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Record W2560226641 · doi:10.25011/cim.v39i6.27511

Effects of Ginkgo biloba extract on brain oxidative condition after cisplatin exposure

2016· article· en· W2560226641 on OpenAlexvenueno aff
Duygu Aydın, Emine Gülçeri Güleç Peker, Meryem Karakurt, Ayşe Gurel, Mustafa Ayyıldız, Şule Çoşkun Cevher, Erdal Ağar, Şenol Dane

Bibliographic record

VenueClinical and investigative medicine · 2016
Typearticle
Languageen
FieldMedicine
TopicGinkgo biloba and Cashew Applications
Canadian institutionsnot available
Fundersnot available
KeywordsGinkgo bilobaCisplatinOxidative stressMalondialdehydePharmacologyNeurotoxicityGlutathioneAntioxidantNitric oxideChemistryToxicityMedicineBiochemistryInternal medicineChemotherapyEnzyme

Abstract

fetched live from OpenAlex

PURPOSE: The purpose of this study was to evaluate the efficacy of Ginkgo biloba extract (EGb 761) on oxidative events of brain in cisplatin-administrated rats. METHODS: Rats were divided into four experimental groups: 1) control (n=6); 2) cisplatin (8 mg/kg, intraperitoneally one dose, n=6); 3) EGb 761 (100 mg/kg intraperitoneally for 15 days, n=6); and 4) cisplatin + EGb 761 (n=6). After drug administration, rats were sacrificed and brain tissues were removed. Nitric oxide (NO), malondialdehyde (MDA) and glutathione (GSH) levels were evaluated in brain tissues. RESULTS: Single dose cisplatin administration significantly increased NO and GSH levels, but decreased MDA levels in brain tissue samples. EGb 761 treatment reversed the effects of cisplatin on NO and GSH levels, but did not affect the decreased MDA levels. CONCLUSION: Results of the study indicate that oxidative stress can be an important pathogenetic mechanism of cisplatin-induced neurotoxicity. EGb 761, an standardized extract of G. biloba leaves that has antioxidant properties, may improve the oxidative stress-related neurological side effects of cisplatin.

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)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.592
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.007
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 head0.071
GPT teacher head0.356
Teacher spread0.285 · 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 designObservational
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

Citations23
Published2016
Admission routes1
Has abstractyes

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