Bioprintable Alginate/Gelatin Hydrogel 3D <em>In Vitro</em> Model Systems Induce Cell Spheroid Formation
Bibliographic record
Abstract
The cellular, biochemical, and biophysical heterogeneity of the native tumor microenvironment is not recapitulated by growing immortalized cancer cell lines using conventional two-dimensional (2D) cell culture. These challenges can be overcome by using bioprinting techniques to build heterogeneous three-dimensional (3D) tumor models whereby different types of cells are embedded. Alginate and gelatin are two of the most common biomaterials employed in bioprinting due to their biocompatibility, biomimicry, and mechanical properties. By combining the two polymers, we achieved a bioprintable composite hydrogel with similarities to the microscopic architecture of a native tumor stroma. We studied the printability of the composite hydrogel via rheology and obtained the optimal printing window. Breast cancer cells and fibroblasts were embedded in the hydrogels and printed to form a 3D model mimicking the in vivo microenvironment. The bioprinted heterogeneous model achieves a high viability for long-term cell culture (> 30 days) and promotes the self-assembly of breast cancer cells into multicellular tumor spheroids (MCTS). We observed the migration and interaction of the cancer-associated fibroblast cells (CAFs) with the MCTS in this model. By using bioprinted cell culture platforms as co-culture systems, it offers a unique tool to study the dependence of tumorigenesis on the stroma composition. This technique features a high-throughput, low cost, and high reproducibility, and it can also provide an alternative model to conventional cell monolayer cultures and animal tumor models to study cancer biology.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".