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Record W2181454853 · doi:10.25011/cim.v38i6.26202

Mechanisms of remodelling of small arteries, antihypertensive therapy and the immune system in hypertension

2015· review· en· W2181454853 on OpenAlexafffundvenueabout
Ernesto L. Schiffrin

Bibliographic record

VenueClinical and investigative medicine · 2015
Typereview
Languageen
FieldNursing
TopicSodium Intake and Health
Canadian institutionsJewish General Hospital
FundersCanadian Institutes of Health Research
KeywordsMedicineVasoconstrictionInflammationBlood pressureImmune systemVascular remodelling in the embryoPerfusionEssential hypertensionInternal medicineCardiologyImmunology

Abstract

fetched live from OpenAlex

This review summarizes my lecture for the 2015 Distinguished Scientist Award from the Canadian Society of Clinical Investigation, and is based mainly on studies in my laboratory on the mechanisms of remodelling of small arteries in experimental animal and human hypertension and on treatments that lower blood pressure and improve structure and function of resistance vessels. Small resistance arteries undergo either inward eutrophic or hypertrophic remodelling, which raises blood pressure and impairs tissue perfusion. These vascular changes are corrected by some antihypertensive drugs, which may lead to improved outcomes. Vasoconstriction, growth, oxidative stress and inflammation are some of the mechanisms, within the vascular wall, that can be beneficially affected by antihypertensive agents. These antihypertensive-sensitive mechanisms are reviewed in this review, together with the inflammatory and immune mechanisms that may participate in hypertension and associated cardiovascular injury. Molecular studies, based on this research, will hopefully identify novel diagnostic and therapeutic targets, which will improve our ability to prevent and treat hypertension and cardiovascular disease.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.447
GPT teacher head0.390
Teacher spread0.057 · 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 designNot applicable
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

Citations34
Published2015
Admission routes4
Has abstractyes

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