Smartphone-based microplate reader for high-throughput quantitation of disease markers in serum
Bibliographic record
Abstract
Herein, a smartphone-based portable reader with integrated optics for standard microtiter plates (96 wells) has been designed and demonstrated for high-throughput quantitation of validated biomarkers in serum. The customized optical attachment was simply constructed with a convex lens and a light source, by which the transmitted light through a 96-well microtiter plate was converged for imaging with a smartphone, so that accurate and wide-range reading of the plate can be achieved. More importantly, relying on the digitized colorimetric analysis of the obtained images, concentrations of various biomarkers can be determined directly using the customized mobile app. A set of validated biomarkers for inflammation and infection, C-reactive protein (CRP), serum amyloid A (SAA), and procalcitonin (PCT) have been quantitated with this new system; both the response ranges and limits of detection meet the requirement of clinical tests. The consistency with the results obtained using a commercial microplate reader proves its reliability and precision, augments its potential as a point-of-care diagnostic device for on-site testing or resource-limited settings.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".