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Record W4391844349 · doi:10.3390/biology13020122

Insights into the Roles of Epigenetic Modifications in Ferroptosis

2024· article· en· W4391844349 on OpenAlexaff
Jinghua Kong, Hao Lyu, Qian Ouyang, Hao Shi, Rui Zhang, Shuai Xiao, Dong Guo, Qi Zhang, Xing‐Zhen Chen, Cefan Zhou, Jingfeng Tang

Bibliographic record

VenueBiology · 2024
Typearticle
Languageen
FieldMedicine
TopicFerroptosis and cancer prognosis
Canadian institutionsUniversity of Alberta
FundersWuhan Science and Technology ProjectNational Key Research and Development Program of ChinaNational Natural Science Foundation of China
KeywordsEpigeneticsBiologyDNA methylationHistoneLipid peroxidationCancer researchCell biologyGeneticsDNAGeneBiochemistryGene expressionOxidative stress

Abstract

fetched live from OpenAlex

, ROS, and lipid peroxidation. Studies have shown that ferroptosis is related to the development of multiple diseases, such as cancer, neurodegenerative diseases, and acute myeloid leukemia. Ferroptosis plays a dual role in the occurrence and development of these diseases. Ferroptosis mainly involves iron metabolism, ROS, and lipid metabolism. Various mechanisms, including epigenetic regulation, have been reported to be deeply involved in ferroptosis. Abnormal epigenetic modifications have been reported to promote tumor onset or other diseases and resistance to chemotherapy drugs. In recent years, diversified studies have shown that epigenetic modification is involved in ferroptosis. In this review, we reviewed the current resistance system of ferroptosis and the research progress of epigenetic modification, such as DNA methylation, RNA methylation, non-coding RNAs, and histone modification in cancer and other diseases by regulating ferroptosis.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.352
Threshold uncertainty score0.133

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.031
GPT teacher head0.312
Teacher spread0.281 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations11
Published2024
Admission routes1
Has abstractyes

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