Disposable Sensor Chips with Molecularly Imprinted Carbon Paste Electrodes for Monitoring Anti-Epileptic Drugs
Bibliographic record
Abstract
is a neurological disorder that affects millions of people worldwide. Anti-epileptic drugs (AEDs) are critical for their management. However, the therapeutic window is narrow, and traditional laboratory-based therapeutic drug monitoring (TDM) methods can be time consuming and unsuitable for point-of-care testing. To address this issue, we developed a disposable sensor chip based on molecularly imprinted polymer-modified carbon paste electrodes (MIP-CPs) for the TDM of AEDs such as phenobarbital (PB), carbamazepine (CBZ), and levetiracetam (LEV). In this work, functional monomers (methacrylic acid) and crosslinking monomers (methylene bisacrylamide and ethylene glycol dimethacrylate) were copolymerized in the presence of the AED template and grafted on the graphite particles by simple radical photopolymerization. The grafted particles were mixed with silicon oil, dissolving ferrocene as a redox marker to make the MIP-carbon paste (CP). Disposable sensor chips were fabricated by packing the MIP-CP into the base made of poly (ethylene glycol terephthalate) (PET) film. The sensor's sensitivity was determined using differential pulse voltammetry (DPV), carried out on a single sensor chip for each operation. Linearity was obtained from 0-60 μg/mL in PB and LEV and 0-12 μg/mL in CBZ, covering their respective therapeutic range. The time taken for each measurement was around 2 min. The experiment using whole bovine blood and bovine plasma indicated that the existence of species that interfered had a negligible effect on the test's sensitivity. This disposable MIP sensor provides a promising approach for point-of-care testing and facilitating the management of epilepsy. Compared with existing tests, this sensor offers a faster and more accurate way to monitor AEDs, which is crucial for optimizing therapy and improving patient outcomes. Overall, the proposed disposable sensor chip based on MIP-CPs represents a significant advancement in AED monitoring, with the potential for rapid, accurate, and convenient point-of-care testing.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".