MIP-Chip: Integrated Microfluidic Plasma Separation and Redox-Enhanced Molecularly Imprinted Polymer Succinate Sensor for Whole Blood Metabolite Analysis
Bibliographic record
Abstract
The precise quantification of metabolites in bodily fluids is essential for advancing digital health monitoring and clinical diagnostics. Among these fluids, whole blood stands out as a valuable source of predictive metabolite biomarkers, providing critical insights into disease diagnosis and progression. However, traditional blood testing methods often require expensive instrumentation and specialized training, primarily due to the need for plasma extraction to remove interfering blood cells. This study addresses these limitations by introducing a novel, sensitive, rapid, reagent-free, and cost-effective capillary microfluidic-integrated molecularly imprinted polymer (MIP) sensor (MIP-Chip) designed for metabolite detection in whole blood. The MIP-Chip integrates two key components: (1) a highly efficient plasma separation module capable of extracting plasma from whole blood (∼95% efficiency) without requiring sample pretreatment or external active forces and (2) an electrochemical MIP sensor employing an ultrasensitive electrode with on-electrode Prussian Blue nanoparticles (PB NPs) as embedded redox probes for sensitive and specific metabolite detection in the extracted plasma. Using this platform, we successfully quantified succinate, a critical metabolite, across a wide linear concentration range (50 nM-250 μM) with a limit of detection of 5 nM. The device processed 120 μL of whole blood, delivering 8 μL of plasma, and completed the entire workflow-from sample introduction to biomarker detection within 25 min. The MIP-Chip demonstrated exceptional performance, including self-powered assay automation, high specificity for succinate quantification in whole blood, excellent reproducibility, and long-term stability of the MIP-based sensor. These features establish the MIP-Chip as a powerful analytical platform for point-of-care diagnostics, offering a significant step forward in clinical metabolite detection and digital health monitoring.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".