Ultrasound-stimulated microbubble enhancement of radiation treatments: endothelial cell function and mechanism
Bibliographic record
Abstract
Endothelial cell death caused by novel microbubble-enhanced ultrasound cancer therapy leads to secondary tumour cell death. In order to characterize and optimize these treatments, the molecular mechanisms resulting from the interaction with endothelial cells were investigated here. Endothelial cells (HUVEC) were treated with ultrasound-stimulated microbubbles (US/MB), radiation (XRT), or a combination of US/MB+XRT. Effects on cells were evaluated at 0, 3, 6, and 24 hours after treatment. Experiments took place in the presence of modulators of sphingolipid-based signalling including ceramide, fumonisin B1, monensin, and sphingosine-1-phosphate. Experimental outcomes were evaluated using histology, TUNEL, clonogenic survival methods, immuno-fluorescence, electron microscopy, and endothelial cell blood-vessel-like tube forming assays. Fewer cells survived after treatment using US/MB+XRT compared to either the control or XRT. The functional ability to form tubes was only reduced in the US/ MB+XRT condition in the control, the ceramide, and the sphingosine-1-phosphate treated groups. The combined treatment had no effect on tube forming ability in either the fumonisin B1 or in the monensin exposed groups, since both interfere with ceramide production at different cellular sites. In summary, experimental results supported the role of ceramide signalling as a key element in cell death initiation with treatments using US/MB+XRT to target endothelial cells.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".