Microfluidic Platform for Generating and Releasing Patient‐Derived Cancer Organoids with Diverse Shapes: Insight into Shape‐Dependent Tumor Growth
Bibliographic record
Abstract
Multicellular spheroids and patient-derived organoids find many applications in fundamental research, drug discovery, and regenerative medicine. Advances in the understanding and recapitulation of organ functionality and disease development require the generation of complex organoid models, including organoids with diverse morphologies. Microfluidics-based cell culture platforms enable time-efficient confined organoid generation. However, the ability to form organoids with different shapes with a subsequent transfer from microfluidic devices to unconstrained environments for studies of morphology-dependent organoid growth is yet to be demonstrated. Here, a microfluidic platform is introduced that enables high-fidelity formation and addressable release of breast cancer organoids with diverse shapes. Using this platform, the impact of organoid morphology on their growth in unconstrained biomimetic hydrogel is explored. It is shown that proliferative cancer cells tend to localize in high positive curvature organoid regions, causing their faster growth, while the overall growth pattern of organoids with diverse shapes tends to reduce interfacial tension at the organoid-hydrogel interface. In addition to the formation of organoids with diverse morphologies, this platform can be integrated into multi-tissue micro-physiological systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".