Detection of heart-fatty acid binding protein in human serum using gold nano/micro-islands and molecularly imprinted polymers
Bibliographic record
Abstract
Here, we have developed a biomimetic sensor using gold nano/micro-islands (NMIs) and molecularly imprinted polymers (MIPs) for electrochemical detection of H-FABP. The fabricated nanostructured electrode was characterized and its analytical performance was evaluated in human serum spiked with H-FABP in the clinical range (10 pg mL−1to 100 ng mL−1). In this regard, an excellent linear performance (R2=0.99) and a low limit of detection (LOD) of 4.86 pg mL−1were observed. Moreover, the selectivity performance of the proposed biosensor was investigated in the presence of troponin T (TnT), and myoglobin (Myo). The differential pulse voltammetry (DPV) results showed that the MIP current has altered approximately 16, 20, and 62% for TnT, Myo, and H-FABP, respectively, confirming the low interference of other cardiac biomarkers on the sensing performance. Ultimately, the developed biomimetic sensor fabricated by the integration of hierarchical structures of gold and MIP opens up future perspectives for sensitive, low-cost, and early detection of myocardial infarction in human serum.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| 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 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".