A microfluidic plasma separation device combined with a surface plasmon resonance biosensor for biomarker detection in whole blood
Bibliographic record
Abstract
Biomarker detection in whole blood enables understanding of the cause, progression, relapse or outcome of treatment of a disease. Conventional biomarker detection techniques, such as enzyme-linked immunosorbent assay, polymerase chain reaction, and immunofluorescence, require long assay time, costly laboratory instruments, large reagent volume and sample pre-processing. Hence, there is an unmet need for reliable capture and detection of biomarkers in unprocessed blood which are adaptable to point-of-care (POC) testing. Here, we present a simple, low-cost, and rapid protein detection device from whole blood samples which has the potential to be employed in a POC setting. The platform consists of two components: a plasma separation device that extracts plasma from whole blood without the application of any external active forces and a SPR sensor chip that uses a label-free optical technique for the detection of biomarkers in the extracted plasma. We have demonstrated the detection of IgG and IgM biomolecules in unprocessed blood at concentrations lower than the physiological value within 15 min. The proposed technique has the potential for improving the diagnosis and screening of many diseases, including cancer, influenza, human immunodeficiency virus, and SARS-Cov2 at POC.
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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 | 0.001 | 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".