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Record W2163771711 · doi:10.1017/s1462399411001931

Statins and myocardial remodelling: cell and molecular pathways

2011· review· en· W2163771711 on OpenAlexfundno aff
Karen E. Porter, Neil A. Turner

Bibliographic record

VenueExpert Reviews in Molecular Medicine · 2011
Typereview
Languageen
FieldMedicine
TopicLipoproteins and Cardiovascular Health
Canadian institutionsnot available
FundersUniversity of LeedsInstitute of GeneticsBritish Heart Foundation
KeywordsHeart failureMedicineExtracellular matrixPrenylationStatinFibroblastInternal medicineCardiologyPharmacologyBioinformaticsBiologyCell biologyCell culture

Abstract

fetched live from OpenAlex

The advent of statins has revolutionised the treatment of patients with raised plasma cholesterol and increased cardiovascular risk. However, the beneficial effects of this class of drugs are far greater than would be expected from lowering of cholesterol alone, and they appear to offer cardiovascular protection at multiple levels, primarily as a result of their pleiotropic activity. Indeed, their favourable effects on the heart seem to be mediated in part through reduced prenylation and subsequent inhibition of small GTPases, particularly those of the Rho family. Such statin-mediated effects are manifested by reduced onset of heart failure and improvements in cardiac dysfunction and remodelling in heart failure patients. Experimental studies have shown that statins mediate their effects on the two major resident cell types of the heart--cardiomyocytes and cardiac fibroblasts--and thus facilitate improvement of adverse remodelling of ischaemic or non-ischaemic aetiology. This review examines evidence for the cellular effects of statins in the heart, and discusses the underlying molecular mechanisms at the level of the cardiomyocyte (hypertrophy, cell death and contractile function) and the cardiac fibroblast (differentiation, proliferation, migration and extracellular matrix synthesis). The prospects for future therapies and ongoing clinical trials are also summarised.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.966
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0070.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.064
GPT teacher head0.335
Teacher spread0.271 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations46
Published2011
Admission routes1
Has abstractyes

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