Reduced Glioma Growth Following Dexamethasone or Anti‐Angiopoietin 2 Treatment
Bibliographic record
Abstract
All patients with glioblastoma, the most aggressive and common form of brain cancer, develop cerebral edema. This complication is routinely treated with dexamethasone, a steroidal anti-inflammatory drug whose effects on brain tumors are not fully understood. Here we show that dexamethasone can reduce glioma growth in mice, even though it depletes infiltrating T cells with potential antitumor activity. More precisely, T cells with helper or cytotoxic function were sensitive to dexamethasone, but not those that were negative for the CD4 and CD8 molecules, including gammadelta and natural killer (NK) T cells. The antineoplastic effect of dexamethasone was indirect, as it did not meaningfully affect the growth and gene expression profile of glioma cells in vitro. In contrast, hundreds of dexamethasone-modulated genes, notably angiopoietin 2 (Angpt2), were identified in cultured cerebral endothelial cells by microarray analysis. The ability of dexamethasone to attenuate Angpt2 expression was confirmed in vitro and in vivo. Selective neutralization of Angpt2 using a peptide-Fc fusion protein reduced glioma growth and vascular enlargement to a greater extent than dexamethasone, without affecting T cell infiltration. In conclusion, this study suggests a mechanism by which dexamethasone can slow glioma growth, providing a new therapeutic target for malignant brain 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".