A-410 A laser scanning smartphone-based imaging platform toward point-of-care diagnostic testing
Bibliographic record
Abstract
Abstract Background Smartphones are being explored for point-of-care testing (POCT) due to their global ubiquity and on-board technologies. When smartphones are paired with advanced luminescent materials like quantum dots (QDs) and polymer dots (Pdots), they have sufficient sensitivity to become a viable alternative to sophisticated laboratory instruments for the quantitative detection of biomarkers. To this end, we have developed and benchmarked a laser-scanning smartphone imaging platform (LS-SIP). Such a device is potentially ideal for readout of fluorescence-based assays, such as those executed with microwell plates, lateral flow test strips, and microfluidic or lab-on-a-chip systems. As a flexible multi-purpose readout device, the LS-SIP will help avoid the possible problem of health care professionals in non-laboratory settings accumulating a myriad of different devices for POCT, each specific to a different assay. Methods The LS-SIP was built from 3D-printed parts, simple optics, a DC motor, and a low-cost, low-power laser diode. A line-shaped laser beam is scanned over an imaging platform. During the scan, the smartphone acquires a movie that is flattened into a complete fluorescence image. A predetermined correction matrix was used to improve precision and correct for spatial non-uniformities in illumination intensity across the field of view. The analytical performance of the device, including benchmarking against a commercial plate reader, was evaluated using multiple fluorescent materials—QDs, dyes, and Pdots—in a custom-designed 50 microwell chip. Proof-of-concept lateral flow binding assays were also done using dextran-coated QDs (Dex-QDs), and advanced made-for-purpose materials like supra-QD and super-QD assemblies. Results For each material, the trend in the smartphone-measured PL intensity versus concentration was approximately sigmoidal (consistent with the gamma correction built into the smartphone video acquisition) with a dynamic range of at least one order of magnitude. For 70 µL aliquots of solution within the microwell chip, limits of detection (LODs) were between 20 pM–2 nM for different colors of Dex-QDs, 3 nM for fluorescein dye, and 70–500 fM for different colors of Pdots. When comparing laser scanning to conventional epi-illumination, the laser scanning had 1-2 orders of magnitude lower LODs. For the lateral flow binding assays, the super-QDs provided the lowest LOD (10 pM) whereas Dex-QDs had the highest LOD (10 nM). Conclusion This research is a step toward improving molecular diagnostic capability and accessibility in resource-limited settings, such as rural and remote communities in Canada and worldwide. In addressing the capability gap between these communities and urban centers, this technology supports greater equity and inclusion in modern health care.
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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 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".