Interleukin-6 electrochemical sensor using poly(o-phenylenediamine)-based molecularly imprinted polymer
Bibliographic record
Abstract
Interleukin-6 (IL-6) is an important cytokine involved in immune responses and maintaining body homeostasis. Elevated IL-6 levels, exceeding ~40 pg/mL in bodily fluids, are associated with inflammation and diseases such as COVID-19, cardiovascular disorders, and Alzheimer’s, necessitating real-time health monitoring for personalized healthcare. In this study, we developed a highly sensitive and selective IL-6 sensing platform by depositing a poly(o-phenylenediamine) (P(o-PD))-based molecularly imprinted polymer (MIP) onto an oxygen-functionalized screen-printed carbon electrode with gold nanoparticles, 3-aminopropyltriethoxysilane (APTES), and glutaraldehyde (GA). While APTES enhanced the peak current due to redox probe adsorption, GA reduced it due to non-conductive aldehyde groups. This functionalized surface improved hydrophilicity due to the presence of amino and carbonyl groups. We identified that a 120-minute NaCl treatment effectively removed IL-6 templates, ensuring the successful embedding of IL-6 in the P(o-PD) matrix. We calculated an imprinting factor of 11.2, indicating the effective imprinting of IL-6 by P(o-PD) and the presence of IL-6 specific binding sites within the MIP, resulting in a robust current response to IL-6. After optimizing the MIP deposition through five cycles, we detected IL-6 concentrations ranging from 2 to 400 pg/mL, with a sensitivity of 3.48 μA/log(pg/mL) and a limit of detection of 1.74 pg/mL. When tested in real human serum, the sensor encountered challenges due to P(o-PD) matrix adsorbing other active molecules. Despite this challenge, our platform demonstrates high selectivity and long-term stability in IL-6 detection. These qualities position our sensor well-suited for point-of-care diagnostics, emphasizing its potential as a reliable tool for real-world applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".