Bibliographic record
Abstract
Epidemiological, clinical and experimental evidence indicates an inverse association between Mg(2+) levels (serum and tissue) and blood pressure. Magnesium may influence blood pressure by modulating vascular tone and structure through its effects on numerous biochemical reactions that control vascular contraction/dilation, growth/apoptosis, differentiation and inflammation. Magnesium acts as a calcium channel antagonist, it stimulates production of vasodilator prostacyclins and nitric oxide and it alters vascular responses to vasoactive agonists. Mammalian cells regulate Mg(2+) concentration through specialized influx and efflux transport systems that have only recently been characterized. Magnesium efflux occurs via Na(2+)-dependent and Na(2+)-independent pathways. Mg(2+) influx is controlled by recently cloned transporters including Mrs2p, SLC41A1, SLC41A1, ACDP2, MagT1, TRPM6 and TRPM7. Alterations in some of these systems may contribute to hypomagnesemia and intracellular Mg(2+) deficiency in hypertension. In particular increased Mg(2+) efflux through altered regulation of the vascular Na(+)/Mg(2+) exchanger and decreased Mg(2+) influx due to defective vascular and renal TRPM6/7 expression/activity may be important. This review discusses the role of Mg(2+) in vascular biology and implications in hypertension and focuses on the putative transport systems that control vascular magnesium homeostasis. Much research is still needed to clarify the exact mechanisms of Mg(2+) regulation in the cardiovascular system and the implications of aberrant transcellular Mg(2+) transport in the pathogenesis of cardiovascular disease.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".