Adhesion based detection, sorting and enrichment of cells in microfluidic Lab-on-Chip devices
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it