Induced Breast Cancer Cell Apoptosis by Synchrotron-Based Irradiation with Monochromatic Microbeams
Bibliographic record
Abstract
Breast cancer is the most common female malignancy and has the second highest cancer related mortality in western women. Radiotherapy (RT) plays an important role in the modern breast cancer management of all stages. However, the efficiency of conventional RT continues to be limited because the treatment induces radio-toxicity in healthy tissues. In this work, we present the feasibility studies of the monochromatic micro-beam therapy (m-MRT) technique, a novel synchrotron based radiotherapy concept that uses high brilliance, monochromatic X-ray micro-beams smaller than 200 microns, applied to treat breast cancer tissue and cell samples. Two different energies were used for those tests: 50 keV and 100 keV. The tumor fragments and cells samples were irradiated ex-situ and then analyzed to assess the damages induced by m-MRT irradiation. Eight patient derived xenografted (PDX) tumor fragments were irradiated and implanted in live NOD Severe Combined Immuno-deficient (SCID) gamma (NSG) mice to assess the effect of irradiation on tumor growth comparing to the control. The pilot studies showed that the m-MRT treatment of cancerous tissue slowed down the tumor growth in (NSG) mice as compared to untreated controls. The biomolecule analysis demonstrated that the irradiation induced cancer cell apoptosis by triggering a stress response of the cells at radiation dose of 60 Gy or higher. Future studies will investigate how the cancer cells respond to the irradiation treatment in vivo in the live animals.
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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 | 0.000 |
| 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".