Integrating advanced Microfluidic lateral flow systems with a finger-prick blood collection cartridge to create an all-in-one platform for point-of-care diagnostics
Bibliographic record
Abstract
Rapid, point-of-care tests are critical for early diagnosis of disease and detection of biological threats. Lateral flow immunoassays (LFIAs) are well-suited for point-of-care testing due to their ease of use and straightforward readout. However, limitations in sensitivity, quantification, and integration into sample-to-result systems indicate the need for further advancements. This paper introduces a novel all-in-one LFIA that integrates a test strip with optimized geometry within a newly designed finger-prick blood collection cartridge. This innovative system offers an efficient solution for the detection and quantification of hepatitis B antigens, combining precision and user-friendliness in a single device. To enhance assay performance, the conventional test strip's response to the target protein was significantly improved by incorporating a constricted test zone and upstream mixing elements. To meet self-testing requirements, the blood collection cartridge incorporates an innovative two-step rotation mechanism that simplifies sample collection, processing, and application onto the test strip. Numerical simulations of flow dynamics and antibody-antigen interactions using COMSOL guided the optimization of the test strip geometry, achieving a substantial improvement in the limit of detection from 1.78 ± 0.08 to 0.55 ± 0.04 ng/mL compared to the classic rectangular strip geometry. The optimized design also increased analytical sensitivity from 1.4 ± 0.1 to 2.8 ± 0.1 RU.mL/ng. The system demonstrated complete functionality, from sample collection to analyte quantification. This integrated, user-friendly platform provides an advanced, sample-to-result diagnostic solution for detecting disease markers from finger-prick blood samples. Its simplicity makes it suitable for point-of-care testing, including home use by non-professionals.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".