Paper discs in a 3D printed microplate hybrid microfluidic device for low-cost, rapid, and ultrasensitive paper-based bioluminescence detection of human epidermal growth factor receptor 2 (HER2) breast cancer biomarker
Bibliographic record
Abstract
Breast cancer is the most common cancer type in women and it has the highest probability of developing into invasive cancer. Early detection of breast cancer is crucial to reduce the disease burden and decrease the mortality rate. Detection of cancer biomarkers is an attractive non-invasive way to implement early diagnosis and follow-up. Colorimetric enzyme-linked immunosorbent assay (ELISA) is one of the most common techniques used for the detection of cancer biomarkers. However, it requires a long incubation time and a large reagent volume, and it has low sensitivity. Here, we propose use of a paper disc in a 3D printed microplate hybrid microfluidic device for ultrasensitive paper-based bioluminescence ELISA for detection of HER2 breast cancer biomarker. Chromatographic paper discs are good substrates for fast immobilization of capture antibody without making any surface modification and they can be replaced with new discs to reuse the 3D printed microplate. The 3D printed microplate has microvalves in the bottom of the wells, so it can stop flow of the reagents for the desired incubation time and it allows the washing solution to flow vertically and drain onto an adsorption pad which increases the washing efficiency. NanoLuc luciferase was used as a label for the detection antibody to achieve the highest sensitivity. Bioluminescence sandwich ELISA for HER2 detection was performed using the hybrid device in just 20 min and the limit of detection was 1.3 fg/mL which is more than 104-fold better than commercial ELISA kits for HER2.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".