Extracellular Vesicles as Conduits of Non-Coding RNA Emission and Intercellular Transfer in Brain Tumors
Bibliographic record
Abstract
Non-coding RNA (ncRNA) species have emerged in as molecular fingerprints and regulators of brain tumor pathogenesis and progression. While changes in ncRNA levels have been traditionally regarded as cell intrinsic there is mounting evidence for their extracellular and paracrine function. One of the key mechanisms that enables ncRNA to exit from cells is their selective packaging into extracellular vesicles (EVs), and trafficking in the extracellular space and biofluids. Vesicular export processes reduce intracellular levels of specific ncRNA in EV donor cells while creating a pool of EV-associated ncRNA in the extracellular space and biofluids that enables their uptake by other recipient cells; both aspects have functional consequences. Cancer cells produce several EV subtypes (exosomes, ectosomes), which differ in their ncRNA composition, properties and function. Several RNA biotypes have been identified in the cargo of brain tumor EVs, of which microRNAs are the most studied, but other species (snRNA, YRNA, tRNA, and lncRNA) are often more abundant. Of particular interest is the link between transforming oncogenes and the biogenesis, cargo, uptake and function of tumor-derived EV, including EV content of oncogenic RNA. The ncRNA repertoire of EVs isolated from cerebrospinal fluid and serum is being developed as a liquid biopsy platform in 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".