Creating a Favorable Microenvironment for Fat Grafting in a Novel Model of Radiation-Induced Mammary Fat Pad Fibrosis
Bibliographic record
Abstract
BACKGROUND: Radiofibrosis of breast tissue compromises breast reconstruction by interfering with tissue viability and healing. Autologous fat transfer may reduce radiotherapy-related tissue injury, but graft survival is compromised by the fibrotic microenvironment. Elevated expression of receptor for hyaluronan-mediated motility (RHAMM; also known as hyaluronan-mediated motility receptor, or HMMR) in wounds decreases adipogenesis and increases fibrosis. The authors therefore developed RHAMM peptide mimetics to block RHAMM profibrotic signaling following radiation. They propose that this blocking peptide will decrease radiofibrosis and establish a microenvironment favoring adipose-derived stem cell survival using a rat mammary fat pad model. METHODS: Rat mammary fat pads underwent a one-time radiation dose of 26 Gy. Irradiated (n = 10) and nonirradiated (n = 10) fat pads received a single intramammary injection of a sham injection or peptide NPI-110. Skin changes were examined clinically. Mammary fat pad tissue was processed for fibrotic and adipogenic markers using quantitative polymerase chain reaction and immunohistochemical analysis. RESULTS: Clinical assessments and molecular analysis confirmed radiation-induced acute skin changes and radiation-induced fibrosis in rat mammary fat pads. Peptide treatment reduced fibrosis, as detected by polarized microscopy of picrosirius red staining, increased collagen ratio of 3:1, reduced expression of collagen-1 crosslinking enzymes lysyl-oxidase, transglutaminase 2, and transforming growth factor β1 protein, and increased adiponectin, an antifibrotic adipokine. RHAMM was expressed in stromal cell subsets and was downregulated by the RHAMM peptide mimetic. CONCLUSION: Results from this study predict that blocking RHAMM function in stromal cell subsets can provide a postradiotherapy microenvironment more suitable for fat grafting and breast reconstruction.
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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 | 0.002 |
| 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) | 0.000 | 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".