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Record W4384197679 · doi:10.3389/fonc.2023.1206965

Epigenetics in myeloproliferative neoplasms

2023· review· en· W4384197679 on OpenAlexfundno aff
Graeme Greenfield, Mary Frances McMullin

Bibliographic record

VenueFrontiers in Oncology · 2023
Typereview
Languageen
FieldMedicine
TopicMyeloproliferative Neoplasms: Diagnosis and Treatment
Canadian institutionsnot available
FundersHealth Service ExecutiveWellcome TrustCanadian Institute for Theoretical Astrophysics
KeywordsEpigeneticsEpigenomeDNA methylationHistoneBiologymicroRNAMyelofibrosisCancer researchBioinformaticsEpigenomicsDiseaseMedicineGeneticsGeneImmunologyInternal medicineGene expression

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.961
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.105
GPT teacher head0.428
Teacher spread0.323 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations3
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueFrontiers in OncologySame topicMyeloproliferative Neoplasms: Diagnosis and TreatmentFrench-language works237,207