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Record W2052974815 · doi:10.1139/y00-080

A comparison of the kinetics of low-density lipoprotein oxidation induced by copper or by γ-rays: Influence of radiation dose-rate and copper concentration

2001· article· en· W2052974815 on OpenAlexaffvenue
Abdelouahed Khalil, Tamàs Fülöp

Bibliographic record

VenueCanadian Journal of Physiology and Pharmacology · 2001
Typearticle
Languageen
FieldChemistry
TopicFree Radicals and Antioxidants
Canadian institutionsHealth and Social Services Centre University Institute of Geriatrics of SherbrookeCentre Hospitalier Universitaire de SherbrookeUniversité de Sherbrooke
Fundersnot available
KeywordsCopperKineticsChemistryRadiochemistryLow-density lipoproteinRadiationRadiation doseChemical kineticsInorganic chemistryCholesterolBiochemistryNuclear medicineMedicine

Abstract

fetched live from OpenAlex

The oxidation of low-density lipoproteins is the first step in the complex process leading to atherosclerosis. The aim of our study was to compare the kinetics of low density lipoprotein oxidation induced by copper ions or by oxygen free radicals generated by 60Co gamma-rays. The effects of copper concentration and irradiation dose-rate on LDL peroxidation kinetics were also studied. The oxidation of LDL was followed by the measurement of conjugated diene, hydroperoxides, and thiobarbituric acid reactive substance formation as well as alpha-tocopherol disappearance. In the case of gamma irradiation, the lag-phase before the onset of lipid peroxidation was inversely correlated to the radiation dose-rate. The radiation chemical rates (nu) increased with increasing dose-rate. Copper-induced LDL peroxidation followed two kinetic patterns: a slow kinetic for copper concentrations between 5-20 microM, and a fast kinetic for a copper concentration of 40 microM. The concentration-dependent oxidation kinetics suggest the existence of a saturable copper binding site on apo-B. When compared with gamma-rays, copper ions act as drastic and powerful oxidants only at higher concentrations (> or = 40 microM).

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)0.000
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 score0.030
Threshold uncertainty score0.286

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.016
GPT teacher head0.279
Teacher spread0.263 · 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

Citations6
Published2001
Admission routes2
Has abstractyes

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