Molecularly Imprinted Electroimpedance Sensor for Detection of 8-Isoprostane in Exhaled Breath Condensate
Bibliographic record
Abstract
Soft lithographically patterned molecularly imprinted polymers (MIP) offer highly durable, low-cost fabricated biochemical sensors, with a highly selective biorecognition mechanism. In MIP architecture, nanocavity binding sites are imprinted onto a polymer thin film coated over an interdigitated electrode structure. As the target molecules affixed to the binding sites, the frequency-dependent electroimpedance is measured across the interdigitated electrode, and the response signal is analyzed to determine analyte concentration. This letter uses poly(vinyl alcohol), N-methyl-4 (4'-formylstyryl)pyridinium methosulfate acetal (PVA-SbQ) to create the desired MIP thin film structure for detecting specific physiologically relevant small molecules, 8-isoprostane, and an oxidative stress biomarker clinical tested in blood or saliva samples. The sensor devices were rigorously tested against comparable biomolecules, including cortisol, cortisone, and progesterone, demonstrating very minimal cross sensitivity, mostly at very high concentrations and well above their physiological concentration range in exhaled breath condensate. The detectable range of 8-isoprostane in aerosolized test and control samples mimicking exhaled breath condensate was identified as 1-100 pg/mL, which comfortably covers the reported physiological concentration range (10-100 pg/mL) of this biomarker in exhaled breath.
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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) | 0.000 | 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 | 0.000 |
| 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".