Fabrication of a PDMS-based substrate with a stiffness gradient for modeling the mechanical microenvironment in single and collective cell studies
Bibliographic record
Abstract
Mechanotransduction plays a pivotal role in shaping cellular behavior including migration, differentiation, and proliferation. To investigate this mechanism more accurately further, this study came up with a novel elastomeric substrate with a stiffness gradient using a sugar-based replica molding technique combined with a two-layer PDMS system. The efficient water solubility of candy allows easy release, creating a smooth substrate. By adjusting the substrate's thickness, the optimal effective gradient length for the study is achievable. Additionally, adjusting substrate thickness precisely controls stiffness, from very soft to hard-tissue-like rigidity. Atomic force microscopy characterization confirmed a continuous stiffness gradient on three commonly used PDMS mixtures, 1:30, 1:50, and 1:75, demonstrating the versatility of this method for fabricating and tuning substrates to mimic various tissue environments. In cellular experiments, 3T3 fibroblast cells exhibited a significant migratory response toward the 1:50/1:75 two-layer stiffness gradient, with cells migrating preferably in stiffer directions. Its cost-effectiveness, smooth surface, and ability to regulate gradient substrates with varied stiffness via different PDMS combinations are key advantages. By precisely replicating physiologically relevant mechanical microenvironments, this method advances mechanobiology research and facilitates modeling of stiffness-guided cellular behaviors, paving the way for reliable tissue engineering and regenerative medicine studies.
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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".