Dexamethasone Attenuates X-Ray-Induced Activation of the Autotaxin-Lysophosphatidate-Inflammatory Cycle in Breast Tissue and Subsequent Breast Fibrosis
Bibliographic record
Abstract
We recently showed that radiation-induced DNA damage in breast adipose tissue increases autotaxin secretion, production of lysophosphatidate (LPA) and expression of LPA1/2 receptors. We also established that dexamethasone decreases autotaxin production and LPA signaling in non-irradiated adipose tissue. In the present study, we showed that dexamethasone attenuated the radiation-induced increases in autotaxin activity and the concentrations of inflammatory mediators in cultured human adipose tissue. We also exposed a breast fat pad in mice to three daily 7.5 Gy fractions of X-rays. Dexamethasone attenuated radiation-induced increases in autotaxin activity in plasma and mammary adipose tissue and LPA1 receptor levels in adipose tissue after 48 h. DEX treatment during five daily fractions of 7.5 Gy attenuated fibrosis by ~70% in the mammary fat pad and underlying lungs at 7 weeks after radiotherapy. This was accompanied by decreases in CXCL2, active TGF-β1, CTGF and Nrf2 at 7 weeks in adipose tissue of dexamethasone-treated mice. Autotaxin was located at the sites of fibrosis in breast tissue and in the underlying lungs. Consequently, our work supports the premise that increased autotaxin production and lysophosphatidate signaling contribute to radiotherapy-induced breast fibrosis and that dexamethasone attenuated the development of fibrosis in part by blocking this process.
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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) | 0.001 | 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".