Fluorescence Resonance Energy Transfer-Based Aptasensor Made of Carbon-Based Nanomaterials for Detecting Lactoferrin at Low Concentrations
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Lactoferrin in the saliva is recently considered a biomarker for the diagnosis of Alzheimer’s disease. In this paper, a solution-based, user-friendly biosensing system has been developed to quickly measure lactoferrin at low concentrations. This aptasensor is applied to the fluorescence resonance energy transfer (FRET) quenching mechanism, in which carbon quantum dots (CDs) act as the FRET donor; the FRET quenching element is made of graphene oxide (GO) nanosheets which show good quenching capability. CDs bioconjugated with a chosen aptamer (CDs–aptamer) have the strongest emission (λ em ) at 447 nm when excitation (λ ex ) is 365 nm. Due to the interaction of the aptamer and GO through the π–π* interaction, GO can approach CDs, resulting in FRET quenching. In the presence of lactoferrin, the fluorescence intensity of CDs–aptamer is restored as the binding affinity between lactoferrin and the aptamer is stronger than the π–π* interaction between the aptamer and GO. A linear relationship between the restored fluorescence intensity and the concentration of lactoferrin in artificial saliva with a range from 4 to 16 μg/mL is observed. The limit of detection of the solution-based aptasensor is estimated at 2.48 μg/mL. In addition, the sensing performance of the aptasensor made of carbon nanomaterials has been evaluated to test different proteins including major salivary proteins. The results show that this aptasensor has a high selectivity to detect LF with a low concentration, <16 μg/mL.
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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 | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".