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Record W1978569502 · doi:10.3164/jcbn.12-75

Method for assessing X-ray-induced hydroxyl radical-scavenging activity of biological compounds/materials

2013· article· en· W1978569502 on OpenAlexaff
Megumi Ueno, Ikuo Nakanishi, Ken‐ichiro Matsumoto

Bibliographic record

VenueJournal of Clinical Biochemistry and Nutrition · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicElectron Spin Resonance Studies
Canadian institutionsInstitute of Particle Physics
Fundersnot available
KeywordsHydroxyl radicalChemistryAntioxidantScavengingAscorbic acidBiological activityScavengerSpin trappingRadicalHydroxyl valueElectron paramagnetic resonanceOrganic chemistryBiochemistryFood scienceIn vitro

Abstract

fetched live from OpenAlex

A method for correctly assessing hydroxyl radical scavenging activity of antioxidative chemicals and/or biological compounds/materials was proposed. This method can simultaneously assess two factors, i.e. hydroxyl radical-scavenging and 5,5-dimethyl-2-hydroxy-1-pyrrolidine-N-oxide (hydroxyl radical adduct of 5,5-dimethyl-1-pyrroline-N-oxide)-reducing ability, as antioxidative properties. In this paper, some biologically common hydrophilic molecules, cell culture media, and rat plasma were tested. X-ray-induced hydroxyl radical can be detected using the electron paramagnetic resonance spin trapping technique. Using X-ray irradiation of the reaction mixture as the hydroxyl radical source, the true hydroxyl radical-scavenging ability of the subjected antioxidant can be assessed. In addition, the method simultaneously measures the reduction of 5,5-dimethyl-2-hydroxy-1-pyrrolidine-N-oxide, to estimate the reducing ability of the antioxidant. Biological materials, such as sugars and proteins, could abolish hydroxyl radical at the biological concentration. Ascorbic acid showed reducing ability at the biological concentration. The simultaneous assessment of hydroxyl radical-scavenging and reducing ability of antioxidants can be an informative index for antioxidants.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.443

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.055
GPT teacher head0.403
Teacher spread0.348 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations20
Published2013
Admission routes1
Has abstractyes

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