Detection of Sub-Nanomolar Concentration of Trypsin by Thicken-Shear Mode (TSM) Acoustic Wave Biosensor
Bibliographic record
Abstract
The determination of protease activity is very important for disease diagnosis, drug development, and quality and safety assurance for dairy products. Therefore, the development of low-cost methods for assessing protease activity is critical. Here, we demonstrate that an acoustic wave-based biosensor operated in the thickness-shear mode (TSM) enables the low-cost detection of protease activity in real time. The TSM sensor was based on a protein substrate (PS) β-casein immobilized on a piezoelectric quartz crystal electrode. The β-casein layer was immobilized onto a gold surface by a carboxylate terminated self-assembled monolayer (SAM) of 11-mercaptoundecanoic acid (MUA). The carboxylic acid terminal was activated by the reaction of a mixture of water- soluble N-(3-Dimethylaminopropyl)-N0-ethylcarbodiimide (EDC) and N–Hydroxysuccinimide (NHS) on the electrode surfaces. We demonstrated that β-casein can form a stable assembly on a piezoelectric quartz crystal electrode. After an enzymatic reaction with trypsin, it cleaved the surface-bound β-casein substrate, which increased the frequency of the crystal in a sigmoidal manner. Trypsin was detected in the range of concentrations from 1 to 50 nM. The limit of detection was 0.2 nM. Initial reaction rates measured at different enzyme concentrations have been used to construct a calibration curve. Considering the results obtained, we believe that the TSM biosensor is a useful tool for protease analysis.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".