Bibliographic record
Abstract
Adult diffuse gliomas account for the majority of primary malignant brain tumours, and are in most cases lethal. Current therapies are often only marginally effective, and improved options will almost certainly benefit from further insight into the various processes contributing to gliomagenesis and pathology. While molecular characterization of these tumours classifies them on the basis of genetic alterations and chromosomal abnormalities, DNA methylation patterns are increasingly understood to play a role in glioma pathogenesis. Indeed, a subset of gliomas associated with improved survival is characterized by the glioma CpG island methylator phenotype (G-CIMP), which can be induced by the expression of mutant isocitrate dehydrogenase (IDH1/2). Aberrant methylation of particular genes or regulatory elements, within the context of G-CIMP-positive and/or negative tumours, has also been shown to be associated with differential survival. In this review, we provide an overview of the current knowledge regarding the role of DNA methylation in adult diffuse gliomas. In particular, we discuss IDH mutations and G-CIMP, MGMT promoter methylation, DNA methylation-mediated microRNA regulation and aberrant methylation of specific genes or groups of genes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".