On-chip combined radiotherapy and chemotherapy testing on soft-tissue sarcoma spheroids to study cell death using flow cytometry and clonogenic assay
Bibliographic record
Abstract
Radiotherapy (RT) and chemotherapy (CT) are the major therapeutics to treat cancer patients. Conventional in vitro 2D models are insufficient to study the combined effects of RT and CT towards optimized dose selection or drug screening. Soft-tissue sarcomas (STS) are rare cancers with profound social impacts as they affect patients of all ages. We developed a microfluidic device to form and culture STS spheroids to study the combined cytotoxicities of RT and CT. Uniformly-sized spheroids of two different cell lines, STS 93 and STS 117, were formed in the device. RT doses of 0.5 Gy, 2 Gy, and 8 Gy were used in combination with CT, doxorubicin at 2 µM and 20 µM. The spheroids culture chambers within the device were arranged in a 3 × 5 matrix form. The device was made "peelable", which enabled us to collect spheroids from each treatment condition separately. Collected spheroids were dissociated into single cells and evaluated using flow cytometry and clonogenic assays. Through this workflow, we observed that STS 93 spheroids treated with doxorubicin die through apoptosis, whereas RT induced death through other pathways. Spheroids from the p53 mutant STS 117 cell line were more resistant to RT and doxorubicin. The developed device could be used for the discovery of new drugs and RT synergies.
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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) | 0.002 | 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".