Mechanism of Cell Transport in a Microchannel With Binding Between Cell Surface and Immobilized Biomolecules
Bibliographic record
Abstract
Recent trends in micro and nano fabrication techniques have opened a new era for microfluidic based immunosensing devices. In immunosensing microfluidic device, the buffer solution transports the different biomolecules and cells. The interaction between the cell and surface of the microchannel takes place during this transport. In the present study, the effect of interaction between the cell and the immobilized biomolecule on the cell transport is analyzed theoretically. A single cell transport is studied with the interaction between the cell surface and the microchannel wall. The type of immobilized biomolecule on the surface and the surface properties of the cell decide the interaction force between cell and biomolecule. In the present analysis, the interaction force between the cell and modified microchannel is considered as a bond force between ligand and receptor. The bond force is equated as an additional rolling friction to investigate the effect of bond force on the cell transport behavior. The coefficient of rolling friction is determined through non-dimensional analysis. The non-dimensional governing equation is solved to investigate the effect of different operation parameters on cell velocity. The cell velocity experiences a resistance while attaining the maximum velocity. This resistance depends on different operating parameters and forces acting on the cell. It is observed that, higher cell density delays the attainment of maximum cell velocity. It is also observed that, the value of maximum cell velocity is function of Reynolds number and bond length. Finally, it is demonstrated that, the bond density and contact area have no effect on the cell velocity behavior beyond the maximum bond density.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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 teacher head, 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".