Microfluidic Arrays of Breast Tumor Spheroids for Drug Screening and Personalized Cancer Therapies
Bibliographic record
Abstract
One of the obstacles limiting progress in the development of effective cancer therapies is the shortage of preclinical models that capture the dynamic nature of tumor microenvironments. Interstitial flow strongly impacts tumor response to chemotherapy; however, conventional in vitro cancer models largely disregard this key feature. Here, a proof of principle microfluidic platform for the generation of large arrays of breast tumor spheroids that are grown under close-to-physiological flow in a biomimetic hydrogel is reported. This cancer spheroids-on-a-chip model is used for time- and labor-efficient studies of the effects of drug dose and supply rate on the chemosensitivity of breast tumor spheroids. The capability to grow large arrays of tumor spheroids from patient-derived cells of different breast cancer subtypes is shown, and the correlation between in vivo drug efficacy and on-chip spheroid drug response is demonstrated. The proposed platform can serve as an in vitro preclinical model for the development of personalized cancer therapies and effective screening of new anticancer drugs.
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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 | 0.001 |
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".