Comparative effects of focused ultrasound and microbubbles on healthy and tumor bearing rat spinal cord
Bibliographic record
Abstract
This study examined the differential sensitivity of intramedullary spinal cord tumors (IMSCTs) and healthy tissue to focused ultrasound (FUS) and microbubbles in a rat model of IMSCT. F98 glioma cells were injected into spinal cords of F344 rats. FUS (580 kHz, 10 ms bursts, 1 Hz pulse repetition frequency, 40 s) was delivered to tumor and adjacent healthy tissue at varying pressures (0–1.2 MPa) following intravenous injection of microbubbles (1.00 ± 0.85 µm; 2.4 × 10 7 microbubbles/100 g). Tissues were collected 24 h post-treatment for histological analysis. Healthy tissue exhibited pressure-dependent damage, including significant differences in red blood cell (RBC) extravasation between 0 and 1.2 MPa conditions (0 ± 0 vs 1.73 × 10 5 ± 2.15 × 10 5 , p = 0.015), hemorrhagic pools, and tissue disintegration. Conversely, the presence of histopathological features in tumors, regardless of pressure, and no significant differences in RBC extravasation areas between exposure conditions suggests no treatment-induced damage at the tested exposures. These findings indicate F98 gliomas are less sensitive to FUS and microbubbles than healthy spinal cord, likely due to reduced vascularity ( p < 0.00001 compared to grey matter, p < 0.05 compared to white matter). This finding indicates alternative strategies (e.g. nanodroplets or molecularly-targeted bubbles) must be explored for effectively treating CNS tumors with low vascularity.
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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".