BIOM-33. TRNA FRAGMENTS IN PLASMA EXTRACELLULAR VESICLES FROM HIGH GRADE GLIOMA PATIENTS, POTENTIAL BIOMARKERS?
Bibliographic record
Abstract
Abstract Transfer RNA (tRNA) are non-coding RNAs responsible for bringing the amino acids to the ribosome during translation. Recent studies in cancer have shown that fragments (tRFs) derived from tRNA cleavage serve regulatory roles in epigenetics, signalling and translation. tRFs are also released from cells bound to proteins and can be found in extracellular vesicles to perform some aspects of paracrine signalling. Utilizing high throughput small RNA sequencing we have identified over 750 uniquely or multi-mapped (6.9% of total sRNA mapped reads) tRNA and tRFs in extracellular vesicles of high-grade glioma patient plasma (IDH-WT). Consistent with novel literature in other cancers there was a general trend to decreased expression of tRNA and tRFs (n = 76 with FDR < 0.05) in high grade glioma plasma EVs compared to controls. However, further analysis of tRFs reveals a much more complex story, as fragment distribution differs between cancer and controls. tRNAs and tRFs may serve as important biomarkers and regulators with therapeutic potential in the future.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".