Adhesion based detection, sorting and enrichment of cells in microfluidic Lab-on-Chip devices
Bibliographic record
Abstract
The detection, isolation and sorting of cells are important tools in both clinical diagnostics and fundamental research. Advances in microfluidic cell sorting devices have enabled scientists to attain improved separation with comparative ease and considerable time savings. Despite the great potential of Lab-on-Chip cell sorting devices for targeting cells with desired specificity and selectivity, this field of research remains unexploited. The challenge resides in the detection techniques which has to be specific, fast, cost-effective, and implementable within the fabrication limitations of microchips. Adhesion-based microfluidic devices seem to be a reliable solution compared to the sophisticated detection techniques used in other microfluidic cell sorting systems. It provides the specificity in detection, label-free separation without requirement for a preprocessing step, and the possibility of targeting rare cell types. This review elaborates on recent advances in adhesion-based microfluidic devices for sorting, detection and enrichment of different cell lines, with a particular focus on selective adhesion of desired cells on surfaces modified with ligands specific to target cells. The effect of shear stress on cell adhesion in flow conditions is also discussed. Recently published applications of specific adhesive ligands and surface functionalization methods have been presented to further elucidate the advances in cell adhesive microfluidic devices.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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".