Exploring the Potential of PEG‐Heparin Hydrogels to Support Long‐Term Ex Vivo Culture of Patient‐Derived Breast Explant Tissues
Bibliographic record
Abstract
Breast cancer is a complex, highly heterogenous, and dynamic disease and the leading cause of cancer-related death in women worldwide. Evaluation of the heterogeneity of breast cancer and its various subtypes is crucial to identify novel treatment strategies that can overcome the limitations of currently available options. Explant cultures of human mammary tissue have been known to provide important insights for the study of breast cancer structure and phenotype as they include the context of the surrounding microenvironment, allowing for the comprehensive exploration of patient heterogeneity. However, the major limitation of currently available techniques remains the short-term viability of the tissue owing to loss of structural integrity. Here, an ex vivo culture model using star-shaped poly(ethylene glycol) and maleimide-functionalized heparin (PEG-HM) hydrogels to provide structural support to the explant cultures is presented. The mechanical support allows the culture of the human mammary tissue for up to 3 weeks and prevent disintegration of the cellular structures including the epithelium and surrounding stromal tissue. Further, maintenance of epithelial phenotype and hormonal receptors is observed for up to 2 weeks of culture which makes them relevant for testing therapeutic interventions. Through this study, the importance of donor-to-donor variability and intra-patient tissue heterogeneity is reiterated.
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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) | 0.001 | 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 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".