Magnolol Traverses the Blood-brain Barrier and Enhances Temozolomide-induced Intrinsic Apoptosis of Glioblastoma Cells <i>via</i> a p53 Redox Signaling Mechanism
Bibliographic record
Abstract
BACKGROUND/AIM: , enhances TMZ-induced apoptosis in glioblastoma cells and explored the underlying mechanisms. MATERIALS AND METHODS: The safety of magnolol was evaluated in normal human astrocytes and mouse cerebral endothelial cells (CECs). Its ability to traverse the CEC tight junction barrier was examined, followed by assessments of its effects on TMZ-induced apoptosis in human and mouse glioblastoma cells. Mechanistic studies focused on p53 redox signaling. RESULTS: Magnolol at non-toxic concentrations crossed the CEC barrier and significantly potentiated TMZ-induced apoptosis in glioblastoma cells. Cotreatment decreased mitochondrial membrane potential, enhanced caspase-9 activity, and activated downstream caspases-3 and -6. Inhibition of caspase-9 or p53 phosphorylation attenuated magnolol's enhancement of TMZ-induced DNA fragmentation and apoptosis. Magnolol also elevated intracellular reactive oxygen species (ROS) levels, which further promoted p53 phosphorylation and apoptotic signaling. These synergistic effects were confirmed in both human U87MG and mouse GL261 glioblastoma cells. CONCLUSION: Magnolol traverses the CEC barrier and amplifies TMZ-induced apoptosis in glioblastoma cells through an intrinsic ROS-p53-caspase signaling cascade. Given its safety toward normal astrocytes and endothelial cells, magnolol represents a promising adjuvant strategy to improve TMZ-based therapy, potentially mitigating drug resistance and adverse effects in GBM treatment.
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 | 0.000 | 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".