Cold Atmospheric Plasma Selectively Disrupts Breast Cancer Growth in a Bioprinted 3D Tumor Microenvironment Model
Bibliographic record
Abstract
Abstract Spine metastases are the most common bone site for breast cancer, with evolving surgery and multidisciplinary care improving outcomes. Current treatments, including chemotherapy and invasive surgery, may damage healthy tissue and may leave residual tumors that lead to recurrence. Cold atmospheric plasma (CAP) offers a non-invasive alternative by delivering reactive oxygen and nitrogen species (RONS) locally to tumor sites, selectively targeting cancer cells while sparing healthy tissue. To assess the impact and selectivity toward tumor cells adjacent to bone-like tissue, we develop a 3D bioprinted tumor-stroma model using a 1% alginate and 7% gelatin cell-laden hydrogel to mimic a bone-like microenvironment. The model co-cultures triple-negative MDA-MB-231 human breast cancer cells with primary human bone marrow mesenchymal stromal cells to simulate tumor-stroma interactions. The effects of CAP treatments are assessed through metabolic activity and viability assays over three days. Results show significant selectivity for cancer cells in both 2D and 3D cultures. CAP minimizes damage to healthy cells, offering the potential for localized treatment over systemic chemotherapies such as doxorubicin. Our novel bioprinted model, combined with a plasma source controlling RONS composition, enables detailed studies of redox-based cancer cell inactivation and highlights CAP as a personalized, non-invasive treatment for bone metastases.
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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".