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Record W3195080457 · doi:10.1155/2021/9912436

Reactive Oxygen Species (ROS) Regulates Different Types of Cell Death by Acting as a Rheostat

2021· review· en· W3195080457 on OpenAlexaff
Gloria E. Villalpando-Rodriguez, Spencer B. Gibson

Bibliographic record

VenueOxidative Medicine and Cellular Longevity · 2021
Typereview
Languageen
FieldMedicine
TopicFerroptosis and cancer prognosis
Canadian institutionsUniversity of ManitobaResearch Institute in Oncology and HematologyCancerCare Manitoba
Fundersnot available
KeywordsNecroptosisProgrammed cell deathCell biologyReactive oxygen speciesCrosstalkBiologyCell typeApoptosisCell fate determinationCellSignal transductionCell signalingTranscription factorBiochemistry

Abstract

fetched live from OpenAlex

Reactive oxygen species (ROS) are essential for cellular signaling and response to stress. The level of ROS and the type of ROS determine the ability of cells to undergo cell death. Furthermore, dysregulation of the antioxidant pathways is associated with many diseases. It has become apparent that cell death can occur through different mechanisms leading to the classifications of different types of cell death such as apoptosis, ferroptosis, and necroptosis. ROS play essential roles in all forms of cell death, but it is only now coming into focus that ROS control and determine the type of cell death that occurs in any given cell. Indeed, ROS may act as a rheostat allowing different cell death mechanisms to be engaged and crosstalk with different cell death types. In this review, we will describe the ROS regulatory pathways and how they control different types of cell death under normal and disease states. We will also propose how ROS could provide a mechanism of crosstalk between cell death mechanisms and act as a rheostat determining the type of cell death.

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)0.001
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 score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.060
GPT teacher head0.320
Teacher spread0.260 · 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

Citations473
Published2021
Admission routes1
Has abstractyes

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