A highly sensitive nanobiosensor based on aptamer-conjugated graphene-decorated rhodium nanoparticles for detection of HER2-positive circulating tumor cells
Bibliographic record
Abstract
Abstract Assessment of human epidermal growth factor receptor-2 (HER2) tumor marker status is an impressive factor in screening, diagnosing and monitoring breast cancer (BC). The electrochemical biosensor is a revolutionary method in cancer diagnosis, which is used in this research to detect HER2 + circulating tumor cells. The electrochemical activity, size, shape, and morphology of the synthesized nanomaterials were analyzed. The hybrid nanocomposite established by the coupling of reduced graphene oxide nanosheets (rGONs) and rhodium nanoparticles (Rh-NPs) on the surface of graphite electrode resulted in improved surface area, electrochemical activity, and biocompatibility. The graphite electrode-based aptasensor (g-aptasensor) demonstrated exceptional performance against HER2-overexpressed SKBR3 cancer cells, with a linear dynamic range of 5.0 to 10.0 × 10 4 cells/mL, an analytical limit of detection (LOD) as low as 1.0 cell/mL, and a limit of quantification (LOQ) of 3.0 cells/mL. The G-rich DNA aptamers can fold into an intermolecular G-quadruplex, which specifically bind to the target molecule. Consequently, the advantages of this highly efficient nanocomposite platform include broad dynamic range, high specificity, selectivity, stability, reproducibility, and low cost. These characteristics indicate that the fabricated nanobiosensor has a high potential for use in detecting and monitoring HER2 level for the care of BC patients and clinical diagnosis.
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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) | 0.000 | 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) | 0.001 | 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".