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Record W2116061385 · doi:10.2298/abs1403271m

Copper-induced changes of lipid peroxidation and hemato-biochemical parameters in rat blood: Protective role of flavonoids

2014· article· en· W2116061385 on OpenAlexaff
Jelena Mladenović, Milica G. Paunović, Miloš M. Matić, Veroljub Knežević, Branka I. Ognjanović, Andraš Š. Štajn, Zorica S. Saičić

Bibliographic record

VenueArchives of Biological Sciences · 2014
Typearticle
Languageen
FieldMedicine
TopicNatural Antidiabetic Agents Studies
Canadian institutionsInstitute for Biological Sciences
FundersMinistarstvo Prosvete, Nauke i Tehnološkog Razvoja
KeywordsLipid peroxidationQuercetinLactate dehydrogenaseChemistryHemoglobinHematocritPharmacologyBiochemistryAntioxidantEndocrinologyEnzymeBiology

Abstract

fetched live from OpenAlex

The effects of subchronic exposure to copper (Cu) on lipid peroxidation, hemato-biochemical parameters, and the possible protective role of flavonoids Quercetin and (-)-Epicatechin were studied. Male Wistar albino rats were treated with Cu (560 mg/L, p.o. as CuCl2?2H2O for 5 weeks) and Quercetin and (-)-Epicatechin (40 mg/kg BW each, i.p., every third day during the last 3 weeks) alone or in combination. Cu increased the concentration of lipid peroxides, decreased the number of erythrocytes, hemoglobin and hematocrit values and increased the activities of aspartate aminotransferase, alanine aminotransferase and lactate dehydrogenase. Coadministration of Quercetin and (-)-Epicatechin with Cu lowered the process of lipid peroxidation and restored examined hemato-biochemical parameters to control values. Our results indicate that Cu induced oxidative damage in erythrocytes, which led to anemia, while Quercetin and (-)-Epicatechin showed a protective effect on the hemato-biochemical processes in the blood of rats.

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 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 score0.065
Threshold uncertainty score0.487

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.028
GPT teacher head0.267
Teacher spread0.239 · 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

Citations17
Published2014
Admission routes1
Has abstractyes

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