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Record W2805263795 · doi:10.2298/abs180503023u

Beneficial effects of α-lipoic acid in diabetes- and drug- induced liver injury

2018· article· en· W2805263795 on OpenAlexaff
Aleksandra Uskoković, Svetlana Dinić, Nevena Grdović, Jelena Arambašić Јovanović, Melita Vidaković, Goran Poznanović, Mirjana Mihailović

Bibliographic record

VenueArchives of Biological Sciences · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBiochemical Acid Research Studies
Canadian institutionsInstitute for Biological Sciences
FundersMinistarstvo Prosvete, Nauke i Tehnološkog Razvoja
KeywordsOxidative stressDiabetes mellitusAntioxidantDrugPharmacologyLipoic acidMedicineToxicityLiver injuryBioinformaticsChemistryBiologyBiochemistryInternal medicineEndocrinology

Abstract

fetched live from OpenAlex

This review summarizes the effects of ?-lipoic acid (LA) on liver damage and complications in diabetes and drug toxicity. LA is a naturally occurring dithiol compound that plays an essential role in mitochondrial metabolism in its protein-bound form. In contrast, free LA in supplements has diverse biological actions, and its antioxidant effect is its most studied and important activity. Due to its strong antioxidant potential, LA could have a promising role in the treatment of pathologies resulting from an imbalance in redox homeostasis. This includes diabetes, which produces deleterious effects on many organs, including the liver. In diabetes specifically, LA prevents ?-cell destruction, enhances glucose uptake, and its antioxidant effects may be particularly useful in slowing down the development of diabetic complications. Diabetesrelated liver damage is a serious complication in which oxidative stress is the main contributor to tissue injury. Oxidative stress is regarded as one of the main pathological mechanisms underlying liver pathologies provoked by other insults, such as drug toxicity, where LA could also be a useful agent in therapeutic intervention. However, before wider application of LA in a clinical setting, experimental and clinical research needs to be extended.

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 score0.098
Threshold uncertainty score0.984

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 studies0.0000.003
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 head0.277
Teacher spread0.262 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

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