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Nicotine Effect on Cardiovascular System and Ion Channels

2006· review· en· W80618799 on OpenAlexaff
Salma Toma Hanna

Bibliographic record

VenueJournal of Cardiovascular Pharmacology · 2006
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNicotinic Acetylcholine Receptors Study
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsNicotineNitric oxideSuperoxideVasodilationMedicineFree radical scavengerAntioxidantInternal medicinePharmacologyCardiologyEndotheliumEndocrinologyChemistryOxidative stressBiochemistry

Abstract

fetched live from OpenAlex

Smoking is a leading cause of cardiovascular disease, hypertension, myocardial infarction, and stroke. Nicotine is one of the components of cigarette smoke. Nicotine effects on the cardiovascular system reflect the activity of the nicotine receptors centrally and on peripheral autonomic ganglia. It has been found that cigarette smoke extract-induced contraction of porcine coronary arteries is related to superoxide anion-mediated degradation of nitric oxide. Treatment of rabbit aortas with an oxygen free radicals scavenger attenuated cigarette smoke impairment of arterial relaxation. Treatment of smokers with vitamin C, an antioxidant, improved impaired endothelium-dependent reactivity of large peripheral arteries. Thus it appears that chronic smoking and acute exposure to cigarette smoke extract may alter endothelium-dependent reactivity via the production of oxygen derived free radicals. This review discusses the effects of nicotine on resistance arterioles, compliance arteries, smooth muscle cells, and ion channels in the cardiovascular system. We discuss studies performed on humans, nicotine-exposed animals, and cell cultures yielding varying and inconsistent results that may be due to differences in experimental design, species, and the dose of exposure. Nicotine exposure appears to induce a combination of free radical production, vascular wall adhesion, and a reduction of fibrinolytic activity in the plasma.

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)0.004
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.944
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.009
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.015
GPT teacher head0.302
Teacher spread0.287 · 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 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

Citations103
Published2006
Admission routes1
Has abstractyes

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