Early Detection of Cardiac Disease with a Paper Based Fluorescence Biosensor
Bibliographic record
Abstract
Early detection of cardiac heart disease is essential for timely intervention. Cardiac troponin I (cTnI) is a validated biomarker of myocardial injury; however, current diagnostic platforms are often lab-based, time-intensive, and inaccessible at the point of care. This thesis presents the development of a fluorescence-based lateral flow assay (fLFA) for sensitive cTnI detection, combining technical optimization with patient-centered design. A Taguchi orthogonal matrix guided refinement of assay parameters, including conjugate pad composition, stabilizers, surfactants, and membrane type. Fluorescent signal quantification using a Zeiss AxioZoom microscope (Cy5 channel) was enhanced through sugar bridge–mediated release, yielding an LOD of 0.032 ng/mL and LOQ of 0.096 ng/mL with strong reproducibility (CV% 5.34% @ LOQ). Patient surveys and engagement identified needs and barriers, informing recommendations for home and clinical integration. This work demonstrates that an optimized fLFA can deliver clinically relevant sensitivity while addressing end-user adoption, supporting translation toward rapid, accessible cardiac diagnostics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".