MétaCan
Menu
Back to cohort
Record W2020970193 · doi:10.1139/y04-094

Long-term effects of the PPARγ activator pioglitazone on cardiac inflammation in stroke-prone spontaneously hypertensive rats

2004· article· en· W2020970193 on OpenAlexafffundvenue
Quy N. Diep, Farhad Amiri, Karim Benkirane, Pierre Paradis, Ernesto L. Schiffrin

Bibliographic record

VenueCanadian Journal of Physiology and Pharmacology · 2004
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPeroxisome Proliferator-Activated Receptors
Canadian institutionsMontreal Clinical Research Institute
FundersCanadian Institutes of Health Research
KeywordsPioglitazoneMedicineInternal medicineInflammationEndocrinologyFibrosisCardiac fibrosisBlood pressureLeft ventricular hypertrophyDiabetes mellitusType 2 diabetes

Abstract

fetched live from OpenAlex

We investigated the long-term effects of the thiazolidinedione PPARgamma activator pioglitazone on cardiac inflammation in stroke-prone spontaneously hypertensive rats (SHRSP), a model of malignant of hypertension. Six-week-old SHRSP were treated with pioglitazone (10 mg/kg per day p.o.) for 20 weeks. The rise in systolic blood pressure (SBP) in SHRSP was only transiently and slightly attenuated by pioglitazone (P < 0.05). On one hand, cardiac hypertrophy was little affected by the pioglitazone treatment, and there was only a reduction of subepicardial interstitial fibrosis. On the other hand, left ventricular NFkappaB and AP-1 binding activities, the expression of TNFalpha, and the adhesion of molecule PECAM were significantly decreased by pioglitazone treatment. Expression of the pro-apoptotic proteins p53 and bax was significantly increased by pioglitazone. Thus, pioglitazone-attenuated cardiac inflammation in SHRSP had little effect on BP or cardiac hypertrophy. PPARgamma activation may play a preventive cardiovascular role by offsetting the cardiac inflammatory response as demonstrated in this genetic model of malignant hypertension.

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 score0.028
Threshold uncertainty score0.480

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 studies0.0000.000
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 head0.005
GPT teacher head0.226
Teacher spread0.221 · 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

Citations43
Published2004
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal of Physiology and PharmacologySame topicPeroxisome Proliferator-Activated ReceptorsFrench-language works237,207