Down‐regulation of caveolin‐1 in glioma vasculature: Modulation by radiotherapy
Bibliographic record
Abstract
Primary brain tumors, particularly glioblastomas (GB), remain a challenge for oncology. An element of the malignant brain tumors' aggressive behavior is the fact that GB are among the most densely vascularized tumors. To determine some of the molecular regulations occuring at the brain tumor endothelium level during tumoral progression would be an asset in understanding brain tumor biology. Caveolin-1 is an essential structural constituent of caveolae that has been implicated in mitogenic signaling, oncogenesis, and angiogenesis. In this work we investigated regulation of caveolin-1 expression in brain endothelial cells (ECs) under angiogenic conditions. In vitro, brain EC caveolin-1 is down-regulated by angiogenic factors treament and by hypoxia. Coculture of brain ECs with tumoral cells induced a similar down-regulation. In addition, activation of the p42/44 MAP kinase is demonstrated. By using an in vivo brain tumor model, we purified ECs from gliomas as well as from normal brain to investigate possible regulation of caveolin-1 expression in tumoral brain vasculature. We show that caveolin-1 expression is strikingly down-regulated in glioma ECs, whereas an increase of phosphorylated caveolin-1 is observed. Whole-brain radiation treatment, a classical way in which GB is currently being treated, resulted in increased caveolin-1 expression in tumor isolated ECs. The level of tumor cells spreading around newly formed blood vessels was also elevated. The regulation of caveolin-1 expression in tumoral ECs may reflect the tumoral vasculature state and correlates with angiogenesis kinetics.
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".