De-Cortisensor: A Portable and Highly Sensitive Direct Enzyme-Based Cortisol Detection Platform
Bibliographic record
Abstract
We present De-Cortisensor, a portable, cost-effective, and wireless electrochemical sensing platform for direct, non-invasive cortisol detection in saliva. The system integrates a custom-designed three-electrode printed circuit board sensor with an SIC4341 near-field communication (NFC)-enabled potentiostat, enabling real-time data transmission to smartphones via the mobile app, Chemister. The detection of cortisol is achieved through a non-ELISA-based direct enzymatic oxidation catalyzed by immobilized 11β-Hydroxysteroid Dehydrogenase type 2 (11β-HSD2), an enzyme uniquely selected for its unidirectional and highly specific conversion of cortisol to inactive cortisone. This facilitates faster and direct electron transfer (DET) to the electrode, eliminating the need for mediators or antibodies, as is typically required in most enzymatic cortisol biosensors. The sensor exhibited a strong linear correlation between oxidative current and cortisol concentrations, ranging from 0 to 100 pM, and with a femto level limit of detection (LOD) of 0.1 fM (0.0362 fgmL-1). It is found to be highly specific to cortisol, displaying negligible signal interference from other sweat biomolecules, such as glucose and lactic acid, as well as similar small molecules like corticosterone and testosterone. Furthermore, the platform effectively detected the cortisol in contrived pooled human saliva using a simple two-step process within 2 minutes, without requiring any sample preprocessing, resulting in a LOD of 1 fM. By combining DET-based enzymatic sensing, low-cost PCB electrodes (USD <$1 per PCB sensor), and an NFC potentiostat, the De-Cortisensor provides a simpler, faster, and highly sensitive solution for real-time, non-invasive cortisol monitoring in saliva, with significant potential in digital health, wearable devices, and stress-tracking applications.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".