Dual‐Action 3D Bioprinted Scaffolds with MXene Quantum Dots for Tumor Suppression and Breast Tissue Regeneration Postmastectomy
Bibliographic record
Abstract
Breast cancer remains among the most prevalent malignancies affecting women globally. Current treatment approaches, including mastectomy, chemotherapy, and radiotherapy, often fail to prevent cancer recurrence and can result in substantial tissue damage, esthetic concerns, and diminished quality of life. Three‐dimensional (3D) bioprinting, stem cell‐based technologies, and MXene nanomaterials show promise in tissue repair and cancer treatment. However, there is a lack of strategies that can offer multiple effects, preventing both breast tissue regeneration and tumor recurrence. In this study, we developed 3D hydrogel scaffolds incorporating stem cells and MXene quantum dots (MQDs) for in vivo application in a mouse model of breast cancer. We compared cellular, acellular, cellular MQD, and acellular MQD scaffolds transplanted into mouse after tumor resection and mastectomy. Notably, the acellular MQD group showed no tumor recurrence by day 14. It demonstrated superior tissue regeneration, confirmed by histological and immunostaining analyses. As a result, we offer a nanotechnological 3D scaffold based on hydrogel with dual functionality in preventing tumor recurrence and facilitating tissue regeneration. This innovative approach has the potential to revolutionize breast cancer treatment by reducing dependence on chemotherapy and radiotherapy. Thus, it offers a promising alternative for improving patient treatment outcomes.
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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".