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Record W2207611361 · doi:10.1139/cjpp-2015-0454

Resveratrol reduces matrix metalloproteinases and alleviates intrahepatic cholestasis of pregnancy in rats

2015· article· en· W2207611361 on OpenAlexvenueno aff
Zhong Chen, Lingqing Hu, Mudan Lu, Zongji Shen

Bibliographic record

VenueCanadian Journal of Physiology and Pharmacology · 2015
Typearticle
Languageen
FieldMedicine
TopicDrug Transport and Resistance Mechanisms
Canadian institutionsnot available
FundersNational Institutes of HealthNanjing UniversityNanjing Medical University
KeywordsUrsodeoxycholic acidCholestasis of pregnancyMatrix metalloproteinaseResveratrolCholestasisInternal medicineEndocrinologyBile acidMatrix metalloproteinase inhibitorEthinylestradiolChemistryMedicinePregnancyPharmacologyFetusBiology

Abstract

fetched live from OpenAlex

Intrahepatic cholestasis of pregnancy (ICP) is a severe liver disorder occurring specifically in pregnancy, and matrix metalloproteinase (MMP)-2 and MMP-9 were found to be elevated in ICP patients. Using ethinylestradiol-induced ICP rats as the model, we examined the effect of resveratrol on ICP symptoms such as bile flow rate, serum enzymatic activities, and TBA concentration, as well as MMP levels, and compared with the known ICP drug ursodeoxycholic acid. Both MMP-2 and MMP-9 were upregulated in ICP rats, and resveratrol treatment could inhibit the elevation of both MMPs, whereas ursodeoxycholic acid did not exhibit any effect. Although ursodeoxycholic acid alleviated ICP symptoms, resveratrol treatment in general exhibited better outcome in restoring bile flow rate, serum enzymatic activities, and TBA concentration. Our results for the first instance strongly supported the potential of RE as a new therapeutic agent in treating ICP, possibly through inhibiting MMP-2 and MMP-9.

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)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.023
GPT teacher head0.291
Teacher spread0.268 · 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
Published2015
Admission routes1
Has abstractyes

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Same venueCanadian Journal of Physiology and PharmacologySame topicDrug Transport and Resistance MechanismsFrench-language works237,207