Fabrication of a PDMS-based substrate with a stiffness gradient for modeling the mechanical microenvironment in single and collective cell studies
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it