Alginate–gelatin–Matrigel hydrogels enable the development and multigenerational passaging of patient-derived 3D bioprinted cancer spheroid models
Bibliographic record
Abstract
Abstract Hydrogels consisting of controlled fractions of alginate, gelatin, and Matrigel enable the development of patient-derived bioprinted tissue models that support cancer spheroid growth and expansion. These engineered models can be dissociated to be then reintroduced to new hydrogel solutions and subsequently reprinted to generate multigenerational models. The process of harvesting cells from 3D bioprinted models is possible by chelating the ions that crosslink alginate, causing the gel to weaken. Inclusion of the gelatin and Matrigel fractions to the hydrogel increases the bioactivity by providing cell-matrix binding sites and promoting cross-talk between cancer cells and their microenvironment. Here we show that immortalized triple-negative breast cancer cells (MDA-MB-231) and patient-derived gastric adenocarcinoma cells can be reprinted for at least three 21 d culture cycles following bioprinting in the alginate/gelatin/Matrigel hydrogels. Our drug testing results suggest that our 3D bioprinted model can also be used to recapitulate in vivo patient drug response. Furthermore, our results show that iterative bioprinting techniques coupled with alginate biomaterials can be used to maintain and expand patient-derived cancer spheroid cultures for extended periods without compromising cell viability, altering division rates, or disrupting cancer spheroid formation.
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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".