A highly sensitive and simply operated protease sensor toward point-of-care testing
Bibliographic record
Abstract
Protease sensors for point-of-care testing (POCT) require simple operation, a detection period of less than 20 minutes, and a detection limit of less than 1 ng mL(-1). However, it is difficult to meet these requirements with protease sensors that are based on proteolytic cleavage. This paper reports a highly reproducible protease sensor that allows the sensitive and simple electrochemical detection of the botulinum neurotoxin type E light chain (BoNT/E-LC), which is obtained using (i) low nonspecific adsorption, (ii) high signal-to-background ratio, and (iii) one-step solution treatment. The BoNT/E-LC detection is based on two-step proteolytic cleavage using BoNT/E-LC (endopeptidase) and l-leucine-aminopeptidase (LAP, exopeptidase). Indium-tin oxide (ITO) electrodes are modified partially with reduced graphene oxide (rGO) to increase their electrocatalytic activities. Avidin is then adsorbed on the electrodes to minimize the nonspecific adsorption of proteases. Low nonspecific adsorption allows a highly reproducible sensor response. Electrochemical-chemical (EC) redox cycling involving p-aminophenol (AP) and dithiothreitol (DTT) is performed to obtain a high signal-to-background ratio. After adding a C-terminally AP-labeled oligopeptide, DTT, and LAP simultaneously to a sample solution, no further treatment of the solution is necessary during detection. The detection limits of BoNT/E-LC in phosphate-buffered saline are 0.1 ng mL(-1) for an incubation period of 15 min and 5 fg mL(-1) for an incubation period of 4 h. The detection limit in commercial bottled water is 1 ng mL(-1) for an incubation period of 15 min. The developed sensor is selective to BoNT/E-LC among the four types of BoNTs tested. These results indicate that the protease sensor meets the requirements for POCT.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".