Plasmonic Biosensors for Health Monitoring: Inflammation Biomarker Detection
Bibliographic record
Abstract
Surface plasmon resonance (SPR) and localized SPR (LSPR) biosensors have emerged as viable technologies in the clinical detection of biomarkers for a wide array of health conditions. The success of SPR biosensors lies in their ability to monitor in real-time label-free biomarkers in complex biofluids. Recent breakthroughs in nanotechnology and surface chemistry have significantly improved this feature, notably from the incorporation of advanced nanomaterials including gold nanoparticles, graphene, and carbon nanotubes providing better SPR sensor performance in terms of detection limits, stability, and specificity. Recent progress in microfluidic integration has enabled SPR biosensors to detect multiple biomarkers simultaneously in complex biological samples. Taken together, these advances are closing the gap for their use in clinical diagnostics and point-of-care (POC) applications. While broadly applicable, the latest advancements in plasmonic biosensing are overviewed using inflammation biomarkers C-reactive protein (CRP), interleukins (ILs), tumor necrosis factor-α (TNF-α), procalcitonin (PCT), ferritin, and fibrinogen for a series of conditions, including cardiovascular diseases, autoimmune disorders, infections, and sepsis, as a key example of plasmonic biosensors for clinical applications. We highlight developments in sensor design, nanomaterial integration, surface functionalization, and multiplexing and provide a look forward to clinical applications by assessing the current limitations and exploring future directions for translating SPR biosensors for diagnostics and health monitoring. By enhancement of diagnostic accuracy, reproducibility, and accessibility, particularly in POC settings, SPR biosensors have the potential to significantly contribute to personalized healthcare and bring real-time, high-precision diagnostics to the forefront of clinical practice.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".