QUANTITATIVE MEASUREMENTS OF DICTYOSTELIUM DISCOIDEUM AMOEBA SHEAR STRESS-DEPENDENT CELL ADHESION ANALYSIS AND MOTION IN A MICROFLUIDIC DEVICE
Bibliographic record
Abstract
Cell adhesion is a fundamental biological process that involves the interaction and attachment of cells to their neighbors or the extracellular matrix (ECM) through specialized protein complexes 1 .These adhesion proteins serve not only as physical connectors but also as receptors that initiate signaling pathways 2 , thus transmitting information from the external environment to the cell's interior and enabling appropriate responses.Shear stress, an external mechanical force, influences different types of cells through mechanotransduction 3 , affecting cell adhesion and gene expression 4 .While the molecular mechanisms of cell adhesion under shear stress have been extensively studied, the mechanical aspects, including adhesion strength, remain a focus of ongoing research 5 .Traditional methods to study cell adhesion strength under shear stress involve macroscale setups, which have limitations such as low throughput and complexity.Microfluidic devices offer a more efficient and versatile platform for these studies, characterized by their small channels, which ensure laminar flow and consistent shear stresses, and require minimal reagent amounts.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".