Dynamic and Reversible Tuning of Hydrogel Viscoelasticity by Transient Polymer Interactions for Controlling Cell Adhesion
Bibliographic record
Abstract
Cells are highly responsive to changes in their mechanical environment, influencing processes such as stem cell differentiation and tumor progression. To meet the growing demand for materials used for high throughput mechanotransduction studies, simple means of dynamically adjusting the environmental viscoelasticity of cell cultures are needed. Here, a novel method is presented to dynamically and reversibly control the viscoelasticity of naturally derived polymer hydrogels through interactions with poly (ethylene glycol) (PEG). Interactions between PEG and hydrogel polymers, possibly involving hydrogen bonding, stiffen the hydrogel matrices. By dynamically changing the PEG concentration of the solution in which polymer hydrogels are incubated, their viscoelastic properties are adjusted, which in turn affects cell adhesion and cytoskeletal organization. Importantly, this effects is reversible, providing a cost-effective and simple strategy for dynamically adjusting the viscoelasticity of polymer hydrogels. This method holds promise for applications in mechanobiology, biomedicine, and the life sciences.
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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".