To examine the mechanism through which adipose-derived stem cell affect healthy and malignant breast epithelial cell proliferation
Bibliographic record
Abstract
Mastectomies are commonly performed procedures to increase the life expectancy of women suffering from breast cancer. Stromal Vascular Fraction (SVF) from patient's abdominal fat is often utilized to supplement fat grafts used in breast reconstructions. Unfortunately, very little is known about the interaction of SVF with residual cancer cells as well as surrounding breast tissue. This study investigates if secreted factors from the SVF cells affect proliferation of residual breast cancer cells and the surrounding breast cells. Methods: The effects of patient-derived SVF samples on the proliferation of estrogen receptor positive (ER+) MCF-7 breast cancer cells, tissue adjacent to ER+ breast tumours (TAT), and healthy breast cells from reduction mammoplasty samples (HBT) was examined. The secretion profile of 41 different cytokines in the conditioned media (CM) of SVF samples grown in co-cultures with the MCF-7, TAT, or HBT cells was compared to the CM of each cell type grown alone. Results: Placing MCF-7 cells in co-cultures with SVF cells led to a 1.53-fold (P<().005) increase in their proliferation. Interestingly, CM obtained from the co-cultures of SVF+MCF-7 cells was sufficient to increase MCF-7 cell proliferation by 1.5 fold (p<0.05), indicating that secreted factors in this CM have pro-proliferative properties on cancer cells. The cytokine array identified increased secretion of a number of cytokines that were uniquely elevated in the co-cultures of SVF with MCF, TAT or HBT cells but not in the individual cell cultures. From the cytokine array data, we confirmed that IL-1 ~.MDC and RANTES cytokines were able to increase MCF-7 cell proliferation independently.Conclusion:Secreted factors from SVF+MCF-7 co-cultures have proproliferative effects on MCF-7 cells. This study is a proof of concept that examining secretion profiles of SVF cocultures can lead to the discovery of cytokines which are able to modulate breast cancer cell proliferation.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".