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P1485Changes of regional myocardial deformation induced by serelaxin reflect gene regulation in experimental heart failure model

2017· article· en· W2761547295 on OpenAlexaboutno aff
Tomas Lapinskas, Jana Grune, Heike Meýborg, Ulrich Kintscher, Rolf Gebker, Burkert Pieske, Sebastian Kelle, Philipp Stawowy

Bibliographic record

VenueEuropean Heart Journal · 2017
Typearticle
Languageen
FieldMedicine
TopicCardiac Fibrosis and Remodeling
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHeart failureCardiologyDeformation (meteorology)Internal medicine

Abstract

fetched live from OpenAlex

Background: First animal studies have shown that serelaxin (recombinant human relaxin) has positive effects on cardiovascular remodeling in heart failure. This study aimed to assess whether cardiac magnetic resonance feature tracking (CMR-FT) can detect differences of the effect of serelaxin on myocardial deformation in pressure-overload induced heart failure mice. Also, to assess the relationship between myocardial deformation parameters and histology findings. Methods: Twenty-eight C57BL/6J male, 8–9 week old mice were subjected to SHAM or transverse aortic constriction (TAC) surgery. After 10 weeks TAC-operated mice were randomized into two groups which received either serelaxin 0.5 mg/kg per day (TAC_Srlxn) or sodium acetate (TAC_Veh) administered as a continuous intravenous infusion using Alzet mini pumps for up to 4 weeks. CMR was performed on a 3 T small-animal MRI system (MRS 3017, MR Solutions, Guildford, UK) at week 10 (before start of study treatment) and 14 (end of study treatment) after surgery. The cine images were used to calculate left ventricular (LV) longitudinal (EllLV), circumferential (EccLV) and radial (ErrLV) strain using dedicated software (CMR42, Circle, Calgary, Canada). After the 14th week mice were sacrificed and hearts were harvested for histological analysis. Cross-sections of hearts at the mid-ventricular level were fixed in formalin, embedded in paraffin and stained with Picrosirius red for detection of collagen content.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.695
Threshold uncertainty score0.492

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.079
GPT teacher head0.333
Teacher spread0.254 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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