A narrow microbeam is more effective for tumor growth suppression than a wide microbeam: an<i>in vivo</i>study using implanted human glioma cells
Bibliographic record
Abstract
The tumoricidal mechanisms of microbeam radiation therapy, and the more recently proposed minibeam radiation therapy, for the treatment of brain tumors are as yet unclear. Moreover, from among the various parameters of beam geometry the impact of changing the beam width is unknown. In this study, suppression of tumor growth in human glioma cells implanted in mice was evaluated experimentally using microbeams of two different widths: a conventional narrow beam (20 µm width, 100 µm center-to-center distance) and a wide beam (100 µm width, 500 µm center-to-center distance). The tumor growth ratio was compared and acute cell death was studied histologically. With cross-planar irradiation, tumor growth was significantly suppressed between days 4 and 28 after 20 µm microbeam irradiation, whereas tumor growth was suppressed, and not significantly so, only between days 4 and 18 after 100 µm microbeam irradiation. Immunohistochemistry using TUNEL staining showed no increase in TUNEL-positive cells with either microbeam at 24 and 72 h post-irradiation. The 20 µm microbeam was found to be more tumoricidal than the 100 µm microbeam, and the effect was not related to apoptotic cell death. The underlying mechanism may be functional tissue deterioration rather than direct cellular damage in the beam path.
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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 | 0.000 |
| 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".