Au nanoparticle decorated graphene nanosheets for electrochemical immunosensing of p53 antibodies for cancer prognosis
Bibliographic record
Abstract
The accurate quantification of the level of p53 antibodies in serum is crucial for cancer prognosis. We report a novel and sensitive label-free immunosensor based on gold nanoparticles (Au NPs) self-assembled onto electrochemically reduced graphene oxide (ERGO) for the detection of p53 antibodies. An electrografted p-aminophenol organic layer was used to immobilize graphene oxide (GO) onto the surface of screen printed carbon electrodes (SPCE). The Au NP/ERGO hybrid interface provides a large surface area for the effective immobilization of p53 antigens, as well as it ascertains the bioactivity and stability of immobilized p53 antigens. Scanning electron microscope, Raman and X-ray photoelectron spectroscopies were used to monitor the sensor fabrication and cyclic voltammetry was used to quantify the extent of Au NPs' surface coverage by p53 antigens. Square wave voltammetry (SWV) of a [Fe(CN)6](3-/4-) couple was employed to investigate the immunosensor fabrication and to monitor the binding events between p53 antigens and p53 antibodies. Under optimized experimental conditions, the biosensor displayed good sensitivity and specificity. The p53 antibodies were detected in a concentration as low as 0.088 pg mL(-1) with a linear range from 0.1 pg mL(-1) to 10 ng mL(-1). The high sensitivity of the immunosensor may derive from the high loading of p53 antibodies on Au NPs which increases the number of binding events.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.000 |
| 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".