High-Level Lanthanide-Doped Upconversion Nanoparticles-Based Aptasensor to Increase Carcinoembryonic Antigen Detection Sensitivity
Bibliographic record
Abstract
Boosting the accuracy and speed of cancer detection is highly desirous in tumor detection, and sensors capable of detecting carcinoembryonic antigen (CEA) have great application prospects in this field. A highly sensitive sensor is constructed based on the fluorescence resonance energy transfer (FRET) with heavily rare-earth-doped upconversion nanoparticles (UCNPs) as energy donors and polydopamine nanoparticles (PDA NPs) as energy acceptors. This sensor detects the fluctuations in CEA molecules via luminescence quenching and recovery resulting from a competitive binding assay between CEA and PDA NPs. The high-level-doped design of UCNPs (i.e., NaYF4@NaYbF4:1%Tm@NaYF4) is beneficial, providing upconversion luminescence intensity that is more than 10 times higher than that of the conventional low-level-doped UCNPs (i.e., NaYF4@NaYF4:20%Yb, 0.2%Tm@NaYF4). The sensor exhibits impressive sensitivity. Specifically, in diluted fetal bovine serum, the detection limit reaches 0.013 ng/mL in the range of 0–1.5 ng/mL (S/N = 3), while the detection limit is 1.38 ng/mL in the range of 1.5–250 ng/mL (S/N = 3). This method has great potential for future applications in the rapid and early diagnosis and treatment of 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".