The Emerging Role of Extracellular Vesicles in the Glioma Microenvironment: Biogenesis and Clinical Relevance
Bibliographic record
Abstract
Gliomas are a diverse group of brain tumors comprised of malignant cells ('tumor' cells) and non-malignant 'normal' cells, including neural (neurons, glia), inflammatory (microglia, macrophage) and vascular cells. Tumor heterogeneity arises in part because, within the glioma mass, both 'tumor' and 'normal' cells secrete factors that form a unique microenvironment to influence tumor progression. Extracellular vesicles (EVs) are critical mediators of intercellular communication between immediate cellular neighbors and distantly located cells in healthy tissues/organs and in tumors, including gliomas. EVs mediate cell-cell signaling as carriers of nucleic acid, lipid and protein cargo, and their content is unique to cell types and physiological states. EVs secreted by non-malignant neural cells have important physiological roles in the healthy brain, which can be altered or co-opted to promote tumor progression and metastasis, acting in combination with glioma-secreted EVs. The cell-type specificity of EV content means that 'vesiculome' data can potentially be used to trace the cell of origin. EVs may also serve as biomarkers to be exploited for disease diagnosis and to assess therapeutic progress. In this review, we discuss how EVs mediate intercellular communication in glioma, and their potential role as biomarkers and readouts of a therapeutic response.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".