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Record W2917984400 · doi:10.2298/abs190208006v

Effects of ibogaine per os treatment on redox homeostasis in rat kidney

2019· article· en· W2917984400 on OpenAlexaff
Teodora Vidonja Uzelac, Nikola Tatalović, Milica Mijović, Aleksandra Nikolić‐Kokić, Zorana Oreščanin-Dušić, Mara Bresjanac, Duško Blagojević

Bibliographic record

VenueArchives of Biological Sciences · 2019
Typearticle
Languageen
FieldNeuroscience
TopicNeurotransmitter Receptor Influence on Behavior
Canadian institutionsInstitute for Biological Sciences
FundersMinistarstvo Prosvete, Nauke i Tehnološkog RazvojaJavna Agencija za Raziskovalno Dejavnost RS
KeywordsOxidative stressGlutathione reductaseSuperoxide dismutasePharmacologyGlutathione peroxidaseGlutathioneChemistryTBARSCatalaseAntioxidantKidneyBiochemistryEndocrinologyEnzymeLipid peroxidationBiology

Abstract

fetched live from OpenAlex

Our previous results showed that a single oral dose (1 or 20 mg/kg body weight) of the anti-addiction agent ibogaine induced in rats 6 and 24 h after administration glycogenolytic activity in hepatocytes, followed by a mild oxidative stress. In this work, we examined the in vivo effect of the same doses of ibogaine on rat kidney morphology, antioxidant enzyme (superoxide dismutases (SOD1 and 2), catalase, glutathione peroxidase, glutathione reductase (GR) and glutathione- S-transferase) activities, and oxidative stress (TBARS) and redox (-SH groups) parameters. The dose of 1 mg/kg ibogaine induced an elevation in SOD1 activity and decreased GR activity after 6 and 24 h. GR activity was decreased at 6 and 24 h after 20 mg/kg ibogaine administration, suggesting changed redox homeostasis. After 24 h, we observed an increase in moderate morphological changes, without changes in urinalyses, indicating that kidney function was not measurably affected. Nevertheless, kidney-function monitoring during and following ibogaine use in human subjects is advisable.

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 score0.024
Threshold uncertainty score0.455

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.037
GPT teacher head0.297
Teacher spread0.260 · 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

Citations4
Published2019
Admission routes1
Has abstractyes

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