Blu-ray Technology-Based Quantitative Assays for Cardiac Markers: From Disc Activation to Multiplex Detection
Bibliographic record
Abstract
Acute myocardial infarction (AMI) is the leading cause of mortality and morbidity globally. To reduce the number of mortalities, reliable and rapid point-of-care (POC) diagnosis of AMI is extremely critical. We herein present a Blu-ray technology-based assay platform for multiplex cardiac biomarker detection; not only off-the-shelf Blu-ray discs (BDs) were adapted as substrates to prepare standard immunoassays and DNA aptamer/antibody hybrid assays for the three key cardiac marker proteins (myoglobin, troponin I, and C-creative protein) but also an unmodified optical drive was directly employed to read the assay results digitally. In particular, we have shown that all three cardiac markers can be quantitated in their respective physiological ranges of interest, and the detection limits achieved are comparable with conventional enzyme-linked immunosorbent assay (ELISA) kits. The Blu-ray assay platform was further validated by measuring real-world samples and establishing a linear correlation with the simultaneously obtained ELISA data. Without the need to modify either the hardware (Blu-ray discs and optical drives) or the software driver, this assay-on-a-BD technique promises to be a low-cost user-friendly quantitative tool for on-site chemical analysis and POC medical diagnosis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".