Bibliographic record
Abstract
The myeloproliferative neoplasms (MPNs) are a group of acquired clonal disorders where mutations drive proliferative disease resulting in increased blood counts and in some cases end-stage myelofibrosis. Epigenetic changes are the reversible modifications to DNA- and RNA-associated proteins that impact gene activity without changing the DNA sequence. This review summarizes mechanisms of epigenetic changes and the nucleosome. The drivers and epigenetic regulators in MPNs are outlined. In MPNs, distinct patterns of epigenetic dysregulation have been seen in chronic and in advanced phases. Methylation age and histone modification are altered in MPNs and by further treatment. The alterations found in methylation age in MPNs and with treatment are discussed, and the changes in histone modification with Janus kinase (JAK) inhibition are evaluated. Currently available therapeutic areas where the epigenome can be altered are outlined. Thus, we review the current knowledge and understanding of epigenetics in MPN and consider further management options. Understanding the epigenome and its alteration in MPNs and epigenetic changes associated with the progression of disease will lead to advances in therapeutic options.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".