Unusual switchable peroxidase-mimicking nanozyme for the determination of proteolytic biomarker
Bibliographic record
Abstract
Detection of enzyme biomarkers originating from either bio-fluids or contaminating microorganisms is of utmost importance in clinical diagnostics and food safety. Herein, we present a simple, low-cost and easy-to-use sensing approach based on the switchable peroxidase-mimicking activity of plasmonic gold nanoparticles (AuNPs) that can catalyse for the oxidation of 3,3’,5’5-tetramethylbenzidine (TMB) for the determination of protease enzyme. The AuNP surface is modified with casein, showing dual functionalities. The first function of the coating molecule is to suppress the intrinsic peroxidase-mimicking activity of AuNPs by up to 77.1%, due to surface shielding effects. Secondly, casein also functions as recognition sites for the enzyme biomarker. In the presence of protease, the enzyme binds to and catalyses the degradation of the coating layer on the AuNP surface, resulting in the recovery of peroxidase-mimicking activity. This is shown visually in the development of a blue colored product (oxidised TMB) or spectroscopically as an increase in absorbance at 370 and 650 nm. This mechanism allows for the detection of protease at 44 ng·mL−1 in 90 min. The nanosensor circumvents issues associated with current methods of detection in terms of ease of use, compatibility with point-of-care testing, low-cost production and short analysis time. The sensing approach has also been applied for the detection of protease spiked in ultra-heat treated (UHT) milk and synthetic human urine samples at a limit of detection of 490 and 176 ng·mL−1, respectively, showing great potential in clinical diagnostics, food safety and quality control.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".