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Record W1558191835

Abstract 17600: Carvedilol Improves Left and Right Ventricular Fibrosis and Function Through Abrogation of Tgfβ1-ctgf Signaling in a Rat Persistent Right Ventricular Afterload Model

2014· article· en· W1558191835 on OpenAlexaff
Kenichi Okumura, Hideyuki Kato, Osami Honjo, Mei Sun, Mark K. Friedberg

Bibliographic record

VenueCirculation · 2014
Typearticle
Languageen
FieldMedicine
TopicPulmonary Hypertension Research and Treatments
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsCarvedilolMedicineCardiologyInternal medicineAfterloadVentricular remodelingHeart failureCTGFFibrosisPulmonary hypertensionRight ventricular hypertrophyCardiac function curveHemodynamicsReceptor
DOInot available

Abstract

fetched live from OpenAlex

Background: Left ventricular (LV) function influences outcomes in right ventricular (RV) failure. Carvedilol reduces mortality in LV failure and improves right ventricular (RV) function in experimental pulmonary arterial hypertension (PAH). However, its impact on ventricular-ventricular interactions and LV function in RV afterload is unknown. We investigated effects of carvedilol on biventricular fibrosis and function in a rat model of persistent PAH. Methods and Results: Rats were randomized into 3 groups: Sham controls, PAH and PAH+carvedilol. Severe PAH was induced by 60mg/kg subcutaneous monocrotaline. In the treatment group, oral carvedilol (15mg/kg/d) was started 2-weeks after monocrotaline injection and continued for 3-weeks until the terminal experiment. Echocardiography and exercise performance were performed at baseline and repeated at the terminal experiment with hemodynamic measurements. LV and RV myocardium were analyzed for hypertrophy, fibrosis and molecular signaling by protein and mRNA an...

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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.016
GPT teacher head0.244
Teacher spread0.228 · 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
Published2014
Admission routes1
Has abstractyes

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