Extracellular vesicles as prospective carriers of oncogenic protein signatures in adult and paediatric brain tumours
Bibliographic record
Abstract
Extracellular vesicles (EVs), including exosomes, act as biological effectors and as carriers of oncogenic signatures in human cancer. The molecular composition and accessibility of EVs in biofluids open unprecedented diagnostic opportunities in malignancies where tumour tissue is difficult to sample, especially in primary and metastatic brain tumours. The ongoing genetic discovery of driver mutations defines the ever increasing numbers of distinct molecular subtypes of brain tumours (orphan diseases), a complexity that may soon be translated into alterations in functional proteins and their oncogenic networks. This may likely be extended to real time changes engendered by the disease progression, tumour heterogeneity, inter-individual variations and therapeutic responses. Meeting these challenges through EV analysis is dependent on technological progress in such areas as generation of mutation- and phospho-specific antibodies, antibody array platforms, nanotechnology, microfluidics, NMR spectroscopy, MS and MRM approaches of quantitative proteomics, which should not be underestimated. Still, vesiculation emerges as a unique process that could be harnessed for the benefit of more individualised patient care.
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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 | 0.001 | 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 | 0.001 |
| 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 | 0.002 |
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".