In Situ Modulation of Cell Behavior via Smart Dual-Ligand Surfaces
Bibliographic record
Abstract
Due to the highly complex nature of the extracellular matrix (ECM), the design and implementation of dynamic, stimuli-responsive surfaces that present well-defined ligands and serve as model ECM substrates have been of tremendous interest to biomaterials, biosensor, and cell biology communities. Such tools provide strategies for identifying specific ligand-receptor interactions that induce vital biological consequences. Herein, we report a novel dual-ligand-presenting surface methodology that modulates dynamic ECM properties to investigate various cell behaviors. Peptides PHSRN, cRGD, and KKKTTK, which mimic the cell- and heparan sulfate-binding domains of fibronectin, and carbohydrates Gal and Man were combined with cell adhesive RGD to survey possible synergistic or antagonist ligand effects on cell adhesion, spreading, growth, and migration. Soluble molecule and enzymatic inhibition assays were also performed, and the levels of focal adhesion kinase in cells subjected to different ligand combinations were quantified. A redox-responsive trigger was incorporated into this surface strategy to spontaneously release ligands in the presence of adhered cells, and cell spreading, growth, and migration responses were measured and compared. The identity and nature of the dual-ligand combination directly influenced cell behavior.
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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) | 0.001 | 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".