Surface Plasmon Resonance Clinical Biosensors for Medical Diagnostics
Bibliographic record
Abstract
The design and application of sensors for monitoring biomolecules in clinical samples is a common goal of the sensing research community. Surface plasmon resonance (SPR) and other plasmonic techniques such as localized surface plasmon resonance (LSPR) and imaging SPR are reaching a maturity level sufficient for their application in monitoring biomolecules in clinical samples. In recent years, the first examples for monitoring antibodies, proteins, enzymes, drugs, small molecules, peptides, and nucleic acids in biofluids collected from patients afflicted with a series of medical conditions (Alzheimer's, hepatitis, diabetes, leukemia, and cancers such as prostate and breast cancers, among others) demonstrate the progress of SPR sensing in clinical chemistry. This Perspective reviews the current status of the field, showcasing a series of early successes in the application of SPR for clinical analysis and detailing a series of considerations regarding sensing schemes, exposing issues with analysis in biofluids, and comparing SPR with ELISA, while providing an outlook of the challenges currently associated with plasmonic materials, instrumentation, microfluidics, bioreceptor selection, selection of a clinical market, and validation of a clinical assay for applying SPR sensors to clinical samples. Research opportunities are proposed to further advance the field and transition SPR biosensors from research proof-of-concept stage to actual clinical applications.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.009 |
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".