Study of flow behaviors on single-cell manipulation and shear stress reduction in microfluidic chips using computational fluid dynamics simulations
Bibliographic record
Abstract
Various single-cell retention structures (SCRSs) were reported for analysis of single cells within microfluidic devices. Undesirable flow behaviors within micro-environments not only influence single-cell manipulation and retention significantly but also lead to cell damage, biochemical heterogeneity among different individual cells (e.g., different cell signaling pathways induced by shear stress). However, the fundamentals in flow behaviors for single-cell manipulation and shear stress reduction, especially comparison of these behaviors in different microstructures, were not fully investigated in previous reports. Herein, flow distribution and induced shear stress in two different single-cell retention structures (SCRS I and SCRS II) were investigated in detail to study their effects on single-cell trapping using computational fluid dynamics (CFD) methods. The results were successfully verified by experimental results. Comparison between these two SCRS shows that the wasp-waisted configuration of SCRS II has a better performance in trapping and manipulating long cylinder-shaped cardiac myocytes and provides a safer "harbor" for fragile cells to prevent cell damage due to the shear stress induced from strong flows. The simulation results have not only explained flow phenomena observed in experiments but also predict new flow phenomena, providing guidelines for new chip design and optimization, and a better understanding of the cell micro-environment and fundamentals of microfluidic flows in single-cell manipulation and analysis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".