Aptasensor Made of Carbon-Based Nanostructures for Detecting Spermine
Bibliographic record
Abstract
Spermine, a type of polyamine, plays an important role in regulating ion channels, inhibits fat formation, and promotes skeletal development and intestinal maturation. Recently, spermine has been considered as a biomarker for diagnosis of pancreatic cancer. In this paper, an aptasensor made of carbon nanostructures has been developed to quickly and quantitatively detect spermine in aqueous media by using the Förster resonance energy transfer (FRET) quenching technique. Carbon quantum dots (CDs), acting as the FRET donor, have an average diameter of 5.0±2.5 nm and show the maximum emission intensity (Imax) at 510 nm when the excitation (λex) is at 400 nm. The spermine DNA aptamer has been bioconjugated onto the surface of CDs. The Imaxof CDs conjugated with the aptamer is decreased significantly after interacting with graphene oxide nanosheets (GO), the FRET quencher, because of the short distance between CDs and GO through coupling with the DNA aptamer. Due to the strong affinity between the DNA aptamer and spermine, the fluorescence intensity of the CDs-based DNA sensor is restored in the presence of spermine. The spermine detection range, 0.2-250 nM, of this DNA sensor made of carbon-based nanostructures, has been analyzed. This study can provide a cost-effective and user-friendly sensing system to quickly detect spermine, a biomarker associated with pancreatic cancer.
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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 | 0.001 |
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".