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Record W2152325979 · doi:10.1186/cc3992

Reactive oxygen species: toxic molecules or spark of life?

2006· review· en· W2152325979 on OpenAlexaff
Sheldon Magder

Bibliographic record

VenueCritical Care · 2006
Typereview
Languageen
FieldImmunology and Microbiology
TopicNeutrophil, Myeloperoxidase and Oxidative Mechanisms
Canadian institutionsRoyal Victoria Regional Health CentreMcGill University Health CentreRoyal Victoria Hospital
Fundersnot available
KeywordsReactive oxygen speciesCell biologyOxidative stressCell signalingIntracellularMedicineSignal transductionOxidative damageBiologyInternal medicine

Abstract

fetched live from OpenAlex

Increases in reactive oxygen species (ROS) and tissue evidence of oxidative injury are common in patients with inflammatory processes or tissue injury. This has led to many clinical attempts to scavenge ROS and reduce oxidative injury. However, we live in an oxygen rich environment and ROS and their chemical reactions are part of the basic chemical processes of normal metabolism. Accordingly, organisms have evolved sophisticated mechanisms to control these reactive molecules. Recently, it has become increasingly evident that ROS also play a role in the regulation of many intracellular signaling pathways that are important for normal cell growth and inflammatory responses that are essential for host defense. Thus, simply trying to scavenge ROS is likely not possible and potentially harmful. The 'normal' level of ROS will also likely vary in different tissues and even in different parts of cells. In this paper, the terminology and basic chemistry of reactive species are reviewed. Examples and mechanisms of tissue injury by ROS as well as their positive role as signaling molecules are discussed. Hopefully, a better understanding of the nature of ROS will lead to better planned therapeutic attempts to manipulate the concentrations of these important molecules. We need to regulate ROS, not eradicate them.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.957
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.070
GPT teacher head0.334
Teacher spread0.265 · 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.

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

Citations205
Published2006
Admission routes1
Has abstractyes

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