MétaCan
Menu
Back to cohort
Record W2914243390 · doi:10.1016/j.lfs.2019.01.043

Atorvastatin inhibits pro-inflammatory actions of aldosterone in vascular smooth muscle cells by reducing oxidative stress

2019· article· en· W2914243390 on OpenAlexafffund
Thiago Bruder‐Nascimento, Gláucia E. Callera, Augusto C. Montezano, Eric J. Belin de Chantemèle, Rita C. Tostes, Rhian M. Touyz

Bibliographic record

VenueLife Sciences · 2019
Typearticle
Languageen
FieldMedicine
TopicHormonal Regulation and Hypertension
Canadian institutionsUniversity of Ottawa
FundersNational Institutes of HealthBritish Heart FoundationAgence Universitaire de la FrancophonieNational Heart, Lung, and Blood InstituteJuvenile Diabetes Research Foundation United KingdomFundação de Amparo à Pesquisa do Estado de São PauloCanadian Institutes of Health ResearchCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsAtorvastatinVascular smooth muscleAldosteroneInflammationOxidative stressReactive oxygen speciesChemistryPharmacologyInternal medicineEndocrinologyMedicineBiochemistry

Abstract

fetched live from OpenAlex

mol/L) stimulation. Atorvastatin inhibited Rac1/2 and p47phox translocation from the cytosol to the membrane, as well as reduced aldosterone-induced ROS production. Atorvastatin also attenuated aldosterone-induced vascular inflammation and macrophage adhesion to VSMC. Similarly EHT1864, a Rac1/2 inhibitor, and tiron, ROS scavenger, reduced macrophage adhesion. Through its inhibitory effects on Rac1/2 activation and ROS production, atorvastatin reduces vascular ROS generation and inhibits VSMC inflammation. Our data suggest that in conditions associated with aldosterone-induced vascular damage, statins may have vasoprotective effects by inhibiting oxidative stress and inflammation.

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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.028
GPT teacher head0.278
Teacher spread0.250 · 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

Citations38
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueLife SciencesSame topicHormonal Regulation and HypertensionFrench-language works237,207