Spatial Photopatterning of Substrate Stiffness in Dual-Cure Silicones for Cardiac Mechano-Regulation
Bibliographic record
Abstract
The mechanical properties of the extracellular matrix play a key role in regulating cellular functions, yet many in vitro models lack the mechanical complexity of native tissues. Traditional hydrogel-based substrates offer tunable stiffness but are often limited by instability, porosity, and coupled changes in both mechanical and structural properties, making it difficult to isolate the effects of stiffness alone. Here, we introduce a spatially patterned dual-cure polydimethylsiloxane (DC-PDMS) system, a nonporous, mechanically tunable polymer that allows for precise spatial control of stiffness over a range of patho-physiological values. This platform enables the design and creation of in vitro models for studying the influence of spatial mechanical cues on cellular behavior. To demonstrate its utility, we examined primary cardiac fibroblast responses across different substrate stiffness conditions. Fibroblasts on soft regions exhibited rounded morphologies with disorganized actin networks, while those on stiffer regions became more elongated with highly aligned stress fibers, indicating stiffness-dependent cytoskeletal remodeling. Stiff substrates also led to nuclear compression and increased nucleus curvature, correlating with increased nuclear localization of YAP, a key mechanotransduction regulator. By allowing cells to interact with mechanically distinct regions within a single substrate, this system provides a powerful approach for investigating mechanotransduction processes relevant to fibrosis and other mechanically regulated diseases. The ability to create stiffness patterns with subcellular resolution makes DC-PDMS a valuable tool for studying cell-material interactions, enabling new insights into mechanobiology-driven cellular responses and therapeutic targets.
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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".