Focused ultrasound-mediated drug delivery of bevacizumab in treating NF2-related schwannomatosis in an animal model
Bibliographic record
Abstract
Neurofibromatosis type 2 (NF2)-related schwannomatosis is an autosomal dominant genetic disorder characterized by the development of cranial and peripheral nerve schwannomas, including bilateral vestibular schwannomas which are associated with substantial morbidity. Current therapeutic strategies comprise surgical resection, radiotherapy, and systemic administration of bevacizumab; however, these approaches are frequently limited by high recurrence rates and morbidity. In this study, a transgenic murine model (Postn-Cre;Nf2flox/flox) was employed to investigate the combined effect of intraperitoneally administered human bevacizumab (5 mg/kg b. w.) and focused ultrasound (FUS) on drug delivery and tumor growth in DRG schwannomas. ELISA-based analysis demonstrated a 3.8-fold increase in intratumoral bevacizumab concentration following the combined application of human bevacizumab and FUS, compared to bevacizumab alone. Tumor growth was assessed using 7-T MRI of the spine with intravenous gadolinium contrast, and outcomes were compared across five experimental groups: (1) human bevacizumab alone, (2) murine bevacizumab alone, (3) human bevacizumab combined with FUS, (4) FUS alone, and (5) untreated controls. Treatments, including drug administration and FUS exposure, were performed three times at two-week intervals. Notably, the combined treatment group (human bevacizumab + FUS) exhibited a trend toward tumor size reduction.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".