A System-on-Board Integrated Multi-analyte PoC Biosensor for Combined Analysis of Saliva and Exhaled Breath
Bibliographic record
Abstract
The need for oral health monitoring Point of Care (PoC) systems is ever growing. This is effectively highlighted by the ongoing COVID-19 pandemic where the lack of rapid PoC testing has placed an unsustainable burden on centralized laboratory testing. Urgent development has furthered pathogenic nucleic acid and antibody detection in oral samples throat swabs, but without corresponding advancements in biochemical monitoring through oral biosensing. We have recently reported two novel biosensor technologies for detection of high impact hormones: cortisol in saliva by organic electrolyte gated FETs (OEGFETs), and 8-isoprostane in exhaled breath condensate (EBC) using molecularly imprinted electroimpedance spectroscopy biosensors (MIP EIS). In this work, we report a first stage integration of the two biosensors - previously bench-top proven - with a miniaturized semi-hermetically sealed soft-fluidic enclosure, onto a low-power (<300 mW) customized printed circuit board. Our findings established comparable detection thresholds for the miniaturized board-based configuration and a lab-based test setup, and their ability to characterize, calibrate, and operate these small footprint biosensors. Testing with the 8-isoprostane EBC MIP EIS biosensors showed the system-on-board had an effective frequency range of 100-100kHz, comparable to lab bench impedance analyzers. Despite internal impedance increases of 210%, the expected data features are present in the impedance graphs collected with the PCB. The system-on-board experiments using OEGFET aptasensor showed a predictable behavior and comparable sensor detection range and resolution using unadulterated supernatant and serial dilutions of cortisol over a range of 273 μM to 2.73pM. The portable, multi-analyte oral biosensor is a promising prototype for future packaging and clinical validation.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".