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Record W2168826137 · doi:10.25011/cim.v31i4.4778

Aminoguanidine ameliorates radiation-induced oxidative lung damage in rats

2008· article· en· W2168826137 on OpenAlexvenueno aff
Celalettin Eroğlu, Oğuz Galip Yıldız, Recep Saraymen, Serdar Soyuer, Eser Kılıç, Servet Özcan

Bibliographic record

VenueClinical and investigative medicine · 2008
Typearticle
Languageen
FieldMedicine
TopicEffects of Radiation Exposure
Canadian institutionsnot available
Fundersnot available
KeywordsSuperoxide dismutaseCatalaseAntioxidantLipid peroxidationLungGlutathione peroxidaseChemistryPharmacologyNitric oxideOxidative stressInternal medicineBiochemistryMedicine

Abstract

fetched live from OpenAlex

PURPOSE: To investigate the possible protective effects of aminoguanidine (AG ) on lung damage in whole body irradiated rats. METHODS: To evaluate the biological damage of radiation on rat lung tissue, lipid peroxidation products were measured using biochemical parameters. Thirty Wistar albino rats were divided into three subgroups: control (C) , irradiation alone (RT), and RT + AG combined. After sacrificing the rats, antioxidant enzymes catalase (CAT), superoxide dismutase (SOD) and glutathione peroxidase (GSHPx) activities and malondiadehyde (MDA), nitric oxide (NO) levels were evaluated in lung tissue. RESULTS: Administration of AG resulted in an increase in the activities of CAT, SOD and GSHPx in the lungs. All were reduced after radiation. In addition, AG administration resulted in a decrease in both NO and MDA levels in lung compared with the irradiated group. CONCLUSION: Amnoguanidine increased the endogenous antioxidant defence mechanism in rats and protected the animals from radiation-induced lung toxicity. Moreover, AG may protect against ionizing radiation-induced lung damage because of its antioxidant effect.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
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)0.0010.000

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 head0.143
GPT teacher head0.368
Teacher spread0.225 · 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 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

Citations21
Published2008
Admission routes1
Has abstractyes

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