling of the sensors in crude serum or plasma. Sensors for detecting methotrexate and for the immune response induced by asparaginase in leukemia treatments directly in sera of patients during ongoing therapies will be presented. Our work towards the detection of HER-2 positive breast cancers will also be presented, with a comparison of commercial and point of care SPR instruments. Our initial work on the development of a SPR-electrochemiluminescence instrument will be presented. We recently revealed that performing SPR and electrochemiluminescence on a single instrument provides unprecedented information about the interfacial processes occurring on the electrode. In addition, studying the energy transfer between the ECL and the plasmon revealed that the optically excited plasmon reduced the ECL intensity in the far-field by about 40% due to a lower plasmon mediated luminescence process. Hence, the combination of SPR and ECL is highly advantageous to study electrochemical processes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.010 |
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".