Translational applications of microRNA genes in medulloblastomas
Bibliographic record
Abstract
PURPOSE: To provide a critical assessment of the clinical translational applications of microRNA (miRNA) genes in medulloblastomas. METHODS: Data were obtained from MEDLINE using Boolean-formatted keyword queries. Top articles were selected for critical analyses - depending on the novelty of findings, qualitative assessment of the citation index and relevance to the diagnosis, prognosis and therapeutic targeting of medulloblastomas. RESULTS: MiRNAs, non-protein-coding RNA molecules, negatively regulate gene expression in a sequence-specific manner during biological processes. In the past few years, miRNA genes have emerged as key regulators of not only molecular events involved in normal brain development and function but also in the molecular pathogenesis of medulloblastomas. In this manner, microRNA genes are identified with functional roles as oncogenes and tumor suppressor genes. At least four miRNAs have proven useful in improving the molecular classification of medulloblastomas, and eight others have shown potential in predicting patients' overall prognosis. Moreover, more than 10 miRNA genes can be potentially utilized in therapies against medulloblastomas, using nine recent methods of targetting miRNAs. CONCLUSION: The quest to identify miRNA genes that are of biological significance in medulloblastomas is on an ongoing venture. Most importantly, these miRNAs have been shown to be of clinical importance for improving the accuracy of diagnosis and prognosis and even developing therapies that can significantly improve patients' overall survival from this deadly disease.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".