P.138 A small RNA signature from extracellular vesicles in patient plasma correlates with recurrence or progression of high-grade gliomas
Bibliographic record
Abstract
Background: While managing patients with high-grade gliomas (HGG), predicting recurrence, or differentiating between pseudoprogression (radiation necrosis) and true tumour progression would be invaluable in improving overall prognosis. Characterizing small RNA (sRNA) expression profiles from plasma-derived extracellular vesicles (EVs) over the course of a patient’s treatments, may allow for patient-specific treatment modifications and improve outcomes. Methods: EVs were isolated using Vn96 capture from plasma obtained longitudinally from HGG patients perioperatively and with routine, follow-up surveillance imaging. sRNA was enriched from the EVs, upon which sequencing and unsupervised hierarchal clustering of sRNA signatures were completed. Expression profiles were grouped longitudinally with the clinical status of patients. Results: Cluster analysis of sequences from nine HGG patients, has revealed a sRNA signature that is able to distinguish between tumours showing evidence of progression and those remaining stable over time. Those samples obtained from patients where a clinical diagnosis of tumour progression or pseudoprogression were uncertain, were found to cluster into progression vs. stable signatures. Clinical follow up of these patients will reveal the predictive value of these identified clusters. Conclusions: These preliminary findings demonstrate the potential utility of small RNA profiling of EVs obtained from patients with high-grade gliomas as non-invasive biomarkers for recurrent/progressive disease or stability/pseudoprogression.
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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) | 0.000 | 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) | 0.003 | 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".