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Record W3011219960 · doi:10.3389/fphar.2020.00239

Ferroptosis and Its Potential Role in Human Diseases

2020· review· en· W3011219960 on OpenAlexaff
Yuanyuan Liu, Rongji Dai, Nafissa Ismail, Weijun Su, Bo Li

Bibliographic record

VenueFrontiers in Pharmacology · 2020
Typereview
Languageen
FieldMedicine
TopicFerroptosis and cancer prognosis
Canadian institutionsUniversity of Ottawa
FundersBeijing Institute of Technology Research Fund Program for Young ScholarsBeijing Institute of TechnologyNational Natural Science Foundation of China
KeywordsProgrammed cell deathDiseaseMechanism (biology)CancerCellCancer researchBiologyCell biologyApoptosisMedicinePathologyBiochemistryGenetics

Abstract

fetched live from OpenAlex

Ferroptosis is a novel regulated cell death pattern discovered when studying the mechanism of erastin-killing RAS mutant tumor cells in 2012. It is an iron-dependent programmed cell death pathway mainly caused by an increased redox imbalance but with distinct biological and morphology characteristics when compared to other known cell death patterns. Ferroptosis is associated with various diseases including acute kidney injury, cancer, and cardiovascular, neurodegenerative, and hepatic diseases. Moreover, activation or inhibition of ferroptosis using a variety of ferroptosis initiators and inhibitors can modulate disease progression in animal models. In this review, we provide a comprehensive analysis of the characteristics of ferroptosis, its initiators and inhibitors, and the potential role of its main metabolic pathways in the treatment and prevention of various diseased states. We end the review with the current knowledge gaps in this area to provide direction for future research on 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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.024
GPT teacher head0.349
Teacher spread0.324 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations277
Published2020
Admission routes1
Has abstractyes

Explore more

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