System-on-Board Integrated Flexible OEGFET Aptasensor for Multianalyte Testing in Saliva
Bibliographic record
Abstract
The need for oral health monitoring point-of-care (PoC) systems is ever growing. We have recently reported a novel, aptamer-based flexible biosensor for detection of a high impact hormone—cortisol—in saliva samples using organic electrolyte-gated FET (OEGFET) technology. In this work, we are reporting a system-on-board (SoB) level integration of an improved flexible OEGFET aptasensor, which was previously reliant on a bench-top measurement setup. The reported flexible OEGFET aptasensor has integrated soft microfluidics and a low-power (< 300 mW) customized printed circuit board. The interfacing of flexible aptasensor to the circuit board was achieved using a low-temperature extrusion printing technique. The system was assessed using spiked saliva supernatants, which established comparable detection threshold for the miniaturized, board-based configuration. In this expanded article, we furthermore demonstrate improvements to device structure and the integration process, which improves the signal-to-noise resolution. By implementing 3-D printing technology, the interconnects between the flexible sensor and conventional printed circuit board (PCB) are more durable and possess better conductivity. The optimized transistor pattern allows for multiple analytes to be tested concurrently. A side-by-side analysis of a device that is specific to cortisol biomolecules and a nonspecific device shows that the SoB is capable of distinguishing between binding and nonbinding device behavior. The portable oral biosensor has the potential to be transformed into a multianalyte sensing platform and is therefore a promising prototype for future clinical validation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.000 |
| 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".