Electrochemiluminescent magnetic biosensor for simultaneous microRNA and parathyroid hormone detection via resonance energy transfer
Bibliographic record
Abstract
• Dual-target ECL biosensor detects miR-222 and PTH for integrated thyroid malignancy diagnosis. • Uses CRET with luminol and dyes, requiring no external light and minimizing background noise. • Achieves ultrasensitive detection (0.38 fM & 0.22 pg/mL) in 30 minutes for point-of-care testing. Thyroid-related malignancies often involve both oncogenic alterations and endocrine imbalance, necessitating integrated biomarker monitoring. In this work, we report an electrochemiluminescent (ECL) magnetic biosensor for the simultaneous detection of microRNA (miR) and parathyroid hormone (PTH), two clinically relevant indicators of thyroid cancer and parathyroid dysfunction. The sensing platform leverages luminol as an ECL donor and organic dyes as energy acceptors within a chemiluminescence resonance energy transfer (CRET) framework, enabling excitation-free signal generation with low background interference. Surface-modified magnetic nanoparticles serve as dual-function carriers, facilitating efficient magnetic enrichment and high-affinity target recognition. The system achieves sensitive and specific dual-analyte detection in a single assay with broad linear ranges and rapid turnaround. Limits of detection in human serum were 0.38 fM for miR-222 and 0.22 pg/mL for PTH. This CRET-ECL magnetic biosensor offers a rapid, sensitive, and minimally invasive approach for point-of-care evaluation of thyroid malignancy and associated endocrine disorders.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".