Engineering bioprintable alginate/gelatin composite hydrogels with tunable mechanical and cell adhesive properties to modulate tumor spheroid growth kinetics
Bibliographic record
Abstract
Tunable bioprinting materials are capable of creating a broad spectrum of physiological mimicking 3D models enabling in vitro studies that more accurately resemble in vivo conditions. Tailoring the material properties of the bioink such that it achieves both bioprintability and biomimicry remains a key challenge. Here we report the development of engineered composite hydrogels consisting of gelatin and alginate components. The composite gels are demonstrated as a cell-laden bioink to build 3D bioprinted in vitro breast tumor models. The initial mechanical characteristics of each composite hydrogel are correlated to cell proliferation rates and cell spheroid morphology spanning month long culture conditions. MDA-MB-231 breast cancer cells show gel formulation-dependency on the rates and frequency of self-assembly into multicellular tumor spheroids (MCTS). Hydrogel compositions comprised of decreasing alginate concentrations, and increasing gelatin concentrations, result in gels that are mechanically soft and contain a greater number of cell-adhesion moieties driving the development of large MCTS; conversely gels containing increasing alginate, and decreasing gelatin concentrations are mechanically stiffer, with fewer cell-adhesion moieties present in the composite gels yielding smaller and less viable MCTS. These composite hydrogels can be used in the biofabrication of tunable in vitro systems that mimic both the mechanical and biochemical properties of the native tumor stroma.
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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".