A high-performance electrochemical aptasensor based on graphene-decorated rhodium nanoparticles to detect HER2-ECD oncomarker in liquid biopsy
Bibliographic record
Abstract
Evaluation of extracellular domain of human epidermal growth factor receptor-2 (HER2-ECD) oncomarker status is an impressive factor in screening, diagnosing and monitoring early-stage breast cancer (BC). Electrochemical aptamer-based nanobiosensor with high sensitivity and selectivity for quantitative and qualitative measurement of HER2-ECD oncomarker was developed. In this study, the nanocomposite made by distinct materials included reduced graphene oxide nano-sheets (rGONs) and rhodium nanoparticles (Rh-NPs) on the graphite electrode (GE) surface. This structure resulted in amplified electrochemical activity, high surface area, stability, and bio-compatibility. Each of the steps of preparing nanomaterials and setting up biosensor were carefully examined by analytical and electrochemical techniques. Various modified electrodes were constructed and analyzed in terms of electrochemical performance, morphology, size, and shape of nanomaterials. The GE-based aptasensor had a noteworthy and conducive results against HER2-ECD with a wide dynamic range of 10.0-500.0 ng/mL, a low limit of detection (LOD) of 0.667 ng/mL (significantly less than the clinical cut-off), and a low limit of quantification (LOQ) of 2.01 ng/mL. The benefits provided by this aptasensor such as broad dynamic range, high sensitivity, selectivity, stability, reproducibility, and low cost suggest tremendous potential for non-invasive detection and monitoring of the HER2-ECD levels of BC care and clinical diagnosis.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".