Independently Tuning the Biochemical and Mechanical Properties of 3D Hyaluronan-Based Hydrogels with Oxime and Diels–Alder Chemistry to Culture Breast Cancer Spheroids
Bibliographic record
Abstract
For native breast cancer cell growth to be mimicked in vitro as spheroids, a well-defined matrix that mimics the tumor microenvironment is required. Finding a biomimetic material for 3D cell culture other than Matrigel has challenged the field. Because hyaluronan is naturally abundant in the tumor microenvironment and can be chemically modified, we synthesized a hyaluronan (HA) hydrogel with independently tunable mechanical and chemical properties for 3D culture of breast cancer cells. By modifying HA with distinct bioorthogonal functional groups, its mechanical properties are controlled by chemical cross-linking via oxime ligation, and its biochemical properties are controlled by grafting bioactive peptides via Diels-Alder chemistry. A series of hydrogels were screened in terms of stiffness and peptide composition for cancer spheroid formation. In the optimal hydrogel formulation, the 3D breast cancer spheroids showed decreased drug diffusion into their core and upregulation of cellular multidrug-resistant efflux pumps similar to what is observed in drug-resistant tumors. Our results highlight the potential of these tunable and well-defined gels in drug screening assays.
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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) | 0.000 | 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 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".