Bibliographic record
Abstract
Bioanalysis at a single-cell level has yielded unparalleled insight into the heterogeneity of complex biological samples. Combined with Lab-on-a-Chip concepts, various simultaneous and high-frequency techniques and microfluidic platforms have led to the development of high-throughput platforms for single-cell analysis. Dielectrophoresis (DEP), an electrical approach based on the dielectric property of target cells, makes it possible to efficiently manipulate individual cells without labeling. This review focusses on the engineering designs of recent advanced microfluidic designs that utilize DEP techniques for multiple single-cell analyses. On-chip DEP is primarily effectuated by the induced dipole of dielectric particles, (i.e., cells) in a non-uniform electric field. In addition to simply capturing and releasing particles, DEP can also aid in more complex manipulations, such as rotation and moving along arbitrary predefined routes for numerous applications. Correspondingly, DEP electrodes can be designed with different patterns to achieve different geometric boundaries of the electric fields. Since many single-cell analyses require isolation and compartmentalization of individual cells, specific microstructures can also be incorporated into DEP devices. This article discusses common electrical and physical designs of single-cell DEP microfluidic devices as well as different categories of electrodes and microstructures. In addition, an up-to-date summary of achievements and challenges in current designs, together with prospects for future design direction, is provided.
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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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".