Pre-irradiation of mouse mammary gland stimulates cancer cell migration and development of lung metastases
Bibliographic record
Abstract
BACKGROUND: In most patients with breast cancer, radiotherapy induces inflammation that is characterised by an increase of promigratory factors in healthy tissues surrounding the tumour. However, their role in the emergence of the migration phenotype and formation of metastases is still unclear. METHODS: A single mammary gland of BALB/c mice was irradiated with four doses of 6 Gy given at a 24-h interval. After the last session of irradiation, treated and control mammary glands were either collected for quantification of promigratory and proinflammatory factors or were implanted with fluorescent ubiquitination-based cell cycle indicator (FUCCI)-expressing mouse mammary cancer D2A1 cells. The migration of cancer cells in the mammary glands was monitored by optical imaging. On day 21, mammary tumours and lungs were collected for histology analyses and the quantification of metastases. RESULTS: Pre-irradiation of the mammary gland increased by 1.8-fold the migration of cancer cells, by 2-fold the quantity of circulating cancer cells and by 2.4-fold the number of lung metastases. These adverse effects were associated with the induction of interleukin-6 (IL-6) and cyclooxygenase-2 (COX-2). CONCLUSION: The emergence of the metastasis phenotype is believed to be associated with the accumulation of mutations in cancer cells. Our results suggest an alternative mechanism based on promigratory factors from irradiated mammary glands. In clinic, the efficiency of radiotherapy could be improved by anti-inflammatory agents that would prevent the stimulation of cancer cell migration induced by radiation.
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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) | 0.001 | 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) | 0.003 | 0.001 |
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".