Irradiation of normal mouse tissue increases the invasiveness of mammary cancer cells
Bibliographic record
Abstract
PURPOSE: Treatment of breast tumours frequently involves irradiating the whole breast to reach malignant microfoci scattered throughout the breast. In this study, we determined whether irradiation of normal tissues could increase the invasiveness of breast cancer cells in a mouse model. MATERIALS AND METHODS: Non-irradiated MC7-L1 mouse mammary carcinoma cells were injected subcutaneously in irradiated and non-irradiated thighs of Balb/c mice. The invasion volume, tumour volume, blood vessel permeability and interstitial volumes were monitored by magnetic resonance imaging (MRI). Slices of normal tissue invaded by cancer cells were examined by histology. Activity of matrix metalloproteinase -2 and -9 (MMP -2 and -9) in healthy and irradiated tissues was determined, and the proliferation index of the invading cancer cells was evaluated. RESULTS: Three weeks after irradiation, enhancement of MC7-L1 cells invasiveness in irradiated thighs was already detected by MRI. The tumour invasion volume continued to extend 28- to 37-fold compared to the non-irradiated implantation site for the following three weeks, and it was associated with an increase of MMP-2 and -9 activities in healthy tissues. The interstitial volume associated with invading cancer cells was significantly larger in the pre-irradiated sites; while the blood vessels permeability was not altered. Cancer cells invading the healthy tissues were proliferating at a lower rate compared to non-invading cancer cells. CONCLUSION: Implantation of non-irradiated mammary cancer cells in previously irradiated normal tissue enhances the invasive capacity of the mammary cancer cells and is associated with an increased activity of MMP-2 and -9 in the irradiated normal tissue.
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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 | 0.001 | 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 | 0.000 |
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".