Magnetic-material-assisted bioassays: From millimeter magnetic bars to nanometer magnetic particles
Bibliographic record
Abstract
The integration of magnetic materials in bioassays has enabled higher assay sensitivity and faster assay, leading to advancements in disease prognosis and diagnosis. This review article discusses recent progress of using a wide range of magnetic materials to facilitate bioassays and achieve high assay performance, from using millimeter magnetic bars to nanometer magnetic particles. It focuses on the various applications of magnetic particles in bioassays, emphasizing their roles as detection labels and mobile substrates. The review covers the use of magnetic materials in different platforms such as microfluidics, paper-based systems, and hydrogel-based bioassays. It also explores recent electrical and magnetic detection techniques, including the blocking effect of microspheres on conductive cell and immunomagnetic reduction (IMR). The advancements in magnetic-based bioassays across various scales and their applications in detecting diseases like COVID-19 and cancer are reviewed. This analysis aims to provide insights into the design and engineering of the magnetic-material-based bioassay platforms for improved disease marker detection and patient outcomes.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.002 |
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".