Micro-Magnetofluidic System for Rare Cell Analysis: From Principle to Translation
Bibliographic record
Abstract
Rare cells play essential roles in the initiation and progression of diseases and therefore their analysis is of great interest. The micro-magnetofluidic system is one of the emerging platforms that have been proposed for the rapid, sensitive, and cost-effective analysis of rare cells. Given its unprecedented throughput, micro-magnetofluidic systems have attracted substantial research interest in the last decade—multiple designs have been proposed, validated, and even advanced to the stage of clinical trials. This mini review aims to provide a timely summary of the relevant progress in the field thus far. We reviewed the concepts and realizations of micro-magnetofluidic devices based on the interaction between nanoparticles and on-chip micro-magnets. Their real-world applications in rare cell analysis were also highlighted and explained. In addition, we discussed the major challenges in the development and translation of micro-magnetofluidic into the clinic, including multi-marker capability and large-scale manufacturability.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".