Drug-Based Gold Nanoparticles Overgrowth for Enhanced SPR Biosensing of Doxycycline
Bibliographic record
Abstract
In clinical chemistry, frequent monitoring of drug levels in patients has gained considerable importance because of the benefits of drug monitoring on human health, such as the avoidance of high risk of over dosage or increased therapeutic efficacy. In this work, we demonstrate that the drug doxycycline can act as an Au nanoparticle (doxy-AuNP) growth and capping agent to enhance the response of a surface plasmon resonance (SPR) biosensor for this drug. SPR analysis revealed the high sensitivity of doxy-AuNPs towards the detection of free doxycycline. More specifically, doxy-AuNPs bound with protease-activated receptor-1 (PAR-1) immobilized on the SPR sensing surface yield the response in SPR, which was enhanced following the addition of free doxy (analyte) to the solution of doxy-AuNPs. This biosensor allowed for doxycycline detection at concentrations as low as 7 pM. The study also examined the role of colloidal stability and growth of doxy-AuNPs in relation to the response-enhancement strategy based on doxy-AuNPs. Thus, the doxy-AuNPs-based SPR biosensor is an excellent platform for the detection of doxycycline and demonstrates a new biosensing scheme where the analyte can provide enhancement.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".