Biophysical and Biochemical Regulation of Cell Dynamics in Magnetically Assembled Cellular Structures
Bibliographic record
Abstract
Soluble signaling molecules and extracellular matrix (ECM) regulate cell dynamics in various biological processes. Wound healing assays are widely used to study cell dynamics in response to physiological stimuli. However, traditional scratch-based assays can damage the underlying ECM-coated substrates. Here, we use a rapid, non-destructive, label-free magnetic exclusion technique to form annular aggregates of bronchial epithelial cells on tissue-culture treated (TCT) and ECM-coated surfaces within 3 h. The cell-free areas enclosed by the annular aggregates are measured at different times to assess cell dynamics. The effects of various signaling molecules, including epidermal growth factor (EGF), oncostatin M, and interleukin 6, on cell-free area closures are investigated for each surface condition. Surface characterization techniques are used to measure the topography and wettability of the surfaces. Further, we demonstrate the formation of annular aggregates on human lung fibroblast-laden collagen hydrogel surfaces, which mimic the native tissue architecture. The cell-free area closures on hydrogels indicate that the substrate properties modulate EGF-mediated cell dynamics. The magnetic exclusion-based assay is a rapid and versatile alternative to traditional wound healing assays.
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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) | 0.000 | 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 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".