Optimization of Ultrasound-mediated Anti-angiogenic Cancer Gene Therapy
Bibliographic record
Abstract
Ultrasound-targeted microbubble destruction (UTMD) can be used to deliver silencing gene therapy to tumors. We hypothesized that UTMD would be effective in suppressing angiogenesis within tumors, and that modulation of the ultrasound pulsing intervals (PI) during UTMD would affect the magnitude of target knockdown. We performed UTMD of vascular endothelial growth factor receptor-2 (VEGFR2) short hairpin (sh)RNA plasmid in an heterotopic mammary adenocarcinoma model in rats, evaluating PIs of 2, 5, 10, and 20 seconds. We demonstrated that UTMD with a PI of 10 seconds resulted in the greatest knockdown of VEGFR2 by PCR, immunostaining, western blotting, smaller tumor volumes and perfused areas, and lower tumor microvascular blood volume (MBV) and flow by contrast-enhanced ultrasound (CEU) compared with UTMD-treated tumors at 2, 5, and 20 seconds, control tumors, tumors treated with intravenous shRNA plasmid and scrambled plasmid. CEU perfusion assessment using the therapeutic probe demonstrated that tumors were fully replenished with microbubbles within 10 seconds, but incompletely replenished at PI-2 and PI-5 seconds. In conclusion, for anti-VEGFR2 cancer gene therapy by UTMD, PI of 10 seconds results in higher target knockdown and a greater anti-angiogenic effect. Complete replenishment of tumor vasculature with silencing gene-bearing microbubbles in between destructive pulses of UTMD is required to maximize the efficacy of anti-angiogenic cancer gene therapy.Molecular Therapy - Nucleic Acids (2013) 2, e94; doi:10.1038/mtna.2013.20; published online 21 May 2013.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".