Ultrasound-stimulated microbubbles enhanced vascular disruption in fractionated radiotherapy-treated tumours via ASMase activation
Bibliographic record
Abstract
Recent studies have indicated that radiotherapy affects tumour vasculature as well as tumour cells. The use of ultrasound-stimulated microbubbles (USMB) can potentially enhance the effects of radiotherapy through the activation of the acid sphingomyelinase [ASMase or sphingomyelin phosphodiesterase 1 (SMPD1)]-ceramide pathway. ASMase knockout (ASMase-/-) and wild-type (WT) mice bearing fibrosarcoma (MCA/129 tumour line) were treated with 10 Gy or 20 Gy in five fractions alongside or independently of USMB treatments. The results indicated that tumour responses to fractionated radiotherapy (fXRT) were enhanced when fXRT was coupled with USMB as part of the treatment regimen. Sphingosine-1-phosphate (S1P)-treated mice and ASMase-/- mice demonstrated radioresistance against fXRT alone, whereas only ASMase-/- mice showed radioresistance against fXRT treatment alone and when combined with USMB. Results indicated that in WT and S1P-treated cohorts, the use of USMB with fXRT enhanced the tumour response compared to use of USMB or fXRT alone. Although in WT and S1P-treated cohorts, there was enhanced vascular disruption, ASMase-/- cohorts demonstrated no significant vascular disruption, indicating the importance of ASMase in facilitating vascular changes in response to fXRT and USMB treatment.
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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".