Targeted Anti-Cancer Provascular Therapy Using Ultrasound-Stimulated Microbubbles
Bibliographic record
Abstract
Hypoxia is a recognized mechanism of resistance to radiation therapy in various solid tumors. It has recently been shown that ultrasound (US) stimulated microbubbles (MBs) cavitation can increase blood perfusion in muscles, by triggering the signaling of nitric oxide. We hypothesized that Ultrasound Targeted Microbubbles Cavitation (UTMC) can radiosensitize solid tumors by increasing blood perfusion and thus reduce tumor hypoxia. A therapeutic transducer (1 MHz, A303S, Olympus) was used to transmit long US pulses (5000 cycles) to the tissue of interest, during the injection of MBs (Definity, Lantheus) via the tail vein (2-5 μL/min). Different US pressure values were tested, ranging from 125 to 750 kPa. UTMC consisted of 60 therapeutic pulses, given at a pulse interval adjusted to allow microbubble replenishment as guided by US contrast imaging (CPS 7MHz, 15L8 probe, Sequoia, Siemens), typically given in about 15 mins. The increase in perfusion in the muscle and in the tumor was quantified by burst replenishment imaging allowing longitudinal quantification of blood perfusion (A×B). In muscle, the increase in blood perfusion following various UTMC treatments was strong and very rapid. For all pulses tested, perfusion increased from 0.5 ± 0.2 dB/s before treatment to 5.4 ± 1.4 dB/s after treatment and typically lasted for at least 10 after UTMC treatment. In the periphery of tumors, there was a significant difference (*** p3[+++ p3. The difference between small and big tumors was also significant (*** p<; 0.005, T-Test). The increase in perfusion persisted for 10 to 25 minutes post therapy, which may be sufficient for radiotherapy treatment. These results suggest that it is possible to increase tumor blood perfusion by UTMC in solid tumors.
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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".