Hydrodynamic shuttling for deterministic high-efficiency multiple single-cell capture in a microfluidic chip
Bibliographic record
Abstract
Studies on cellular heterogeneity have emerged as a powerful approach for developing new strategies to treat diseases including cancer. However, it is difficult to set up an in vitro co-culture experiment to study the interaction of individual live cells. In this paper, we report a hydrodynamic shuttling chip (HSC) which can deterministically capture single cells into microfluidic chambers to set up multiple single-cell co-culture experiments in which individual live cells can be microscopically observed. Using this chip device, we demonstrated a triple single-cell culture of oral squamous cell carcinoma and lymphatic endothelial cells to observe the effect of cell-cell interaction on the cell motility. Triple, single-cell pairing efficiency by our HSC device was eightfold higher than that of the probabilistic method. Using this HSC device, we were able to perform triple-culture experiments to show the cell type-dependent motility of oral squamous cell carcinoma and lymphatic endothelial cells, which was not observed in co-culture experiments.
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.
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 | 0.001 | 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 | 0.001 | 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".