Ultrasound-stimulated microbubble enhanced low-dose radiation response
Bibliographic record
Abstract
We have recently demonstrated that mechanical perturbation of endothelial cells from ultrasound-stimulated microbubbles (USMB) results in enhanced tumor radiosensitivity at low 2 Gy doses of radiation. Our hypothesis is that USMB-based endothelial membrane perturbations produce ceramide via a sphingomyelinase (ASMase) pathway, and act synergistically with radiation to enhance overall tumor response. Here, we investigate the role of the SMase-ceramide pathway on USMB-based endothelial radiosensitization. Experiments were carried out in wild type (C57BL/6) and ASMase knockout mice, implanted with a fibrosarcoma line (MCA-129). Animals were treated with radiation doses varying from 0 to 8 Gy alone, or in combination with ultrasound-stimulated microbubbles. Treatment response was assessed with Doppler ultrasound vascularity index acquired at 3, 24, and 72 hrs using a VEVO770 preclinical ultrasound system. Staining using ISEL, ceramide, and CD31 immunohistochemistry of tumor sections was used to complement results. In contrast to wild type animals, ASMase knockout mice, or wild-type mice receiving S1P, were found to be generally resistant to the anti-vascular effects of radiation and USMB. Minimal cell death and no vascular shutdown was observed following treatments in those experimental groups. Overall conclusions drawn from this work suggest a mechanotransduction-like effect that results in endothelial radiosensitization.
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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) | 0.002 | 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".