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Pioglitazone Ameliorates Lipid Metabolic Disorder in KKAy Mice

2011· article· en· W2151795909 on OpenAlexvenueno aff
Sifan Chen, Qing Wei, Yanshuang Sun, Xincai Xiao, Zili Zhang, Liang‐Ru Ke, Niman Zhou, Zhiming Shan, Xiang Feng, Wei Zhu

Bibliographic record

VenueJournal of Pharmacy and Nutrition Sciences · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPeroxisome Proliferator-Activated Receptors
Canadian institutionsnot available
FundersSun Yat-sen UniversitySt. John's University
KeywordsPioglitazoneAMPKSteatosisInternal medicineEndocrinologyAntioxidantLipid profileLipid metabolismFatty liverMetabolic disorderBlood lipidsChemistryMedicinePhosphorylationDiabetes mellitusCholesterolBiochemistryProtein kinase AType 2 diabetes

Abstract

fetched live from OpenAlex

Pioglitazone (pio) has been used as an effective hypoglycemic drug in medicine, however, the effects and mechanisms of pio on lipid metabolic disorder are still largely unknown. To explore the effects of pio on serum and liver lipid level and antioxidant ability of mice with lipid metabolic disorder, KKAy mice were treated with piofor 12 weeks and their lipid and antioxidant indices were compared to those of KKAy mice without pio treatment. C57BL/6J mice were used as control animals. The results show that pio treatment reduces serum and liver TG, elevates serum HDL-C level, increases serum and liver SOD activity, attenuates serum MDA content, ameliorates liver steatosis, induces liver PPARγexpression and enhances AMPKα phosphorylation level. In conclusion, the results indicate that pio could regulate blood lipid level, reduce liver lipid deposition and enhance antioxidant capacity of mice with lipid metabolic disorder, which is possibly through increasing AMPKα phosphorylation.

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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.034
GPT teacher head0.310
Teacher spread0.277 · 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

Citations0
Published2011
Admission routes1
Has abstractyes

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