Counting-based microfluidic paper-based devices capable of analyzing submicroliter sample volumes
Bibliographic record
Abstract
In this paper, we report the development of semiquantitative counting-based lateral flow assay (LFA)-type microfluidic paper-based analytical devices (μPADs) to analyze samples at submicroliter volumes. The ability to use submicroliter sample volumes is a significant advantage for μPADs since it enables enhanced multiplexing, reduces cost, and increases user-friendliness since small sample volumes can be collected using methods that do not require trained personnel, such as finger pricking and microneedles. The challenge of accomplishing a semiquantitative test readout using submicroliter sample volumes was overcome with a counting-based approach. In order to use submicroliter sample volumes, we developed a flow strategy with a running liquid to facilitate flow through the assay. The efficacy of the devices was confirmed with glucose and total human immunoglobulin E (IgE) tests using 0.5 μl and 1 μl of sample solutions, respectively. Semiquantitative results were generated to predict glucose concentrations in the range of 0–12 mmol/l and IgE concentrations in the range of 0–400 ng/ml. The counting-based approach correlates the number of dots that exhibited a color change to the concentration of the analyte, which provides a more user-friendly method as compared with interpreting the intensity of a color change. The devices reported herein are the first counting-based LFA-type μPADs capable of semiquantitative testing using submicroliter sample volumes.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".