Composite alginate gels for tunable cellular microenvironment mechanics
Bibliographic record
Abstract
The mechanics of the cellular microenvironment can be as critical as biochemistry in directing cell behavior. Many commonly utilized materials derived from extra-cellular-matrix create excellent scaffolds for cell growth, however, evaluating the relative mechanical and biochemical effects independently in 3D environments has been difficult in frequently used biopolymer matrices. Here we present 3D sodium alginate hydrogel microenvironments over a physiological range of stiffness (E = 1.85 to 5.29 kPa), with and without RGD binding sites or collagen fibers. We use confocal microscopy to measure the growth of multi-cellular aggregates (MCAs), of increasing metastatic potential in different elastic moduli of hydrogels, with and without binding factors. We find that the hydrogel stiffness regulates the growth and morphology of these cell clusters; MCAs grow larger and faster in the more rigid environments similar to cancerous breast tissue (E = 4-12 kPa) as compared to healthy tissue (E = 0.4-2 kpa). Adding binding factors from collagen and RGD peptides increases growth rates, and change maximum MCA sizes. These findings demonstrate the utility of these independently tunable mechanical/biochemistry gels, and that mechanical confinement in stiffer microenvironments may increase cell proliferation.
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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".