Toward Point-of-Care Diagnostics of Breast Cancer: Development of an Optical Biosensor Using Quantum Dots
Bibliographic record
Abstract
Due to the lack of portable, field-deployable diagnostic tests for detecting cancer cells, especially for breast cancer, current detection techniques involve the collection of blood and/or tissue samples, which then need to be sent to laboratories for further analysis before decisions can be made by the treating physicians. Conventional techniques such as mammograms and blood analysis are time consuming, requiring specialized technical personnel, in addition to large and expensive laboratory equipment. Herein, we report the development of a novel sensing method to detect breast cancer-specific microRNAs (miRNAs) captured using a complementary sequence binding technique, and we quantify the mechanism using time-resolved Forster resonance energy transfer (TR-FRET). Using terbium-cryptate, the proposed technique reduces the number of steps required to detect the analyte biomarker in clinical serum samples. We also demonstrate the dual detection of biomarkers using the DNA supporter sequence in the proposed TR-FRET technique. We provide a validated proof-of-concept for a minimally invasive, breast cancer-specific, biomarker detection assay, and demonstrate detection limits in the picomolar range using microfluidics as a detection platform for clinical serum samples. The developed microfluidic biosensor has the potential for use as a portable, field-deployable, and highly sensitive diagnostic tool for the rapid and early detection of breast cancer-specific miRNA signatures.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 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".