The Treatment of Malignant Meningioma with Verotoxin
Bibliographic record
Abstract
Malignant meningiomas (MMs) are aggressive intracranial neoplasms with a 75% 5-year recurrence rate. Verotoxin 1 (VT1) is an Escherichia coli toxin, which has recently been shown to have anti-neoplastic action by targeting the globotriosylceramide (Gb(3)) glycolipid on tumor cells and tumor neovasculature. To investigate the potential use of VT1 as a clinical agent for MM, we initially tested 16 meningiomas for Gb(3) expression. Nine of 11 MMs (82%), but only one of five benign meningiomas (20%), were positive for Gb(3). An orthotopic xenograft model was used to test the efficacy of VT1 treatment for MM. We first demonstrated that Gb(3) was highly expressed by the MM cell line, IOMM-Lee, and that this cell line was highly sensitive to VT1 treatment in vitro. A single intratumoral injection of VT1 significantly improved survival in nude mice harboring intracranial tumours (P<.0001). Factor-eight immunostaining of tumours harvested from VT1-treated animals revealed a marked reduction in the tumour microvascular density. In addition, the tumors of VT1-treated animals displayed increased apoptosis by TUNEL analysis and showed a significant decrease in cell proliferation, as determined by MIB-5 immunostaining. VT1 treatment of MM is effective in our orthotopic xenograft model, and warrants further exploration as a potential treatment for these highly anaplastic and aggressive neoplasms.
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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) | 0.000 | 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".