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Record W4411455621 · doi:10.1111/1753-0407.70116

Potential Significance of Targeting Ferroptosis for Intervention of Diabetic Cardiomyopathy

2025· review· en· W4411455621 on OpenAlexaff
Qian Lei, Burton B. Yang, Juanjuan Lyu

Bibliographic record

VenueJournal of Diabetes · 2025
Typereview
Languageen
FieldMedicine
TopicFerroptosis and cancer prognosis
Canadian institutionsUniversity of TorontoSunnybrook Health Science Centre
FundersFundamental Research Funds for the Central UniversitiesNational Health Commission of the People's Republic of ChinaNational Natural Science Foundation of China
KeywordsDiabetic cardiomyopathyMedicineOxidative stressDiabetes mellitusCardiomyopathyReactive oxygen speciesPathogenesisClinical significanceMechanism (biology)BioinformaticsCause of deathHeart failureInternal medicineEndocrinologyCell biologyDiseaseBiology

Abstract

fetched live from OpenAlex

BACKGROUND: Diabetes represents a significant global health concern, with diabetic cardiomyopathy (DCM) emerging as a primary cause of mortality among individuals with diabetes. Despite the prevalence of DCM, advancements in therapeutic and preventative strategies remain constrained. METHODS: Recent studies were reviewed to provide a comprehensive summary of pathogenesis and clinical treatment of DCM, effect of ferroptosis, and potential value of ferroptosis inhibitors in DCM intervention. RESULTS: A growing body of research indicates that oxidative stress, inflammatory reactions, and other factors play a role in the onset and progression of DCM. Oxidative stress within cardiomyocytes is a primary mechanism implicated in the development of DCM, whereby heightened intracellular reactive oxygen species (ROS) facilitate cell death via ferroptosis. Ferroptosis inhibitors hold great promise as therapeutic agents. CONCLUSIONS: This review provides an overview of the involvement of iron homeostasis regulation, oxidative stress, and ferroptosis in DCM, and the significance of ferroptosis in the prevention and treatment of DCM.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.833
Threshold uncertainty score0.938

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0010.000
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 head0.022
GPT teacher head0.318
Teacher spread0.296 · 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 designOther design
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

Citations4
Published2025
Admission routes1
Has abstractyes

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