Are we better together? Addressing a combined treatment of pitavastatin and temozolomide for brain cancer
Bibliographic record
Abstract
Pitavastatin is commonly prescribed to treat hypercholesterolemia through the regulation of cholesterol biosynthesis. Interestingly, it has also demonstrated a great potential for treating brain tumors, although the detailed cytotoxic mechanism, particularly in glioblastoma, remains incompletely understood. This work explores the activity of pitavastatin in 2D and 3D glioblastoma models, in an attempt to provide a more representative and robust overview of its anticancer potential in glioblastoma. The results show that not only is pitavastatin 10-1000 times-fold more effective in reducing tumoral metabolic activity than temozolomide, but also demonstrate a synergistic activity with this alkylating drug. In addition, low micromolar concentrations of this statin strongly impair the growth and the invasion ability of multicellular tumor spheroids. The obtained qRT-PCR and proteomics data highlight the modulation of cell death via apoptosis (BAX/BCL2, CASP9) and autophagy (BECN1, BNIP3, BNIP3L and LC3B), as well as an epithelial to mesenchymal transition blockage (HTRA1, SERPINE1, WNT5A, ALDH3B1 and EPHA2) and remodeling of the extracellular matrix (VCAN, SERPINE1 and TGFBI). Overall, these results lay the foundation for further investigations on the potential combinatory clinical treatment with temozolomide.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".