Deterministic Lateral Displacement as a Means to Enrich Large Cells for Tissue Engineering
Bibliographic record
Abstract
The enrichment or isolation of selected cell types from heterogeneous suspensions is required in the area of tissue engineering. State of the art techniques utilized for this separation include preplating and sieve-based approaches that have limited ranges of purity and variable yield. Here, we present a deterministic lateral displacement (DLD) microfluidic device that is capable of separating large epithelial cells (17.3 +/- 2.7 in diameter) from smaller fibroblast cells (13.7 +/- 3.0 microm in diameter) as a potential alternative approach. The mixed suspension examined is intended to represent the content of digested rat cardiac tissue, which contains equal proportions of cardiomyocyte (17.0 +/- 4.0 microm diameter) and nonmyocyte populations (12.0 +/- 3.0 microm diameter). High purity separation (>97%) of the larger cell type is achieved with 90% yield in a rapid and single-pass process. The significance of this work lies in the recognition that DLD design principles can be applied for the microfluidic enrichment of large cells, up to the 40 microm diameter level examined in this work.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".