Core-shell hydrogel beads with extracellular matrix for tumor spheroid formation
Bibliographic record
Abstract
Creating multicellular tumor spheroids is critical for characterizing anticancer treatments since they may provide a better model of the tumor than conventional monolayer culture. Moreover, tumor cell interaction with the extracellular matrix can determine cell organization and behavior. In this work, a microfluidic system was used to form cell-laden core-shell beads which incorporate elements of the extracellular matrix and support the formation of multicellular spheroids. The bead core (comprising a mixture of alginate, collagen, and reconstituted basement membrane, with gelation by temperature control) and shell (comprising alginate hydrogel, with gelation by ionic crosslinking) were simultaneously formed through flow focusing using a cooled flow path into the microfluidic chip. During droplet gelation, the alginate acts as a fast-gelling shell which aids in preventing droplet coalescence and in maintaining spherical droplet geometry during the slower gelation of the collagen and reconstituted basement membrane components as the beads warm up. After droplet gelation, the encapsulated MCF-7 cells proliferated to form uniform spheroids when the beads contained all three components: alginate, collagen, and reconstituted basement membrane. The dose-dependent response of the MCF-7 cell tumor spheroids to two anticancer drugs, docetaxel and tamoxifen, was compared to conventional monolayer culture.
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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".