Evidence map›Paper›PMID 38613722›Full record

ReviewMolecular biotechnology2025

Deciphering Ferroptosis: From Molecular Pathways to Machine Learning-Guided Therapeutic Innovation.

Megha Mete, Amiya Ojha, Priyanka Dhar, Deeplina Das

Abstract readReview
PubMed Publisher
In one paragraph

Review in Molecular biotechnology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.3field-weighted citation impact, top 21% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 3 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 2 institutions in 1 country.

Megha Mete *Department of Bioengineering, National Institute of Technology Agartala, Agartala, Tripura, 799046, India.ORCID http://orcid.org/0009-0000-8379-8078
Amiya Ojha *Department of Bioengineering, National Institute of Technology Agartala, Agartala, Tripura, 799046, India.ORCID http://orcid.org/0009-0003-1084-6472
Priyanka DharCSIR-Indian Institute of Chemical Biology, Kolkata, 700032, India.ORCID http://orcid.org/0009-0000-5060-4150
Deeplina DasDepartment of Bioengineering, National Institute of Technology Agartala, Agartala, Tripura, 799046, India. deeplina.bio@nita.ac.in.ORCID http://orcid.org/0000-0002-7889-3780
National Institute of Technology Agartala · INIndian Institute of Chemical Biology · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Ferroptosis is a unique form of cell death reliant on iron and lipid peroxidation. It disrupts redox balance, causing cell death by damaging the plasma membrane, with inducers acting through enzymatic pathways or transport systems. In cancer treatment, suppressing ferroptosis or circumventing it holds significant promise. Beyond cancer, ferroptosis affects aging, organs, metabolism, and nervous system. Understanding ferroptosis mechanisms holds promise for uncovering novel therapeutic strategies across a spectrum of diseases. However, detection and regulation of this regulated cell death are still mired with challenges. The dearth of cell, tissue, or organ-specific biomarkers muted the pharmacological use of ferroptosis. This review covers recent studies on ferroptosis, detailing its properties, key genes, metabolic pathways, and regulatory networks, emphasizing the interaction between cellular signaling and ferroptotic cell death. It also summarizes recent findings on ferroptosis inducers, inhibitors, and regulators, highlighting their potential therapeutic applications across diseases. The review addresses challenges in utilizing ferroptosis therapeutically and explores the use of machine learning to uncover complex patterns in ferroptosis-related data, aiding in the discovery of biomarkers, predictive models, and therapeutic targets. Finally, it discusses emerging research areas and the importance of continued investigation to harness the full therapeutic potential of targeting ferroptosis.

Indexed as

FerroptosisMachine LearningAnimalsBiomarkersHumansIronLipid PeroxidationMetabolic Networks and PathwaysNeoplasmsSignal TransductionBiomarkersIronChronic diseasesFerroptosisMachine learningSmall moleculesTherapeutics

Identifiers

PMID38613722
OpenAlexW4394783099

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.