<scp>l</scp> -Arginine Transport in the Human Coronary and Peripheral Circulation
Bibliographic record
Abstract
BACKGROUND: Nitric oxide synthase (NOS) uses arginine for the production of nitric oxide (NO). High intracellular concentrations of arginine suggest that NOS activity should be independent of plasma arginine supply. However, under certain conditions, increased plasma arginine concentrations appear to be associated with increased NOS activity. The purpose of this study was to explore arginine transport within the human coronary and peripheral circulation METHODS AND RESULTS: Mass-labeled 15N2-arginine was infused to steady state before cardiac catheterization in 31 patients. After diagnostic angiography, a catheter was placed in the coronary sinus. The transcardiac concentration gradient (aorta-coronary sinus) of 15N2-arginine was used as a measure of arginine transport at baseline and during infusions of acetylcholine and N(G)-monomethyl-L-arginine (L-NMMA). No gradient was detected at rest. During the infusion of acetylcholine, a significant gradient was detected (2.5+/-1.2 micromol/L, P=0.01) corresponding to a fractional extraction of 11.7+/-7.5%. This is consistent with in vitro studies that suggest that stimulation of NOS induces arginine transport. During the infusion of L-NMMA, the concentration of 15N2-arginine increased in the coronary sinus, producing a gradient of -3.9+/-1.3 micromol/L (P=0.0002), corresponding to a fractional production of 20.5+/-5.0%. This is consistent with in vitro studies that suggest that L-NMMA induces the efflux of arginine from the cell to the extracellular space via transporter-mediated transstimulation. CONCLUSIONS: The use of steady-state 15N2-arginine to examine transorgan L-arginine gradients represents a novel tool for the study of L-arginine transport and the mechanisms of endothelial and NOS dysfunction.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".