Tuning the Microenvironment: Click‐Crosslinked Hyaluronic Acid‐Based Hydrogels Provide a Platform for Studying Breast Cancer Cell Invasion
Bibliographic record
Abstract
A big challenge in cell culture is the non‐natural environment in which cells are routinely screened, making in vivo phenomena, such as cell invasion, difficult to understand and predict. To study cancer cell invasion, extracellular matrix (ECM) analogs with decoupled mechanical and chemical properties are required. Hyaluronic acid (HA)‐based hydrogels crosslinked with matrix‐metalloproteinase (MMP)‐cleavable peptides are developed to study MDA‐MB‐231 breast cancer cell invasion. Hydrogels are synthesized by reacting furan‐modified HA with bismaleimide peptide crosslinkers in a Diels–Alder click reaction. This new hydrogel takes advantage of the biomimetic properties of HA, which is overexpressed in breast cancer, and eliminates the use of nonadhesive crosslinkers, such as poly(ethylene glycol) (PEG). The crosslink (mechanical) and ligand (chemical) densities are varied independently to evaluate the effects of each parameter on cell migration. Increased crosslink density correlates with decreased MDA‐MB‐231 cell invasion whereas incorporation of MMP‐cleavable sequences within the peptide crosslinker enhances invasion. Increasing the ligand density of pendant GRGDS groups induces cell proliferation, but has no significant impact on invasion. By independently tuning the mechanical and chemical environment of ECM mimetic hydrogels, a platform is provided that recapitulates variable tissue properties and elucidates the role of the microenvironment in cancer cell invasion.
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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".