Direct Reading of Bona Fide Barcode Assays for Diagnostics with Smartphone Apps
Bibliographic record
Abstract
The desire to develop new point-of-care (POC) diagnostic tools has led to the adaptation of smartphones to tackle limitations in state-of-the-art instrumentation and centralized laboratory facilities. Today's smartphones possess the computer-like ability to image and process data using mobile apps; barcode scanners are one such type of apps. We demonstrate herein that a diagnostic assay can be performed by patterning immunoassay strips in a bona fide barcode format such that after target binding and signal enhancement, the linear barcode can be read directly with a standard smartphone app. Quantitative analysis can then be performed based on the grayscale intensities with a customized mobile app. This novel diagnostic concept has been validated for a real-world application, i.e., the detection of human chorionic gonadotropin, a pregnancy hormone. With the possibility of multiplex detection, the barcode assay protocol promises to boost POC diagnosis research by the direct adaptation of mobile devices and apps.
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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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".