Time-Resolved Förster Resonance Energy Transfer Nanoassay Based on CdTe Quantum Dots for Sensitive Detection of Prostate Cancer Antigen 3
Bibliographic record
Abstract
Prostate Cancer Antigen 3 (PCA3) is a long noncoding RNA highly expressed in prostate cancer cells, making it a promising biomarker for noninvasive prostate cancer diagnosis. Simple and rapid detection using nanoprobes can potentially overcome the limitations of traditional diagnostic techniques. Here, we designed, characterized, and applied a DNA-strand displacement assay based on Förster Resonance Energy Transfer (FRET) between terbium (Tb) ions and semiconductor quantum dots (QDs) as a proof-of-concept for sensitive and specific mix-and-measure quantification of a synthetic DNA analogue of PCA3. The assay utilized QDs synthesized through an aqueous one-pot method. The time-resolved (TR) FRET assays achieved a detection limit of 0.65 nmol L –1 by using a SPARK benchtop fluorescence plate reader and 0.32 nmol L –1 by using a KRYPTOR Compact PLUS clinical plate reader. Despite slightly decreased performance, the TR-FRET nanoassay demonstrated reliable quantification of nanomolar PCA3 concentrations also in samples containing up to 50% of serum. These findings underscore the potential of Tb-to-QD FRET assays for rapid clinical prostate cancer diagnostics, offering a promising tool for the early detection of PCA3 in a noninvasive manner.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".