Chromatographic behaviour of single cells in a microchannel with dynamic geometry
Bibliographic record
Abstract
We present the design of a microchannel with dynamic geometry that imparts different flow rates to different cells based on their physical properties. This dynamic microchannel is formed between a textured surface and a flexible membrane. As cells flow through the microchannel, the height of the channel oscillates causing periodic entrapment of the larger cells, and as a result, attenuating their velocity relative to the bulk liquid. The smaller cells are not slowed by the moving microstructure, and move synchronously with the bulk liquid. The ability of the dynamic microchannel to selectively attenuate the flow rate of eukaryotic cells is similar to a size-exclusion chromatography column, but with the opposite behavior. The speed of smaller substances is attenuated relative to the larger substances in traditional size-exclusion chromatography columns, whereas the speed of the larger substances that is attenuated in the dynamic microchannel. We verified this property by tracking the flow of single cells through the dynamic microchannel. L1210 mouse lymphoma cells (MLCs), peripheral blood mononuclear cells (PBMCs), and red blood cells (RBCs) were used as model cells. We showed that the flow rate of MLC is slowed by more than 50% compared to PBMCs and RBCs. We characterized the operation of the microchannel by measuring the velocity of each of the three cell types as a function of the pressures used to oscillate the membrane position, as well as the duty cycle of the oscillation.
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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".